Method performed by first node in wireless communication system and first node

Abstract

The present disclosure relates to wireless communication techniques. Specifically, the embodiment of the invention provides a method executed by a first node in a wireless communication system and the first node, the method proposes a new clutter deletion scheme, and the method comprises the following steps: sending a first signal, receiving an echo signal of the first signal, performing channel estimation based on the echo signal, obtaining at least two groups of first channel estimation results obtained by dividing the channel estimation results or dividing the echo signals; obtaining a first clutter estimation result based on the at least two groups of first channel estimation results; and on the basis of the first clutter estimation result, performing clutter deletion on the at least two groups of first channel estimation results to obtain a second channel estimation result. According to the embodiment of the invention, the clutter can be deleted after the clutter estimation result is obtained based on the grouped channel estimation result, the time delay and operation complexity of clutter estimation can be effectively reduced, and the efficiency of clutter estimation is improved.

Description

Technical Field

[0001] The present disclosure relates to the field of wireless communication technologies, and more particularly, to a method performed by a first node in a communication system and the first node. Background Art

[0002] Considering the development of wireless communication from generation to generation, these technologies have been mainly developed for human-targeted services such as voice calls, multimedia services, and data services. With the commercialization of the fifth-generation (5G) communication system, the number of connected devices is expected to grow exponentially. These will be increasingly connected to the communication network. Examples of the Internet of Things may include vehicles, robots, drones, home appliances, displays, smart sensors connected to various infrastructures, construction machinery, and factory equipment. Mobile devices are expected to evolve in various forms such as augmented reality glasses, virtual reality headsets, and holographic devices. Efforts have been made to develop an improved 6G communication system to provide various services by connecting hundreds of billions of devices and things in the sixth-generation (6G) era. For these reasons, the 6G communication system is called a super 5G system.

[0003] The 6G communication system, which is expected to be commercialized around 2030, will have a peak data rate of tera (1,000 giga) - level bps and a radio latency of less than 100 μsec, and thus will be 50 times the data rate of the 5G communication system and have 1 / 10 of its radio latency.

[0004] To achieve such high data rates and ultra-low latency, implementing 6G communication systems in the terahertz band (e.g., 95 GHz to 3 THz band) has been considered. It is expected that, since path loss and atmospheric absorption in the terahertz band are more severe than those in the millimeterwave (mmWave) band introduced in 5G, technologies capable of ensuring signal transmission distance (i.e., coverage) will become even more critical. As the main technology for ensuring coverage, it is necessary to develop radiofrequency (RF) components, antennas, and new waveforms with better coverage than orthogonal frequency division multiplexing (OFDM), beamforming, and large-scale multiple input multiple output (MIMO), full dimensional multiple input multiple output (FD-MIMO), array antennas, and multi-antenna transmission technologies such as large-scale antennas. In addition, new technologies for improving signal coverage in the terahertz band, such as metasurface-based lenses and antennas, orbital angular momentum (OAM), and reconfigurable intelligent surface (RIS), have been under discussion.

[0005] In addition, in order to improve spectral efficiency and overall network performance, the following technologies have been developed for 6G communication systems: full-duplex technology that enables uplink transmission and downlink transmission to simultaneously use the same frequency resources; network technologies that comprehensively utilize satellites, high-altitude platform stations (HAPS), etc.; improved network architectures that support mobile base stations, etc., and enable network operation optimization and automation, etc.; dynamic spectrum sharing technology via collision avoidance based on spectrum usage prediction; the use of artificial intelligence (AI) in wireless communication to improve overall network operation by leveraging AI from the design phase of developing 6G and internalizing end-to-end AI support functions; and next-generation distributed computing technology that overcomes the computing power limitations of user equipment (UE) through ultra-high-performance communication and computing resources (such as mobile edge computing (MEC), cloud, etc.) that can be realized on the network. In addition, by designing new protocols to be used in 6G communication systems, developing mechanisms for implementing hardware-based secure environments and secure use of data, and developing technologies for maintaining privacy, efforts are continuing to strengthen connectivity between devices, optimize the network, promote the softwareization of network entities, and increase the openness of wireless communication.

[0006] Research and development of 6G communication systems, which are expected to include ultra-connectivity such as person to machine (P2M) and machine to machine (M2M), will bring the next ultra-connectivity experience. In particular, services such as true immersive extended reality (XR), high-fidelity mobile holograms, and digital replicas are expected to be provided through 6G communication systems. In addition, services such as remote surgery, industrial automation, and emergency response for security and reliability enhancement will be provided through 6G communication systems, enabling the technology to be applied to various fields such as industry, healthcare, automotive, and household appliances. Summary of the Invention

[0007] The purpose of the embodiments of the present disclosure is to provide a solution for reducing computational complexity. To achieve this purpose, the embodiments of the present disclosure provide a method, a first node, and a readable storage medium executed by the first node in a communication system, specifically as follows:

[0008] On the one hand, the embodiments of the present disclosure provide a method executed by the first node in a communication system, and the method includes:

[0009] Send a first signal, receive an echo signal of the first signal, perform channel estimation based on the echo signal, and obtain at least two groups of first channel estimation results;

[0010] Based on the at least two groups of first channel estimation results, obtain a first clutter estimation result;

[0011] Based on the first clutter estimation result, perform clutter deletion on the at least two groups of first channel estimation results to obtain a second channel estimation result;

[0012] Wherein, the at least two groups of first channel estimation results are obtained through at least one of the following operations:

[0013] Perform channel estimation on the echo signal to obtain a third channel estimation result, and in the third channel estimation result, obtain at least one third channel estimation result at intervals of a preset interval value and divide them into the same group, so as to obtain at least two groups of first channel estimation results obtained by dividing the channel estimation results;

[0014] Obtain at least one echo signal at intervals of the interval value in the echo signal and divide them into the same group to obtain at least two groups of echo signals, and perform channel estimation on the at least two groups of echo signals to obtain at least two groups of first channel estimation results obtained by dividing the echo signals.

[0015] In a feasible embodiment, the interval value is determined based on the Doppler frequency distribution of non-target objects.

[0016] In a feasible embodiment, the method further includes if it is determined to perform clutter estimation based on the second channel estimation result, then execute at least one of the following:

[0017] Perform clutter estimation based on at least one of the first signal and the third channel estimation result to obtain a second clutter estimation result, and perform clutter deletion on the third channel estimation result based on the second clutter estimation result to obtain a fourth channel estimation result, so as to perform target detection based on at least one of the second channel estimation result and the fourth channel estimation result;

[0018] Perform clutter estimation based on at least one of the first signal and the at least two groups of first channel estimation results obtained by dividing the echo signals to obtain a third clutter estimation result, and perform clutter deletion on the at least two groups of first channel estimation results obtained by dividing the echo signals based on the third clutter estimation result to obtain a fifth channel estimation result, so as to perform target detection based on at least one of the second channel estimation result and the fifth channel estimation result.

[0019] In a feasible embodiment, estimating clutter based on at least one of the first signal and the third channel estimation result to obtain a second clutter estimation result, and performing clutter cancellation on the third channel estimation result based on the second clutter estimation result to obtain a fourth channel estimation result, including:

[0020] Constructing a first matrix of a clutter subspace based on at least one of the frequency-domain signal of the first signal and the third channel estimation result;

[0021] Performing signal reconstruction based on the first matrix and the third channel estimation result to obtain a reconstructed second clutter estimation result;

[0022] Performing clutter cancellation on the third channel estimation result based on the second clutter estimation result to obtain a fourth channel estimation result;

[0023] Estimating clutter based on at least one of the first signal and at least two groups of first channel estimation results obtained by dividing the echo signal to obtain a third clutter estimation result, and performing clutter cancellation on at least two groups of first channel estimation results obtained by dividing the echo signal based on the third clutter estimation result to obtain a fifth channel estimation result, including:

[0024] Constructing a second matrix of a clutter subspace based on at least one of the frequency-domain signal of the first signal and at least two groups of first channel estimation results obtained by dividing the echo signal;

[0025] Performing signal reconstruction based on the second matrix and at least two groups of first channel estimation results obtained by dividing the echo signal to obtain a reconstructed third clutter estimation result;

[0026] Performing clutter cancellation on at least two groups of first channel estimation results obtained by dividing the echo signal based on the third clutter estimation result to obtain a fifth channel estimation result.

[0027] In a feasible embodiment, it further includes:

[0028] Determining whether the target detection based on the fourth channel estimation result or the fifth channel estimation result meets a first condition;

[0029] If the first condition is met, constructing a third matrix of a clutter subspace based on the result of the target detection;

[0030] Performing signal reconstruction based on the third matrix and the third channel estimation result to obtain a reconstructed fourth clutter estimation result; or, performing signal reconstruction based on the third matrix and at least two groups of first channel estimation results obtained by dividing the echo signal to obtain a fifth clutter estimation result;

[0031] Based on the fourth clutter estimation result, clutter removal is performed on the third channel estimation result to obtain a sixth channel estimation result; or, based on the fifth clutter estimation result, clutter removal is performed on at least two groups of first channel estimation results obtained by dividing the echo signal to obtain a seventh channel estimation result;

[0032] Wherein, the first condition includes at least one of the following:

[0033] The power value of the fourth channel estimation result or the fifth channel estimation result of the detected target object is greater than the noise floor power, and the detected Doppler frequency of the target object is not equal to 0;

[0034] The power value of the fourth channel estimation result or the fifth channel estimation result of the detected target object is greater than the noise floor power, and the detected Doppler frequency of the target object is an integer multiple of the non-zero Doppler resolution;

[0035] The power value of the fourth channel estimation result or the fifth channel estimation result of the target object corresponding to at least one of the preset distance and the preset angle is greater than the noise floor power, and the detected Doppler frequency of the target object is not equal to 0;

[0036] The power value of the fourth channel estimation result or the fifth channel estimation result of the target object corresponding to at least one of the preset distance and the preset angle is greater than the noise floor power, and the detected Doppler frequency of the target object is an integer multiple of the non-zero Doppler resolution.

[0037] In a feasible embodiment, the determining to perform clutter estimation based on the second channel estimation result includes:

[0038] Determine whether the second channel estimation result satisfies a preset second condition;

[0039] If the second channel estimation result satisfies the second condition, determine to perform clutter estimation;

[0040] If the second channel estimation result does not satisfy the second condition, perform target detection based on the second channel estimation result;

[0041] Wherein, the second condition includes at least one of the following:

[0042] The noise floor power of the second channel estimation result is greater than or equal to a preset threshold;

[0043] The power of the channel estimation result corresponding to the zero Doppler frequency in the second channel estimation result is greater than or equal to a preset threshold;

[0044] The noise floor power corresponding to at least one of the preset distance and the preset angle in the second channel estimation result is greater than or equal to a preset threshold value;

[0045] The power value of the channel estimation result corresponding to the zero Doppler frequency corresponding to at least one of the preset distance and the preset angle in the second channel estimation result is greater than or equal to a preset threshold value.

[0046] In a feasible embodiment, the preset threshold value is related to the distance of the detected target object and / or the radar cross section area of the target object.

[0047] On the other hand, an embodiment of the present disclosure provides a first node in a wireless communication system. The node includes a transceiver and at least one processor coupled to the transceiver. The at least one processor is configured to execute the method provided in any embodiment of the present disclosure.

[0048] On the other hand, an embodiment of the present disclosure further provides a computer-readable storage medium. A computer program is stored in the storage medium. When the computer program is run by a processor, it executes the method provided in any embodiment of the present disclosure.

[0049] On the other hand, a computer program product is provided. The product includes a computer program. When the computer program is run by a processor, it executes the method provided in any optional embodiment of the present disclosure.

[0050] The beneficial effects brought by the technical solutions provided by the embodiments of the present disclosure will be introduced in combination with specific embodiments below. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 A schematic structural diagram of a wireless network system applicable to the embodiments of the present disclosure is shown;

[0052] Figure 2 A schematic structural diagram of an exemplary base station according to the present disclosure is shown;

[0053] Figure 3 A schematic structural diagram of an exemplary user equipment according to the present disclosure is shown;

[0054] Figure 4 A schematic flowchart of a method executed by a first node provided by the embodiments of the present disclosure is shown;

[0055] Figure 5 A schematic flowchart of a method for performing clutter estimation and clutter cancellation provided by the embodiments of the present disclosure is shown;

[0056] Figure 6 A schematic flowchart of a method for performing first clutter estimation provided by the embodiments of the present disclosure is shown;

[0057] Figure 7 Shows a schematic diagram of grouping the channel estimation results provided by the embodiments of the present disclosure;

[0058] Figure 8 Shows a schematic flowchart of a method for clutter deletion provided by the embodiments of the present disclosure;

[0059] Figure 9 Shows a schematic diagram of arranging the channel estimation results provided by the embodiments of the present disclosure;

[0060] Figure 10 Shows a schematic flowchart of another method for clutter estimation and clutter deletion provided by the embodiments of the present disclosure;

[0061] Figure 11 Shows a schematic flowchart of a method for dynamically determining whether to perform clutter estimation and clutter deletion provided by the embodiments of the present disclosure;

[0062] Figure 12 Shows a schematic diagram of the structure of an electronic device provided by the embodiments of the present disclosure. Detailed implementation manners

[0063] Before proceeding with the following detailed implementation manners, it may be advantageous to set forth definitions of certain words and phrases used throughout the patent document. The term "connect" and its derivatives refer to any direct or indirect communication between two or more elements, regardless of whether those elements are in physical contact with each other. The terms "send," "receive," and "communicate" and their derivatives encompass both direct and indirect communication. The terms "include" and "comprise" and their derivatives mean inclusion without limitation. The term "or" is inclusive, meaning and / or. The phrase "associated with" and its derivatives mean including, being included within, being interconnected with, containing, being contained within, being connected to or being connected with, being coupled to or being coupled with, being communicable with, cooperating with, being intertwined, being juxtaposed, being close to, being bound to or being bound with, having, having the attribute of, having a relationship to or having a relationship with, etc. The term "controller" means any device, system, or part thereof that controls at least one operation. Such a controller may be implemented in hardware or in a combination of hardware and software and / or firmware. The functions associated with any particular controller, whether local or remote, may be centralized or distributed. When the phrase "at least one of" is used to list items, it means that different combinations of one or more of the listed items may be used, and it may only be necessary to have one item in the list. For example, "at least one of A, B, and C" includes any of the following combinations: A, B, C, A and B, A and C, B and C, and only A, only B, only C. Similarly, the term "set" means one or more. Thus, a set of items may be a single item or a set of two or more items.

[0064] Moreover, the various functions described below can be implemented or supported by one or more computer programs, each formed of computer-readable program code and embodied in a computer-readable medium. The terms "application" and "program" refer to one or more computer programs, software components, instruction sets, procedures, functions, objects, classes, instances, related data, or portions thereof that are suitable for implementation in appropriate computer-readable program code. The phrase "computer-readable program code" includes any type of computer code, including source code, object code, and executable code. The phrase "computer-readable medium" includes any type of medium that can be accessed by a computer, such as read only memory (ROM), random access memory (RAM), hard disk drive, compact disc (CD), digital video disc (DVD), or any other type of memory. "Non-transitory" computer-readable media excludes wired, wireless, optical, or other communication links that convey transient electrical or other signals. Non-transitory computer-readable media includes media in which data can be stored permanently and media such as rewritable optical discs or erasable memory devices in which data can be stored and later rewritten.

[0065] Throughout this patent document, definitions are provided for certain other words and phrases. Those of ordinary skill in the art should understand that, in many if not most instances, such definitions apply to the prior as well as future use of the words and phrases so defined.

[0066] The figures included herein and the various embodiments used to describe the principles of the present disclosure are for illustration only and should not be construed in any way as limiting the scope of the present disclosure. In addition, those of ordinary skill in the art will understand that the principles of the present disclosure can be implemented in any appropriately arranged wireless communication system.

[0067] The following Figures 1 to 3 describes various embodiments of the present disclosure implemented in a wireless communication system. Figures 1 to 3 The description does not imply a physical or architectural limitation on the ways in which different embodiments can be implemented. Different embodiments of the present disclosure can be implemented in any appropriately arranged communication system.

[0068] Figure 1 illustrates an example wireless network according to an embodiment of the present disclosure. Figure 1 The embodiment of the wireless network shown is for illustration only. Other embodiments of the wireless network 100 can be used without departing from the scope of the present disclosure.

[0069] As shown Figure 1 in the figure, the wireless network includes base stations (next generation node B, gNB or gNodeB) 101, gNB 102, and gNB 103. gNB 101 communicates with gNB 102 and gNB 103. gNB 101 also communicates with at least one network 130 such as the Internet, a proprietary Internet Protocol (IP) network, or other data networks.

[0070] gNB 102 provides wireless broadband access to network 130 for a plurality of first user equipment (UEs) within the coverage area 120 of gNB 102. The plurality of first UEs includes UE 111 that may be located in a small business (SB); UE 112 that may be located in an enterprise (E); UE 113 that may be located in a WiFi hot spot (HS); UE 114 that may be located in a first residence (R1); UE 115 that may be located in a second residence (R2); and UE 116 that may be a mobile device (M) such as a cellular phone, a wireless laptop computer, a wireless personal digital assistant (PDA), etc. gNB 103 provides wireless broadband access to network 130 for a plurality of second UEs within the coverage area 125 of gNB 103. The plurality of second UEs includes UE 115 and UE 116 as well as user stations (SS, e.g., UEs) 117, 118, and 119. In some embodiments, one or more of gNBs 101 - 103 may communicate with each other and with UEs 111 - 116 using existing wireless communication technologies, and one or more of UEs 111 - 119 may communicate directly with each other (e.g., UEs 117 - 119) using other existing or proposed wireless communication technologies.

[0071] Depending on the network type, the term "base station" or "BS" can refer to any component (or collection of components) configured to provide wireless access to a network, such as a transmit point (TP), a transmit-receive point (TRP), an enhanced (or "evolved") base station (eNodeB or eNB), a 5G base station (gNB), a macrocell, a femtocell, a wireless fidelity (WiFi) access point (AP), or other wireless-capable device. The base station can provide wireless access according to one or more wireless communication protocols, such as 3GPP 5G New Radio (NR), Long Term Evolution (LTE), LTE-Advanced (LTE-A), High Speed Packet Access (HSPA), Wi-Fi 802.11a / b / g / n / ac, etc. For convenience, various names of base station types, devices, and functions can be used interchangeably in this patent document to refer to the network infrastructure components that provide wireless access to remote terminals. Moreover, depending on the network type, the term "user equipment" (UE) can refer to any component such as a mobile station (MS), a user station (SS), a remote terminal, a wireless terminal, a receiving point, or a user device. For convenience, various names of user equipment types, devices, and functions can be used interchangeably in this patent document to refer to the remote wireless device that wirelessly accesses the BS regardless of whether the UE is a mobile device (such as a mobile phone or a smartphone) or a device that is generally regarded as a fixed device (such as a desktop computer or a vending machine).

[0072] The dashed lines illustrate the approximate extent of coverage areas 120 and 125, and coverage areas 120 and 125 are shown as generally circular merely for illustrative and explanatory purposes. It should be clearly understood that coverage areas associated with a gNB, such as coverage areas 120 and 125, can have other shapes, including irregular shapes, depending on the configuration of the gNB and variations in the wireless environment associated with natural and man-made obstacles.

[0073] As described in more detail below, one or more of UEs 111 - 119 include circuitry, programming, or a combination thereof. In certain embodiments, one or more of gNBs 101 - 103 include circuitry, programming, or a combination thereof.

[0074] Although Figure 1 an example of a wireless network is shown, it can be Figure 1Make various changes. For example, the wireless network 100 can include any number of gNBs and any number of UEs in any suitable arrangement. Moreover, gNB 101 can communicate directly with any number of UEs and provide those UEs with wireless broadband access to the network 130. Similarly, each of gNBs 102-103 can communicate directly with the network 130 and provide UEs with direct wireless broadband access to the network 130. Additionally, gNBs 101, 102, and / or 103 can provide access to other or additional external networks such as an external telephone network or other types of data networks.

[0075] Figure 2 FIG. shows an example base station according to an embodiment of the present disclosure. Figure 2 The embodiment of gNB 102 shown in is for illustration only, and Figure 1 gNBs 101 and 103 of may have the same or similar configurations. However, gNBs appear in a variety of configurations, and Figure 2 do not limit the scope of the present disclosure to any particular implementation of gNBs.

[0076] As Figure 2 shown, gNB 102 includes a plurality of antennas 200a-200n, a plurality of radio frequency (RF) transceivers 201a-201n, transmit (TX) processing circuitry 203, and receive (RX) processing circuitry 204. gNB 102 also includes a controller / processor 205, a memory 206, and a backhaul or network interface (IF) 207.

[0077] The RF transceivers 201a-201n receive incoming RF signals, such as signals transmitted by UEs in the network 100, from the antennas 200a-200n. The RF transceivers 201a-201n down-convert the incoming RF signals to generate intermediate frequency (IF) or baseband signals. The IF or baseband signals are sent to the RX processing circuitry 204, which generates a processed baseband signal by filtering, decoding, and / or digitizing the baseband or IF signal. The RX processing circuitry 204 sends the processed baseband signal to the controller / processor 205 for further processing.

[0078] The TX processing circuit 203 receives analog or digital data (such as voice data, web data, e-mail, or interactive video game data) from the controller / processor 205. The TX processing circuit 203 encodes, multiplexes, and / or digitizes the outgoing baseband data to generate a processed baseband or IF signal. The RF transceiver 201a - 201n receives the outgoing processed baseband or IF signal from the TX processing circuit 203 and upconverts the baseband or IF signal to an RF signal transmitted via the antennas 201a - 201n.

[0079] The controller / processor 205 may include one or more processors or other processing devices that control the overall operation of the gNB 102. For example, the controller / processor 205 may control the reception of forward channel signals and the transmission of reverse channel signals performed by the RF transceivers 201a - 201n, the RX processing circuit 204, and the TX processing circuit 203 according to well-known principles. The controller / processor 205 may also support additional functions, such as more advanced wireless communication functions.

[0080] For example, the controller / processor 205 may support beamforming or directional routing operations, in which the outgoing signals from the multiple antennas 200a - 200n are weighted differently to effectively steer the outgoing signals in a desired direction. Any of a variety of other functions may be supported in the gNB 102 by the controller / processor 205.

[0081] The controller / processor 205 is also capable of executing programs and other processes located in the memory 206, such as an operating system (OS). The controller / processor 205 may move data into the memory 206 or out of the memory 206 as needed for the execution of processes.

[0082] The controller / processor 205 is also connected to a backhaul or network interface 207. The backhaul or network interface 207 allows the gNB 102 to communicate with other devices or systems via a backhaul connection or via a network. The interface 207 may support communication via any suitable wired or wireless connection. For example, when the gNB 102 is implemented as part of a cellular communication system (such as a cellular communication system supporting 5G, LTE, or LTE - A), the interface 207 may allow the gNB 102 to communicate with other gNBs via a wired or wireless backhaul connection. When the gNB 102 is implemented as an access point, the interface 207 may allow the gNB 102 to communicate via a wired or wireless local area network or via a wired or wireless connection to a larger network (such as the Internet). The interface 207 includes any suitable structure that supports communication via a wired or wireless connection such as Ethernet or an RF transceiver.

[0083] Memory 206 is connected to controller / processor 205. A portion of memory 206 may include random access memory (RAM), and another portion of memory 206 may include flash memory or other read-only memory (ROM).

[0084] Although Figure 2 illustrates one example of gNB 102, various changes may be made Figure 2 thereto. For example, gNB 102 may include any number of Figure 2 each of the components shown in. As a specific example, the access point may include multiple interfaces 207, and the controller / processor 205 may support routing functions to route data between different network addresses. As another specific example, although shown as including a single instance of TX processing circuit 203 and a single instance of RX processing circuit 204, gNB 102 may include multiple instances of each (such as one for each RF transceiver). For example, Figure 2 the various components in may be combined, further subdivided, or omitted, and additional components may be added according to specific requirements.

[0085] Figure 3 An example user equipment according to an embodiment of the present disclosure is shown. Figure 3 The embodiment of UE 116 shown in is for illustration only, and Figure 1 UEs 111 - 115 and 117 - 119 may have the same or similar configurations. However, UEs appear in a variety of configurations, and Figure 3 the scope of the present disclosure is not limited to any particular implementation of the UE.

[0086] As Figure 3 shown in, UE 116 includes antenna 301, radio frequency (RF) transceiver 302, TX processing circuit 303, microphone 304, and receive (RX) processing circuit 305. UE 116 also includes speaker 306, controller or processor 307, input / output (I / O) interface (IF) 308, input device 309, touch screen display 310, and memory 311. Memory 311 includes OS 312 and one or more applications 313.

[0087] The RF transceiver 302 receives an incoming RF signal transmitted by the gNB of the network 100 from the antenna 301. The RF transceiver 302 down-converts the incoming RF signal to generate an IF or baseband signal. The IF or baseband signal is sent to the RX processing circuit 305, which generates a processed baseband signal by filtering, decoding, and / or digitizing the baseband or IF signal. The RX processing circuit 305 sends the processed baseband signal to the speaker 306 (such as for voice data) or the processor 307 for further processing (such as for web browsing data).

[0088] The TX processing circuit 303 receives analog or digital voice data from the microphone 304 or other outgoing baseband data (such as web data, email, or interactive video game data) from the processor 307. The TX processing circuit 303 encodes, multiplexes, and / or digitizes the outgoing baseband data to generate a processed baseband or IF signal. The RF transceiver 302 receives the outgoing processed baseband or IF signal from the TX processing circuit 303 and up-converts the baseband or IF signal to an RF signal transmitted via the antenna 301.

[0089] The processor 307 may include one or more processors or other processing devices and execute the OS 312 stored in the memory 311 to control the overall operation of the UE 116. For example, the processor 307 may control the reception of forward channel signals and the transmission of reverse channel signals performed by the RF transceiver 302, the RX processing circuit 305, and the TX processing circuit 303 according to well-known principles. In some embodiments, the processor 307 includes at least one microprocessor or microcontroller.

[0090] The processor 307 is also capable of executing other processes and programs located in the memory 311, such as a process for CSI (Channel State Information) reporting on the uplink channel. The processor 307 may move data into the memory 311 or out of the memory 311 as needed to execute the processes. In some embodiments, the processor 307 is configured to execute the application 313 based on the OS 312 or in response to a signal received from the gNB or the operator. The processor 307 is also coupled to the I / O interface 308, which provides the UE 116 with the ability to connect to other devices such as laptop computers and portable computers. The I / O interface 308 is a communication path between these accessories and the processor 307.

[0091] The processor 307 is also connected to a touch screen display 310. A user of the UE 116 can use the touch screen display 310 to input data into the UE 116. The touch screen display 310 can be a liquid crystal display, a light emitting diode display, or other display capable of rendering text and / or at least limited graphics such as from a website.

[0092] A memory 311 is connected to the processor 307. A portion of the memory 311 can include RAM, and another portion of the memory 311 can include flash memory or other ROM.

[0093] Although Figure 3 an example of the UE 116 is shown, various changes can be made to Figure 3 it. For example, Figure 3 the various components in Figure 3 can be combined, further subdivided, or omitted, and additional components can be added according to specific needs. As a specific example, the processor 307 can be divided into multiple processors, such as one or more central processing units (CPUs) and one or more graphics processing units (GPUs). Moreover, although

[0094] With the progress of science and technology, the types of communication devices are increasing. In addition to traditional devices such as mobile phones and computers, it can also include mobile robots, such as autonomous vehicles, drones, etc. This type of mobile device often needs to have the ability to be accurately positioned or be able to be accurately positioned, so as to accurately identify the current situation and make a response, that is, have the positioning ability similar to that provided by radar technology. A direct way can be to equip the communication device with a radar module. However, in recent years, as the working frequency band of the communication system gradually develops towards higher frequency bands, the communication frequency band is gradually approaching the radar frequency band, and the resulting interference and resource conflicts between the communication system and the radar system will be inevitable. One way to solve this problem can be to consider a communication and radar fusion system, called communication perception integration technology, to further enhance the function of the communication system and improve the spectrum efficiency. Currently, both the industrial community and the academic community regard communication perception integration as one of the key technologies for future communication systems.

[0095] The core concept of communication-sensing integration is to use the same set of hardware devices to achieve the sensing function of the surrounding environment at the cost of as little resource overhead as possible while ensuring the basic communication function. That is, the communication nodes in the communication system (such as gNB, UE, repeater, integrated access and backhaul base station IAB, etc.) can simultaneously have the functions of communication and sensing, and can be called communication-sensing integration nodes, hereinafter simply referred to as communication-sensing nodes. To add the sensing function to the communication system, a feasible solution is that the first communication-sensing node (which can also be called the first node) receives the sensing signal (hereinafter simply referred to as the sensing signal) sent by the second communication-sensing node (which can also be called the second node), and obtains the characteristics of the sensing target object according to the signal detection of the sensing signal. Since the propagation channel frequency response will change correspondingly after the sensing signal is reflected by the sensing target object in the environment, and there is a corresponding relationship between the change in the frequency response and the characteristics of the sensing target object such as distance and moving speed, the first communication-sensing node can estimate the propagation channel according to the received sensing channel, so as to obtain the characteristics of the sensing target object. Among them, the characteristics of the sensing target object may include but are not limited to the number of sensing target objects, the distance between the sensing target object and the communication-sensing node, the radial speed of the sensing target object, etc.; and, the sensing target object can be an object accessing the communication network (such as a base station, a terminal, etc.), or an object not accessing the communication network (such as a small unmanned aerial vehicle, a building, an animal, a plant, etc.).

[0096] In the above communication-sensing solution, the first communication-sensing node and the second communication-sensing node can be a base station and a user respectively. For example, the first communication-sensing node is a user equipment and the second communication-sensing node is its serving cell; or the second communication-sensing node is a user equipment and the first communication-sensing node is its serving cell. According to the sensing requirement, the serving cell can configure the user equipment to send or receive the sensing signal. At this time, corresponding to the user equipment being configured to send the uplink sensing signal, the base station of the serving cell receives the uplink sensing signal and performs sensing measurement. Hereinafter, this process is simply referred to as uplink sensing; corresponding to the user equipment being configured to receive the downlink sensing signal, the user equipment receives the downlink sensing signal and performs sensing measurement. Hereinafter, this process is simply referred to as downlink sensing.

[0097] Since the sensed object and the observation node are usually located in the ground or low-altitude environment, the sensing signal received by the communication-sensing node may be affected by clutter in the environment, seriously degrading the sensing performance. Among them, the clutter is caused by the echoes reflected by scatterers in the environment, such as the ground, buildings, trees, etc. Since the scatterers causing the clutter are usually not the target objects actually needed to be sensed, the clutter is an interference signal for the echoes of the target objects actually needed to be sensed (such as people, vehicles, unmanned aerial vehicles, etc.).

[0098] For example, when the synaesthesia node is a stationary node (such as a base station, etc.), the clutter interference caused by the ground and surrounding buildings is an important factor affecting the system's sensing performance, especially for the detection, positioning, and tracking of the often-mentioned "low, slow, small" targets. In the environment, in addition to the reflected signals of the target object to be detected, there are also various kinds of clutter from the reflections of surrounding buildings and the ground, which will affect the detection of stationary or low-speed moving target objects. When the moving speed of the target object is small and the reflected energy is weak, it is more severely affected by the sidelobes of the stationary clutter. If no processing is done and the echoes are directly coherently accumulated, the sidelobes of the clutter may cover the target due to being too strong, greatly reducing the ability of the synaesthesia node to detect the target. Therefore, clutter suppression is a key measure to ensure that the synaesthesia node can detect the target object that needs to be sensed. In particular, the detection of low-speed targets places higher requirements on the ability to eliminate clutter interference.

[0099] In the prior art, the method of clutter suppression can be carried out based on the accumulated echo data. Specifically, after receiving multiple sets of echo data, the multiple sets of echo data are processed. However, since this method needs to centrally process the multiple sets of received echo data, the time delay for accumulating echo data is relatively high, and the complexity of processing the multiple sets of echo data is also relatively high, seriously affecting the estimation efficiency of clutter and the detection of targets. Therefore, how to effectively reduce the time delay and computational complexity of clutter estimation and improve the estimation efficiency of clutter is an urgent problem to be solved.

[0100] In this disclosure, in order to optimize the integrated communication and sensing system, solve or improve one or more existing problems, a sensing solution (a method executed by a first node in the integrated communication system) is provided to reduce the computational complexity and improve the detection ability of the sensed target object. In addition, the solution also proposes a clutter estimation and cancellation method for the communication and sensing node, which can adaptively determine whether to perform non-real-time or real-time clutter estimation. Among them, non-real-time estimation enables the communication and sensing node to adapt to the long-term changes of clutter, and real-time estimation can capture the short-term changes of clutter. Specifically, the non-real-time clutter estimation method accumulates the echoes of the environmental background (environmental clutter), extracts the clutter features from the accumulated echoes (which can be seen in detail in Embodiment 2 below), and based on the results of the non-real-time clutter estimation (the extracted clutter features), the clutter components can be reconstructed in real time, and the clutter components in the echo signal containing clutter and the sensed target object can be effectively cancelled (which can be seen in detail in Embodiment 3 below), thereby improving the detection performance of the sensed target object. The real-time clutter estimation method is based on the real-time accumulated echoes, estimates the clutter channel in the frequency domain, and then performs real-time clutter reconstruction and cancellation (which can be seen in detail in Embodiment 4 below). Moreover, the proposed clutter estimation and cancellation method for the communication and sensing node can also adaptively determine the clutter estimation and cancellation method based on the result of the first clutter cancellation using non-real-time clutter estimation, aiming to improve the detection performance of the target object. Specifically, it dynamically determines whether to continue using the echo signal obtained by performing the first clutter cancellation using the results of non-real-time clutter estimation for target object detection, or use real-time clutter estimation, and use the results of real-time clutter estimation to cancel the clutter, and then perform target object detection based on the echo signal after the first clutter cancellation using the results of real-time clutter estimation (which can be seen in detail in Embodiment 5 below). The advantage of this is that by combining non-real-time clutter estimation with real-time clutter estimation, the communication and sensing node can obtain a more accurate clutter characteristic model, has higher robustness to dynamically changing clutter scenarios and environmental conditions, and realizes better clutter cancellation and target object detection ability.

[0101] The method provided by the embodiments of this disclosure can be executed by any electronic device / node. For example, the node can be a user equipment in a wireless communication system or a network node. Among them, the network node can be a base station or other network nodes.

[0102] It should be noted that for some of the term names involved in the embodiments of the present disclosure, the term names that already exist in communication standards can be adopted. Some term names may be newly added or newly defined term names. For these newly added or newly defined term names, other names may also be adopted in future communication standards, or they can be described in other ways (such as a text description). The names or designations of various signals / information / matrices / spaces / results involved in the embodiments of the present disclosure are not unique. In theory, as long as the functions, the contents included, or the descriptions or explanations of the signals / information / matrices / spaces / results can correspond or be associated, the names or designations of the signals / information / matrices / spaces / results can be changed.

[0103] The technical solutions provided by the present disclosure and the technical effects produced by the technical solutions are described below through the description of various alternative embodiments. Without conflict or contradiction, the following embodiments can refer to, draw on, or combine with each other. For the same terms, similar features, and similar implementation steps in different embodiments, they will not be described repeatedly. In an embodiment including multiple steps, if there is no clear sequence of the multiple steps, the embodiments of the present disclosure do not uniquely limit the execution sequence of the multiple steps.

[0104] The alternative embodiments of the method provided by the present disclosure are further described below in combination with the principle of the solution provided by the present disclosure and several alternative embodiments. The steps of different embodiments can be combined or replaced with each other without conflict.

[0105] Figure 4 A method executed by a first node in a wireless communication system provided by an embodiment of the present disclosure is shown. This method is a clutter estimation method. The first node is a sensing node. Optionally, the first node can be a user equipment, and the first node can also be a base station in a communication system, etc., such as Figure 4 As shown, the method may include S101 - S104:

[0106] S101: Transmit a first signal, receive the echo signal of the first signal, perform channel estimation based on the echo signal, and obtain at least two groups of first channel estimation results;

[0107] S102: Obtain a first clutter estimation result based on at least two groups of first channel estimation results;

[0108] S103: Perform clutter deletion on at least two groups of first channel estimation results based on the first clutter estimation result to obtain a second channel estimation result.

[0109] Among them, at least two groups of first channel estimation results can be obtained through at least one of the following operations A1 - A2:

[0110] A1: Perform channel estimation on the echo signal to obtain a third channel estimation result. In the third channel estimation result, at least one third channel estimation result is obtained every preset interval value and divided into the same group, and at least two groups of first channel estimation results obtained by dividing the channel estimation results are obtained.

[0111] A2: In the echo signal, at least one echo signal is obtained every interval value and divided into the same group to obtain at least two groups of echo signals. Channel estimation is performed on the at least two groups of echo signals to obtain at least two groups of first channel estimation results obtained by dividing the echo signals.

[0112] Optionally, the first signal can be a signal sent by the first node to the second node. Exemplarily, the first node can send the first signal to the second node, and the second node can send the second signal to the first node. In the field of communication and sensing, the first signal and the second signal can be called sensing signals, which can include signals sent for sensing; correspondingly, the first node and the second node can be communication and sensing nodes (also called sensing nodes). After the sensing node sends the sensing signal, it can receive the echo signal of the sensing signal. wherein, the positive integer N p is the number of echoes received by the sensing node, and the positive integer N s is the number of sampling points of a single echo.

[0113] Optionally, when performing channel estimation based on the echo signal, it can be directly performing channel estimation on the echo signal, or first grouping the echo signal, and then performing channel estimation on at least two groups of echo signals obtained after grouping.

[0114] The following describes the operations for obtaining at least two groups of first channel estimation results.

[0115] In a feasible embodiment, the channel estimation results are grouped to obtain at least two groups of first channel estimation results obtained by dividing the channel estimation results. Specifically, perform channel estimation on the echo signal to obtain a third channel estimation result. In the third channel estimation result, at least one third channel estimation result is obtained every preset interval value and divided into the same group, and at least two groups of first channel estimation results obtained by dividing the channel estimation results are obtained.

[0116] Among them, channel estimation can be performed on the obtained N p echo signals to obtain a third channel estimation result It is understandable that the number of echo signals here is the same as the number of third channel estimation results. That is, for one echo signal, one third channel estimation result can be obtained through channel estimation. Optionally, one implementation of obtaining the third channel estimation result can be to perform a discrete Fourier transform (DFT) on the echo signal Y of the first signal to obtain the time-domain expression Y of the echo signal f , and then use the least squares (LS) method for channel estimation to obtain the third channel estimation result where is the reference signal

[0117] Among them, the sensing node can group the third channel estimation results H based on a preset interval value to obtain at least two groups of first channel estimation results after grouping. Exemplarily, when the interval value is 4, one or more third channel estimation results can be obtained every 4 third channel estimation results and divided into the same group to obtain at least two groups of first channel estimation results

[0118] Optionally, the interval value can be the same as the number of groups. For example, it includes N p echo numbers, that is, it includes N p third channel estimation results. N g is the interval value, and the number of third channel estimation results in each group is N b = N p / N g , and the number of frequency-domain sampling points in each group is N z = N b ×N s . Grouping the third channel estimation results can obtain at least two groups of first channel estimation results

[0119] Optionally, for N p third channel estimation results, at least one third channel estimation result can be selected every N g channel estimation results and grouped into the same group. After grouping the third channel estimation results H, at least two groups of first channel estimation results can be obtained

[0120] Optionally, a possible grouping method is as Figure 7 shown. The index of the third channel estimation result of the echo signal of the received first signal is {1, 2,..., Np}, then the set of echo signal indexes constituting the first grouped channel estimation result is {1, N g +1, 2N g +1,... N p -N g +1}, and the set of echo signal indexes constituting the second grouped channel estimation result is {2, Ng +2, 2N g +2, … N p -N g +2}, and so on, to form the N g th set of echo signal index sets of channel estimation results as {N g , 2N g , 3N g ,... N p}.

[0121] Among them, the value of N g is a positive integer greater than 1 and is a divisor of N p . It should be noted that the advantage of grouping the channel estimation results is that through the above grouping method, it can be ensured that each group of channel estimation results contains all the main frequency domain characteristics of the clutter. For example, the Doppler frequency shift characteristic of the clutter is retained, and the frequency domain characteristics of the clutter in each group of channel estimation results are similar. By performing feature analysis on multiple groups of channel estimation results, the accuracy of clutter feature analysis can be improved. In addition, the value of the interval N g depends on the speed (Doppler frequency shift) distribution of the clutter in the environment. When the speed of the clutter is larger or the high-speed clutter occupies a high proportion in the speed distribution of the clutter in the environment, in order to ensure that the Doppler characteristics of the clutter are not lost in each group of channel estimation results (the high-speed clutter can also be reflected in each group of channel estimation results), a smaller interval N g is required to select one, two or more channel estimation results from the first channel estimation result H of the echo signal of the received first signal and group them in the same group. Then, the number of channel estimation results in each group increases, and the interval value N g becomes smaller. For example, for a sensing node, if the speed distribution of the clutter in the current detection scenario is mainly zero speed, the interval value N g can be selected as a larger value; if for a sensing node, if the speed distribution of the clutter in the current detection scenario is mainly high speed, the interval value N g can be selected as a smaller value. The advantage of doing this is that by flexibly adjusting N g , it can be ensured that while not losing the clutter speed information in each group of channel estimation results, multiple groups of channel estimation results can be obtained. Extracting clutter features based on multiple groups of channel estimation results can effectively reduce the influence of noise and improve the accuracy of clutter feature extraction.

[0122] Optionally, when grouping the third channel estimation results based on a preset interval value, the number of groups obtained by division is related to the number of continuously acquired channel estimation results at the interval. In one example, each group of first channel estimation results may include different third channel estimation results acquired at intervals, and there may be consecutive third channel estimation results. In one example, the same third channel estimation result may also be divided into the first channel estimation results of different groups. For example, when grouping, there is the same third channel estimation result in the first channel estimation results of one group and the first channel estimation results of another group.

[0123] Example illustration: Assume there are 10 echo signals (correspondingly 10 third channel estimation results), and the interval value is 5. Then when grouping by acquiring one third channel estimation result every 5 third channel estimation results, the number of groups set can be the same as the interval value, obtaining 5 groups of first channel estimation results; if when grouping by acquiring 2 or more third channel estimation results every 5 third channel estimation results and still keeping the number of groups the same as the interval value, there will be the same third channel estimation result included in the channel estimation results of different groups, that is, a third channel estimation result will be repeatedly sampled and divided into different groups; if when grouping by acquiring 2 or more third channel estimation results every 5 third channel estimation results, the configured number of groups can be related to the number of 2 or more third channel estimation results acquired at intervals. At this time, it can be set that there is no repeated sampling for the same third channel estimation result when grouping.

[0124] In a feasible embodiment, the echo signals are grouped to obtain at least two groups of first channel estimation results obtained by dividing the echo signals. Specifically, in the echo signals, at least one echo signal is acquired every interval value and divided into the same group, obtaining at least two groups of echo signals. Channel estimation is performed on the at least two groups of echo signals to obtain at least two groups of first channel estimation results obtained by dividing the echo signals.

[0125] Among them, the sensing node can group the N p acquired echo signals based on a preset interval value to obtain at least two groups of grouped echo signals. Exemplarily, when the interval value is 5, one or more echo signals can be acquired every 5 echo signals and divided into the same group to obtain at least two groups of echo signals.

[0126] Optionally, the interval value can be the same as the number of groups. For example, it includes N p echo numbers, N g is the interval value, and the number of echo signals in each group is N b = N p / N g , and the number of frequency domain sampling points in each group is Nz = N b ×N s 。

[0127] Optionally, a possible grouping method is given. If the indexes of the echo signals of the first received signal are {1, 2, …, Np}, then the set of echo signal indexes forming the first group is {1, N g +1, 2N g +1, …, N p -N g +1}, the set of echo signal indexes forming the second group is {2, N g +2, 2N g +2, …, N p -N g +2}, and so on. The set of echo signal indexes forming the N g th group is {N g , 2N g , 3N g , …, N p}.

[0128] Among them, the value of N g is a positive integer greater than 1 and is a divisor of N p . It should be noted that the advantage of grouping the echo signals is that through the above grouping method, it can be ensured that each group of echo signals contains all the main frequency domain characteristics of the clutter. For example, the Doppler frequency shift characteristic of the clutter is retained, and the frequency domain characteristics of the clutter in each group of echo signals are similar. By performing channel estimation on multiple groups of echo signals and then performing feature analysis, the accuracy of clutter feature analysis can be improved. In addition, the value of the interval N g depends on the velocity (Doppler frequency shift) distribution of the clutter in the environment. When the velocity of the clutter in the environment is larger or the high-velocity clutter accounts for a high proportion in the velocity distribution of the clutter in the environment, in order to ensure that the Doppler characteristics of the clutter are not lost in each group of echo signals (the high-velocity clutter can also be reflected in each group of echo signals), a smaller interval N g is required to select one, two or more echo signals from the echo signals of the first received signal and group them in the same group. Then, the number of echo signals in each group increases, and the interval value N g becomes smaller. For example, relative to the sensing node, if the velocity distribution of the clutter in the current detection scenario is mainly zero velocity, the interval value N g can be selected as a larger value; if for the sensing node, if the velocity distribution of the clutter in the current detection scenario is mainly high velocity, the interval value N g can be selected as a smaller value. The advantage of doing this is that by flexibly adjusting N g, it can ensure that while not losing the clutter velocity information in each group of echo signals, multiple groups of echo signals can be obtained. After channel estimation based on multiple groups of echo signals, clutter feature extraction can effectively reduce the influence of noise and improve the accuracy of clutter feature extraction.

[0129] Optionally, at least two first channel estimation results are obtained An implementation method can be to perform discrete Fourier transform (DFT) on the echo signal Y of each group to obtain the time-domain expression Y of the echo signal f , and then use the least squares (LS) method for channel estimation to obtain at least two channel estimation results.

[0130] Optionally, when grouping the echo signals based on a preset interval value, the number of divided groups is related to the number of echo signals continuously obtained at the interval. In one example, each group of echo signals may include different echo signals obtained at intervals, and there may be consecutive echo signals. In one example, the same echo signal can also be divided into different groups of echo signals. For example, when grouping, there may be the same echo signal in one group of echo signals as in another group. Example illustration: Suppose there are 10 echo signals and the interval value is 5. Then when obtaining one echo signal for grouping every 5 echo signals, the number of set groups can be the same as the interval value, obtaining 5 groups of echo signals; if obtaining 2 or more echo signals for grouping every 5 echo signals and still keeping the number of groups the same as the interval value, then there will be the same echo signal in different groups of echo signals, that is, an echo signal will be repeatedly sampled and divided into different groups; if obtaining 2 or more echo signals for grouping every 5 echo signals, the configured number of groups can be related to the number of 2 or more echo signals obtained at the interval. At this time, it can be set that the same echo signal is not repeatedly sampled for grouping.

[0131] Optionally, before grouping the third channel estimation result or the echo signal, it further includes: determining the interval value based on the Doppler frequency distribution of non-target objects in the preset detection scenario.

[0132] Among them, the target object in the preset detection scenario includes the object to be detected in this detection scenario. Exemplarily, assuming that the current task is to detect drones in the sky, then the target object in the detection scenario at this time is the drone, and the objects other than the drone are non-target objects (clutter), such as clouds, birds, etc. The interval value can be determined based on the Doppler frequency distribution preset for the non-target object. Exemplarily, assuming that the current task is to detect ships in the sea, then the ship is the target object in the detection scenario at this time, and the objects other than the ship are non-target objects (clutter), such as sea water, fish, etc. The interval value can be determined based on the Doppler frequency distribution preset for the non-target object.

[0133] Optionally, the sensing node can perform clutter feature analysis based on at least two sets of first channel estimation results G to obtain the first clutter estimation result. The method for performing clutter feature analysis can be eigenvalue decomposition (EVD) or singular value decomposition (SVD). A possible implementation is to perform singular value decomposition on at least two sets of first channel estimation results G, G = U∑V H , where U and V are orthogonal matrices, and ∑ is a diagonal matrix containing singular values. Based on the result of singular value decomposition, select the first L, L ≤ N g principal singular vectors in the matrix V (or U) corresponding to the largest singular values to form a matrix for estimating the clutter subspace. The matrix C composed of the principal singular vectors can capture the main characteristics of the clutter echo. When L << N g , the complexity of clutter reconstruction based on the matrix C composed of the principal singular vectors can be greatly reduced. In general outdoor scenarios, the value of L can be 2 to 4. It should be noted that according to the processing ability of the sensing node, the process of clutter feature analysis can be real-time or non-real-time; among them, real-time means that after the sensing node receives the N p th echo signal, the sensing node can complete the clutter feature analysis and extract the clutter features before obtaining the next N p echo signals; non-real-time means that after the sensing node receives the kN p th echo signal, the sensing node performs clutter feature analysis based on the first N p echoes at the beginning and extracts the clutter features. Among them, the value of k depends on the data processing ability of the sensing node. The larger the k value, the weaker the data processing ability of the sensing node. The advantage of the sensing node being able to perform real-time clutter feature analysis is that the extracted clutter features can reflect the current environmental clutter in real time, and the environmental clutter can be deleted to significantly improve the sensing performance. When the sensing node can perform non-real-time clutter feature analysis, the extracted clutter features can be used for subsequent real-time clutter reconstruction to delete clutter in the received echo signals.

[0134] Optionally, the first clutter estimation result obtained from clutter feature analysis includes a matrix for clutter reconstruction (the process of obtaining this matrix can be regarded as the first clutter estimation). The sensing node can obtain a matrix for clutter reconstruction (the first clutter estimation result) based on the matrix C composed of the principal singular vectors, and this matrix can be used for real-time clutter reconstruction and clutter removal. A possible implementation method for obtaining the clutter reconstruction matrix is to obtain based on matrix C where matrix P1 can be saved for subsequent real-time clutter estimation. Alternatively, another implementation method for obtaining the clutter reconstruction matrix is to obtain based on matrix C where matrices P2 and C can be saved for subsequent real-time clutter estimation. The advantage of this method is to reduce the computational complexity during matrix multiplication. It should be noted that when the background environment changes, matrix P1 or P2 and C may no longer reflect the clutter after the environmental change. Therefore, it is necessary to re-execute the clutter estimation method provided in the above embodiments to obtain new matrices P1 or P2 and C to reflect the clutter after the environmental change.

[0135] Optionally, the sensing node can reconstruct the first clutter estimation result (also known as the first clutter signal) based on the matrix for clutter reconstruction (matrix P1 or P2 and C) and at least two sets of first channel estimation results G. A possible method for reconstructing the clutter estimation result includes obtaining the reconstructed first clutter signal H based on the reconstructed matrix P1 and at least two sets of first channel estimation results G c1 = P1G. Or, a possible method for reconstructing the clutter signal includes obtaining the reconstructed clutter signal H based on the reconstructed matrix P2 and C and at least two sets of first channel estimation results G c1 = P2C H G.

[0136] Optionally, when the sensing node performs clutter removal on at least two sets of first channel estimation results, it can delete the reconstructed clutter signal (the first clutter deletion) from at least two sets of first channel estimation results G to obtain the channel estimation result G after the first clutter deletion r . Specifically, the deletion method can be G r = G - H c1 . The sensing node rearranges the elements in the channel estimation result G after the first clutter deletion r to obtain the channel estimation result after the first clutter deletion (also known as the second channel estimation result) A possible arrangement method is as Figure 9 shown. Each group of elements (each group of elements contains Ns sampling points) in the first column of G r is indexed by {1, N g +1, 2N g +1,... N p - Ng +1} are arranged in the columns of H r1 , and each group of elements (each group of elements contains Ns sampling points) in the second column of G r is arranged in the columns of H with indices {2, N g + 2, 2N g + 2, … N p - N g + 2} are arranged in the columns of H r1 , and so on. Each group of elements (each group of elements contains Ns sampling points) in the N r -th column of G g is arranged in the columns of H with indices {N g , 2N g , 3N g ,... N p}, and finally the second channel estimation result H r1 is formed. r1 .

[0137] Optionally, the first clutter estimation may be a non-real-time clutter estimation.

[0138] In the embodiments of the present disclosure, a set of accumulated echo signals can be processed. When at least two groups of first channel estimation results are obtained through channel estimation, the third channel estimation result or the echo signal can be grouped based on a preset interval value, so as to effectively reduce the operation complexity and improve the processing efficiency through grouping; and clutter feature extraction can also be performed based on multiple groups of channel estimation results, which can effectively reduce the influence of noise and improve the accuracy of clutter feature extraction, so as to effectively improve the detection ability of the perceived target object.

[0139] In a feasible embodiment, the provided method further includes S104: If it is determined to perform clutter estimation based on the second channel estimation result, at least one of the following operations 1 - 2 is executed:

[0140] Operation 1: Perform clutter estimation based on at least one of the first signal and the third channel estimation result to obtain a second clutter estimation result, and perform clutter deletion on the third channel estimation result based on the second clutter estimation result to obtain a fourth channel estimation result, so as to perform target detection based on at least one of the second channel estimation result and the fourth channel estimation result.

[0141] Among them, the communication and sensing node can determine whether real-time clutter estimation (second clutter estimation) is required based on the second channel estimation result. Exemplarily, the second clutter estimation may be a real-time clutter estimation performed by the communication and sensing node based on at least one of the frequency-domain signal of the received first signal and the third channel estimation result, and the obtained second clutter estimation result can be used for clutter deletion.

[0142] Optionally, if the integrated sensing and communication node determines that a second clutter estimation needs to be performed, the integrated sensing and communication node performs a second clutter estimation based on the first signal and / or the third channel estimation result to obtain a second clutter estimation result, and performs clutter cancellation on the third channel estimation result based on the second clutter estimation result to obtain a fourth channel estimation result. Conversely, if the integrated sensing and communication node determines that a second clutter estimation does not need to be performed, the integrated sensing and communication node no longer performs clutter estimation and clutter cancellation. Subsequently, based on this determination result, the integrated sensing and communication node can perform target detection based on the second channel estimation result and / or the fourth channel estimation result.

[0143] Operation 2: Perform clutter estimation based on at least one of the first signal and at least two groups of first channel estimation results obtained by dividing the echo signal to obtain a third clutter estimation result, and perform clutter cancellation on at least two groups of first channel estimation results obtained by dividing the echo signal based on the third clutter estimation result to obtain a fifth channel estimation result, so as to perform target detection based on at least one of the second channel estimation result and the fifth channel estimation result.

[0144] Among them, the integrated sensing and communication node can determine whether real-time clutter estimation (third clutter estimation) needs to be performed based on the second channel estimation result. Exemplarily, the third clutter estimation can be real-time clutter estimation performed by the integrated sensing and communication node based on at least one of the frequency-domain signal of the received first signal and at least two groups of first channel estimation results obtained by dividing the echo signal, and the obtained third clutter estimation result can be used for clutter cancellation.

[0145] Optionally, if the integrated sensing and communication node determines that a third clutter estimation needs to be performed, the integrated sensing and communication node performs a third clutter estimation based on the first signal and / or at least two groups of first channel estimation results obtained by dividing the echo signal to obtain a third clutter estimation result, and performs clutter cancellation on at least two groups of first channel estimation results obtained by dividing the echo signal based on the third clutter estimation result to obtain a fifth channel estimation result. Conversely, if the integrated sensing and communication node determines that a third clutter estimation does not need to be performed, the integrated sensing and communication node no longer performs clutter estimation and clutter cancellation. Subsequently, based on this determination result, the integrated sensing and communication node can perform target detection based on the second channel estimation result and / or the fifth channel estimation result.

[0146] In a feasible embodiment, performing clutter estimation based on at least one of the first signal and the third channel estimation result to obtain a second clutter estimation result, and performing clutter cancellation on the third channel estimation result based on the second clutter estimation result includes:

[0147] Construct a first matrix of the clutter subspace based on at least one of the frequency-domain signal of the first signal and the third channel estimation result;

[0148] Perform signal reconstruction based on the first matrix and the third channel estimation result to obtain a reconstructed second clutter estimation result;

[0149] Based on the second clutter estimation result, perform clutter removal on the third channel estimation result to obtain a fourth channel estimation result.

[0150] Optionally, the sensing node obtains a projection matrix of the clutter subspace (also referred to as the first matrix. The process of obtaining the first matrix can be regarded as performing a second clutter estimation, a real-time clutter estimation). A possible method for the sensing node to obtain the clutter subspace is to use the least squares (LS) algorithm to construct the clutter subspace in the frequency domain based on the frequency domain signal (reference signal) of the transmitted sensing signal. Among them, the sensing signal can be a symbol based on OFDM modulation. Specifically, the constructed clutter subspace can be expressed as where r k,i , k = 1,..., N s , i = 1,..., N p is the reference signal corresponding to the k-th subcarrier in the frequency domain signal of the i-th sensing signal, N p is the number of sensing signals, and N s is the number of subcarriers of the frequency domain signal of the sensing signal. Based on the clutter subspace, the communication-sensing node can obtain the projection matrix of the clutter subspace When the number of subcarriers is N s , the communication-sensing node can obtain N s projection matrices P i , i = 1,..., N s . Optionally, based on the same method, a clutter subspace can also be constructed based on the third channel estimation result, and a projection matrix can be obtained based on the clutter subspace. In a feasible embodiment, a possible method for the sensing node in the present disclosure to obtain the clutter subspace can also be to construct the clutter subspace based on the frequency domain signal of the first signal and the third channel estimation result, and obtain the first matrix based on this clutter subspace.

[0151] Optionally, the sensing node reconstructs the second clutter signal (also known as the second clutter estimation result) based on the projection matrix of the clutter subspace. The reconstruction method can be that the sensing node multiplies the third channel estimation result obtained by performing channel estimation on the echo signal of the first signal by the projection matrix of the clutter subspace to obtain the second clutter signal. The third channel estimation result is where h i , i = 1,..., N p is the channel estimation result corresponding to a single sensing signal. The sensing signal can be a symbol modulated based on OFDM symbols, and z i , i = 1,..., N sis the frequency-domain estimation result corresponding to a single subcarrier of the frequency-domain sensing signal. H may be the third channel estimation result in the above embodiments, and the example of obtaining this channel estimation result will not be elaborated here. The sensing node is based on N s projection matrices P of the clutter subspaces i , i = 1, ..., N s , to obtain the second clutter signal where where z i , i = 1, ..., N s is the frequency-domain estimation result corresponding to a single subcarrier of the frequency-domain sensing signal.

[0152] Optionally, the sensing node deletes the reconstructed second clutter signal H from the third channel estimation result H c2 (second clutter deletion), to obtain the fourth channel estimation result H r2 (also known as the channel estimation result after the second clutter deletion). Specifically, the deletion method may be that H r2 = H - H c2 .

[0153] In a feasible embodiment, clutter estimation is performed based on at least one of at least two groups of first channel estimation results obtained from the first signal and the divided echo signals, to obtain a third clutter estimation result, and clutter deletion is performed on at least two groups of first channel estimation results obtained from the divided echo signals based on the third clutter estimation result, to obtain a fifth channel estimation result, including:

[0154] Construct a second matrix of the clutter subspace based on at least one of at least two groups of first channel estimation results obtained from the frequency-domain signal of the first signal and the divided echo signals;

[0155] Perform signal reconstruction based on the second matrix and at least two groups of first channel estimation results obtained from the divided echo signals, to obtain a reconstructed third clutter estimation result;

[0156] Based on the third clutter estimation result, perform clutter deletion on at least two groups of first channel estimation results obtained from the divided echo signals, to obtain a fifth channel estimation result.

[0157] Optionally, the method of constructing the second matrix (projection matrix) of the clutter subspace and the method of performing signal reconstruction to obtain the third clutter estimation result may refer to the methods of obtaining the first matrix and the second clutter estimation result in the above embodiments, and the present disclosure will not elaborate here.

[0158] Optionally, after obtaining the fourth channel estimation result or the fifth channel estimation result, the following steps are further included:

[0159] Determine whether the target detection based on the fourth channel estimation result or the fifth channel estimation result satisfies the first condition;

[0160] If the first condition is satisfied, construct a third matrix of the clutter subspace based on the result of the target detection;

[0161] Perform signal reconstruction based on the third matrix and the third channel estimation result to obtain a reconstructed fourth clutter estimation result; or, perform signal reconstruction based on the third matrix and at least two groups of first channel estimation results obtained by dividing the echo signals to obtain a fifth clutter estimation result;

[0162] Based on the fourth clutter estimation result, perform clutter cancellation on the third channel estimation result to obtain a sixth channel estimation result; or, based on the fifth clutter estimation result, perform clutter cancellation on at least two groups of first channel estimation results obtained by dividing the echo signals to obtain a seventh channel estimation result.

[0163] Optionally, the method for constructing the third matrix (projection matrix) of the clutter subspace may refer to the method for constructing the first matrix of the clutter subspace in the above embodiments, and details are not described herein again.

[0164] In one example, the sensing node may apply a target detection algorithm to the fourth channel estimation result H r2 or the fifth channel estimation result to detect and estimate the parameters of the target object to be detected. Specifically, the sensing node may perform an inverse discrete Fourier transform (IDFT) on each column of H r2 to obtain a signal in the time delay domain, perform a Fourier transform (DFT) on each row to obtain a signal in the Doppler domain, and detect the distance or speed (Doppler frequency) of one or more target objects through fixed threshold detection, where the distance can be obtained by multiplying the time delay by the speed of light and dividing by 2, and the speed can be obtained by multiplying the Doppler frequency by the wavelength of the sensing signal and dividing by 2.

[0165] Optionally, the sensing node may determine whether there is a target object that satisfies the first condition based on the result of the target detection or during the target detection process. If so, repeat the above step of determining whether the first condition is satisfied; if not, end.

[0166] Optionally, the first condition includes at least one of the following:

[0167] (1) The power value of the fourth channel estimation result or the fifth channel estimation result of the detected target object is greater than the noise floor power (greater than 0), and the detected Doppler frequency of the target object is not equal to 0;

[0168] Wherein, to determine the power value p of the fourth channel estimation result or the fifth channel estimation result of the target object i, i = 1, ..., N t Whether 0 < p1 < p is satisfied i , and the Doppler f i > 0, where N t is the number of perceived targets detected by the communication-sensing node, p1 (unit: watt) is a fixed threshold. Specifically, a possible value of p1 is the noise floor power, and the value of the noise floor power can be a theoretical value or an actual measured value. The advantage of setting such conditions is that when the power value of the fourth-channel estimation result or the fifth-channel estimation result of a real target object with non-zero Doppler is greater than p1, there may be false targets at the same distance. This condition can quickly screen out the real target objects that may produce false targets, reducing the impact on target detection due to clutter deletion.

[0169] (2) It is detected that the power value of the fourth-channel estimation result or the fifth-channel estimation result of the target object is greater than the noise floor power (greater than 0), and the detected Doppler frequency of the target object is a non-zero integer multiple of the Doppler resolution.

[0170] Among them, it is determined whether the power value p of the fourth-channel estimation result or the fifth-channel estimation result of the target object i , i = 1, ..., N t satisfies 0 < p1 < p i , and the Doppler f i = kf Δ , k = 1, 2, ..., N t , where N t is the number of perceived targets detected by the communication-sensing node, p1 (unit: watt) is a fixed threshold, and f Δ (unit: hertz) is the Doppler resolution. Specifically, a possible value of p1 is the noise floor power, and the value of the noise floor power can be a theoretical value or an actual measured value. The advantage of setting such conditions is that when the Doppler of a real target object with non-zero Doppler is an integer multiple of the Doppler resolution, the power value of the fourth-channel estimation result or the fifth-channel estimation result of the corresponding false target at the same distance will be relatively high. This condition can effectively screen out the real target objects with relatively high power values of the fourth-channel estimation result or the fifth-channel estimation result of the corresponding false targets, reducing the computational complexity of the clutter subspace projection matrix and reducing the impact on target detection due to clutter deletion.

[0171] (3) It is detected that the power value of the fourth-channel estimation result or the fifth-channel estimation result of the target object corresponding to at least one of the preset distance and the preset angle is greater than the noise floor power, and the detected Doppler frequency of the target object is not equal to 0.

[0172] Compared with condition (1) in the first condition, in condition (3), the consideration of at least one of the distance and the angle is added. In some specific scenarios, it may be necessary to consider the situation within a certain distance or at a certain angle. At this time, in order to effectively reduce the computational complexity and improve the efficiency of target detection, according to the needs of the current detection task, the fourth channel estimation result or the fifth channel estimation result of the target object corresponding to at least one of the preset distance and the preset angle can be considered.

[0173] (4) It is detected that the power value of the fourth channel estimation result or the fifth channel estimation result of the target object corresponding to at least one of the preset distance and the preset angle is greater than the noise floor power, and the detected Doppler frequency of the target object is an integer multiple of the non-zero Doppler resolution.

[0174] Compared with condition (2) in the first condition, in condition (4), the consideration of at least one of the distance and the angle is added. In some specific scenarios, it may be necessary to consider the situation within a certain distance or at a certain angle. At this time, in order to effectively reduce the computational complexity and improve the efficiency of target detection, according to the needs of the current detection task, the fourth channel estimation result or the fifth channel estimation result of the target object corresponding to at least one of the preset distance and the preset angle can be considered.

[0175] It should be noted that when the fourth channel estimation result or the fifth channel estimation result of the target object with a non-zero Doppler frequency is relatively high, clutter deletion after real-time clutter estimation will result in the appearance of false targets, leading to a high false alarm probability. Specifically, the Doppler frequency of the false target is 0, and it is at the same distance as the detected perceived target object with a non-zero Doppler frequency. The advantage of using the first condition to select the detected target that meets the conditions is that it can simply screen out the perceived target object that causes false targets to suppress the influence of the false targets caused by it.

[0176] In an example, the sensing node obtains a new clutter subspace projection matrix based on the target detection result, and then reconstructs the clutter signal. Specifically, the sensing node can be based on the clutter subspace is the reference signal corresponding to the k-th subcarrier in the frequency-domain signal of the i-th sensing signal, N p is the number of sensing signals, N s is the number of subcarriers in the frequency-domain signal of the sensing signal, and a new clutter subspace is constructed Among them, f i , i = 1,..., N v is the Doppler frequency corresponding to the N v target objects with non-zero Doppler detected in S905, T s is the signal duration of the sensing signal, Np is the number of sensing signals. Based on the new clutter subspace, the communication-sensing node can obtain the projection matrix of the new clutter subspace When the number of subcarriers is N s the communication-sensing node can obtain N s projection matrices of the new clutter subspace It should be noted that the advantage of constructing the clutter signal using the projection of the new clutter subspace is that it can effectively suppress the false targets generated by target objects with high Doppler frequencies at the equidistant zero Doppler frequency, reducing the false alarm probability.

[0177] In a feasible embodiment, determining clutter estimation based on the second channel estimation result includes:

[0178] Determine whether the second channel estimation result meets a preset second condition;

[0179] If the second channel estimation result meets the second condition, determine to perform clutter estimation;

[0180] If the second channel estimation result does not meet the second condition, perform target detection based on the second channel estimation result.

[0181] Optionally, the sensing node can determine whether to meet the second condition based on the second channel estimation result H r1 When meeting the second condition, perform real-time clutter estimation and clutter deletion, and when not meeting the second condition, can perform target object detection based on the second channel estimation result.

[0182] Optionally, the sensing node can apply a target detection algorithm to the second channel estimation result H r1 to detect and estimate the parameters of the target object required. Specifically, the sensing node can obtain the signal in the time-delay domain by performing the inverse discrete Fourier transform (IDFT) on each column of H r1 and obtain the signal in the Doppler domain by performing the Fourier transform (DFT) on each row, and detect the distance or speed (Doppler frequency) of one or more target objects through fixed threshold detection, where the distance can be obtained by multiplying the time delay by the speed of light and dividing by 2, and the speed can be obtained by multiplying the Doppler frequency by the wavelength of the sensing signal and dividing by 2.

[0183] Optionally, the second condition includes at least one of the following:

[0184] (1) The noise floor power of the second channel estimation result is greater than or equal to a preset threshold, and this preset threshold is related to the distance of the detected target object and / or the radar cross-sectional area of the target object.

[0185] Among them, the second channel estimation result H r1The noise floor power (unit: watt) is greater than p2 + Δ. Here, the value of p2 (unit: watt) can be the noise floor power value of the channel estimation result after the first clutter estimation and deletion based on the channel estimation result. Δ (unit: watt) is a positive number greater than zero, and its value depends on the target object sensed in the environment. Specifically, when the distance of the target object is relatively close and / or its radar cross-section (RCS) is relatively high, Δ can take a relatively large value; when the distance of the target object is relatively far and / or its radar cross-section (RCS) is relatively small, Δ can take 0 or a relatively small value. When H r1 When the noise floor power of is greater than p2 + Δ, it can be reflected that the clutter estimation result reconstructed based on the clutter reconstruction matrix can no longer reflect the clutter in the current environment. After the sensing node deletes the clutter, the remaining clutter components will affect the detection of the actual target object.

[0186] (2) The power of the channel estimation result corresponding to the zero Doppler frequency in the second channel estimation result is greater than or equal to a preset threshold.

[0187] Among them, the zero Doppler frequency indicates a stationary target object, that is, a Doppler frequency with a speed of zero.

[0188] (2.1) The average power value of the channel estimation result corresponding to the zero Doppler component of the second channel estimation result is greater than or equal to a preset threshold, and this preset threshold is related to the distance of the detected target object and / or the radar cross-section of the target object.

[0189] Among them, the channel estimation result h corresponding to the zero Doppler component after clutter deletion r1 The average power (unit: watt) is greater than p3 + Δ. Here, the value of p3 (unit: watt) can be the average power value of the zero Doppler component estimation result after clutter estimation and deletion based on the channel estimation result. Δ (unit: watt) is a positive number greater than zero, and its value depends on the target object sensed in the environment. Specifically, when the distance of the target object is relatively close and / or its radar cross-section (RCS) is relatively high, Δ can take a relatively large value; when the distance of the target object is relatively far and / or its radar cross-section (RCS) is relatively small, Δ can take 0 or a relatively small value. When h r1 When the average power of is greater than p3 + Δ, it can be reflected that the clutter estimation result reconstructed based on the clutter reconstruction matrix can no longer reflect the zero Doppler clutter component (static) in the current environment. After the sensing node deletes the clutter, the remaining zero Doppler clutter components will affect the detection of the static actual target object.

[0190] (2.2) The highest power value of the channel estimation result corresponding to the zero Doppler component of the second channel estimation result is greater than or equal to a preset threshold, and this preset threshold is related to the distance of the detected target object and / or the radar cross-section of the target object.

[0191] Among them, the channel estimation result h corresponding to the zero Doppler component after clutter deletion r1 has a maximum power greater than p4 + Δ. Here, the value of p4 (unit: watt) can be the maximum power value of the zero Doppler component estimation result after clutter estimation and deletion based on the channel estimation result. Δ (unit: watt) is a positive number greater than zero, and its value depends on the target object sensed in the environment. Specifically, when the distance of the target object is relatively close and / or its radar cross-sectional area (RCS) is relatively high, Δ can take a relatively large value; when the distance of the target object is relatively far and / or its radar cross-sectional area (RCS) is relatively small, Δ can take 0 or a relatively small value. By determining whether the maximum power of h r1 is greater than p4 + Δ, it can be simply and quickly determined whether the clutter estimation result reconstructed based on the clutter reconstruction matrix can still reflect the zero Doppler clutter component (static) in the current environment, resulting in the remaining zero Doppler clutter component affecting the detection of the static actual target object after the sensing node performs clutter deletion.

[0192] (3) The noise floor power corresponding to at least one of the preset distance and the preset angle in the second channel estimation result is greater than or equal to a preset threshold.

[0193] Compared with condition (1) in the second condition, condition (3) adds the consideration of at least one of the distance and the angle. In some detection scenarios, it may be necessary to consider the situation at certain distances and angles. In order to effectively reduce the computational complexity and improve the target detection efficiency, the low noise function corresponding to at least one of the preset distance and the preset angle in the second channel estimation result can be considered according to the current detection requirement.

[0194] (4) The power value of the channel estimation result corresponding to the zero Doppler frequency corresponding to at least one of the preset distance and the preset angle in the second channel estimation result is greater than or equal to a preset threshold.

[0195] Compared with condition (2) in the second condition, condition (4) adds the consideration of at least one of the distance and the angle. In some specific scenarios, it may be necessary to consider the situation within certain distances or at certain angles. At this time, in order to effectively reduce the computational complexity and improve the efficiency of target detection, the channel estimation result corresponding to the zero Doppler frequency corresponding to at least one of the preset distance and the preset angle can be considered according to the current detection task requirement.

[0196] The method provided by the present disclosure will be described below in conjunction with some optional embodiments (the first, second, third, etc. in the following embodiments are independent of the expressions in the above embodiments, that is, the order of the first, second, etc. in Embodiments 1 to 5 below only acts on the corresponding embodiments).

[0197] Embodiment 1

[0198] This embodiment provides a method for adaptively estimating and removing clutter, as Figure 5 shown, the specific steps are as follows:

[0199] S401: The communication-sensing node performs a first clutter estimation, and then based on the first clutter estimation result, the communication-sensing node performs a first clutter removal on the echo signal of the first signal sent. Among them, the first clutter estimation can be the above non-real-time clutter estimation, and the result of this clutter estimation cannot be obtained in real time and needs a certain processing time to be obtained (such as Embodiment 2), and it is impossible to perform clutter removal on the currently received echo signal based on the first clutter estimation result. After obtaining the first clutter estimation result, based on the first clutter estimation result, the communication-sensing node can perform real-time clutter reconstruction and perform a first clutter removal on the echo signal of the first signal sent (such as the channel estimation result after the first clutter removal) (such as Embodiment 3). The first signal can be a signal for sensing sent by the communication-sensing node.

[0200] S402: The communication-sensing node determines whether a second clutter estimation is required based on the echo signal after the first clutter removal (such as Embodiment 5). Specifically, the second clutter estimation can be a real-time clutter estimation that the communication-sensing node can perform based on the received echo signal, and the obtained second clutter estimation result can be used to perform clutter removal on the currently received echo signal (such as the channel estimation result corresponding to this echo signal) (such as Embodiment 4). If the communication-sensing node determines that a second clutter estimation is required, the communication-sensing node performs a second clutter estimation to obtain a second clutter estimation result, and performs a second clutter removal on the echo signal based on the second clutter estimation result. Conversely, if the communication-sensing node determines that a second clutter estimation is not required, the communication-sensing node does not perform a second clutter estimation and a second clutter removal.

[0201] S403: The communication-sensing node performs target object detection based on the echo signal after the first and / or second clutter removal.

[0202] Embodiment 2

[0203] According to Embodiment 1, the method for the communication-sensing node to perform the first clutter estimation is as Figure 6 shown:

[0204] S501: The sensing node sends a sensing signal and receives an echo signal where the positive integer N p is the number of echoes received by the sensing node, and the positive integer N s is the number of sampling points of a single echo.

[0205] S502: The sensing node performs channel estimation on the echo signal to obtain a channel estimation result Specifically, one implementation of obtaining the channel estimation result is to perform a discrete Fourier transform (DFT) on the echo signal Y to obtain the time-domain expression Y of the echo signal f , and then use the least squares (LS) method for channel estimation to obtain where is the reference signal

[0206] S503: The sensing node groups the channel estimation results H to obtain N g groups of channel estimation results, and the number of channel estimation results in each group is N b = N p / N g , and the number of frequency-domain sampling points in each group is N z = N b ×N s . That is, for N p channel estimation results, every N g channel estimation results are selected and grouped into the same group. After grouping the channel estimation results H, the grouped channel estimation results can be obtained Specifically, a possible grouping method is as Figure 7 shown. The index of the channel estimation result of the received echo signal is {1, 2,..., Np}, then the set of echo signal indices constituting the first group of channel estimation results is {1, N g +1, 2N g +1,..., N p -N g +1}, the set of echo signal indices constituting the second group of channel estimation results is {2, N g +2, 2N g +2,..., N p -N g +2}, and so on. The set of echo signal indices constituting the N g th group of channel estimation results is {N g , 2N g , 3N g ,..., N p}. Among them, the value of N g is a positive integer greater than 1 and is a divisor of N p . It should be noted that the advantage of grouping the channel estimation results is that through the above grouping method, it can be ensured that each group of channel estimation results contains all the main frequency-domain characteristics of the clutter. For example, the Doppler frequency shift characteristics of the clutter are retained, and the frequency-domain characteristics of the clutter in each group of channel estimation results are similar. By performing feature analysis on multiple groups of channel estimation results, the accuracy of clutter feature analysis can be improved. In addition, the number of groups N gIts value depends on the velocity (Doppler shift) distribution of clutter in the environment. When the velocity of the clutter is higher or the high-velocity clutter accounts for a high proportion in the velocity distribution of the clutter in the environment, in order to ensure that the Doppler characteristics of the clutter are not lost in each group of channel estimation results (the high-velocity clutter can also be reflected in each group of channel estimation results), a smaller interval N is required. g is used to select a channel estimation result from the channel estimation results H of the received echo signals to be grouped in the same group, then the number of channel estimation results in each group increases, and the number of groups N g becomes smaller. For example, for a sensing node, if the velocity distribution of the clutter in the current detection scenario is mainly zero velocity, the number of groups N g can be selected as a larger value; if for a sensing node, if the velocity distribution of the clutter in the current detection scenario is mainly high velocity, the number of groups N g can be selected as a smaller value. The advantage of doing this is that by flexibly adjusting N g , it can be ensured that while not losing the clutter velocity information in each group of channel estimation results, multiple groups of channel estimation results can be obtained. Based on multiple groups of channel estimation results for clutter feature extraction can effectively reduce the influence of noise and improve the accuracy of clutter feature extraction.

[0207] S504: The sensing node performs clutter feature analysis based on the grouped channel estimation results G. The method for performing clutter feature analysis can be eigenvalue decomposition (EVD) or singular value decomposition (SVD). Specifically, a possible implementation is to perform singular value decomposition on the grouped channel estimation results G, G = U∑V H , where U and V are orthogonal matrices, and ∑ is a diagonal matrix containing singular values. Based on the result of singular value decomposition, select the first L, L ≤ N g main singular vectors in the matrix V (or U) corresponding to the largest singular values to form a matrix for estimating the clutter subspace. The matrix C composed of the main singular vectors can capture the main characteristics of the clutter echo. When L << N g , the complexity of clutter reconstruction based on the matrix C composed of the main singular vectors can be greatly reduced. In a general outdoor scenario, the value of L can be 2 - 4. It should be noted that according to the processing ability of the sensing node, the process of clutter feature analysis can be real-time or non-real-time. Among them, real-time can be that after the sensing node receives the N p th echo signal, the sensing node can complete the clutter feature analysis and extract the clutter features before obtaining the next N p echo signals; non-real-time can be that after the sensing node receives the kN p th echo signal, the sensing node is based on the first N pAfter analyzing the clutter characteristics of k echoes and extracting the clutter characteristics, the value of k depends on the data processing ability of the sensing node. The larger the value of k, the weaker the data processing ability of the sensing node. The advantage of the sensing node being able to perform real-time clutter feature analysis is that the extracted clutter features can reflect the current environmental clutter in real time. After deleting the environmental clutter, the sensing performance can be significantly improved. When the sensing node can only perform non-real-time clutter feature analysis, the extracted clutter features can be used for subsequent real-time clutter reconstruction to delete clutter in the received echo signal.

[0208] S505: The sensing node obtains a matrix for clutter reconstruction (the first clutter estimate) based on the matrix C composed of the main singular vectors. This matrix can be used for real-time clutter reconstruction and clutter deletion. A possible implementation method for obtaining the clutter reconstruction matrix is to obtain, based on matrix C, where matrix P1 can be saved for subsequent real-time clutter estimation. Alternatively, another implementation method for obtaining the clutter reconstruction matrix is to obtain, based on matrix C, where matrix P2 and C can be saved for subsequent real-time clutter estimation. The advantage of this method is to reduce the computational complexity when multiplying matrices. The specific process of clutter reconstruction based on the clutter reconstruction matrix is described in detail in Embodiment 3. It should be noted that when the background environment changes, matrix P1 or P2 and C may no longer reflect the clutter after the environmental change. Therefore, it is necessary to re-obtain the new matrix P1 or P2 and C according to steps S501 - S505 to reflect the clutter after the environmental change.

[0209] Embodiment 3

[0210] According to Embodiment 1, the method for the communication and sensing node to perform the first clutter deletion is as Figure 8 shown:

[0211] S701: The sensing node sends a sensing signal and receives an echo signal where the positive integer N p is the number of echoes received by the sensing node, and the positive integer N s is the number of sampling points of a single echo.

[0212] S702: The sensing node performs channel estimation on the echo signal to obtain a channel estimation result Specifically, one implementation way to obtain the channel estimation result is to perform a discrete Fourier transform (DFT) on the echo signal Y to obtain the time-domain expression Y of the echo signal f , and then use the least squares method (LS) for channel estimation to obtain where is the reference signal.

[0213] S703: The sensing node groups the channel estimation result H to obtain Ng Group channel estimation results, where the number of echoes in each group is N b = N p / N g , and the number of frequency-domain sampling points in each group is N z = N b × N s . Then, after grouping the channel estimation results H, the grouped channel estimation results can be obtained Specifically, a possible grouping method is as Figure 7 shown. The indexes of the channel estimation results of the received echo signals are {1, 2,..., Np}. Then, the set of echo signal indexes that constitute the first group of channel estimation results is {1, N g +1, 2N g +1,... N p - N g +1}, the set of echo signal indexes that constitute the second group of channel estimation results is {2, N g +2, 2N g +2,... N p - N g +2}, and so on. The set of echo signal indexes that constitute the N g th group of channel estimation results is {N g , 2N g , 3N g ,... N p}.

[0214] S704: The sensing node reconstructs the clutter signal based on the matrix for clutter reconstruction and the grouped channel estimation results G. Among them, the acquisition of the matrix P1 or P2 and C for clutter reconstruction can be seen in Embodiment 2, which will not be elaborated here. A possible method for reconstructing the clutter signal is to obtain the reconstructed clutter signal H c1 = P1G. Or, a possible method for reconstructing the clutter signal is to obtain the reconstructed clutter signal H c1 = P2C H G.

[0215] S705: The sensing node deletes the reconstructed clutter signal (the first clutter deletion) from the grouped channel estimation results G to obtain the clutter-deleted channel estimation results G r . Specifically, the deletion method can be G r = G - H c1 .

[0216] S706: The sensing node rearranges the elements in the clutter-deleted channel result G r to obtain A possible arrangement is as Figure 9 shown. Each group of elements in the first column of G r (each group of elements contains Ns sampling points) is arranged in the columns of H g with indices {1, N g + 1, 2N p + 1, …, N g - N r1 + 1}. Each group of elements in the second column of G r (each group of elements contains Ns sampling points) is arranged in the columns of H g with indices {2, N g + 2, 2N p + 2, …, N g - N r1 + 2}, and so on. Each group of elements in the N r -th column of G g (each group of elements contains Ns sampling points) is arranged in the columns of H g with indices {N g , 2N g , 3N p , …, N r1}, finally forming the channel estimation result H r1 after clutter removal.

[0217] S707: The sensing node can apply a target detection algorithm to the channel estimation result H r1 to detect and estimate the parameters of the desired target object. Specifically, the sensing node can obtain the signal in the time-delay domain by performing an inverse discrete Fourier transform (IDFT) on each column of H r1 , and obtain the signal in the Doppler domain by performing a Fourier transform (DFT) on each row. Through fixed-threshold detection, the distance or speed (Doppler frequency) of one or more target objects can be detected, where the distance can be obtained by multiplying the time delay by the speed of light and dividing by 2, and the speed can be obtained by multiplying the Doppler frequency by the wavelength of the sensing signal and dividing by 2.

[0218] Example 4

[0219] According to Example 1, the method for the communication and sensing node to perform the second clutter estimation and the second clutter removal is as Figure 10 shown:

[0220] S901: The sensing node obtains the projection matrix of the clutter subspace (second clutter estimation). A possible method for the sensing node to obtain the clutter subspace is to construct the clutter subspace in the frequency domain based on the frequency-domain signal (reference signal) of the transmitted sensing signal using the least squares (LS) algorithm, where the sensing signal can be a symbol based on OFDM modulation. Specifically, the constructed clutter subspace can be expressed as where r k,i, k = 1, ..., N s , i = 1, ..., N p is the reference signal corresponding to the k-th subcarrier in the frequency-domain signal of the i-th sensing signal, and N p is the number of sensing signals, and N s is the number of subcarriers of the frequency-domain signal of the sensing signal. Based on the clutter subspace, the communication-sensing node can obtain the projection matrix of the clutter subspace When the number of subcarriers is N s , the communication-sensing node can obtain N s projection matrices P of the clutter subspace i , i = 1, ..., N s .

[0221] S902: The sensing node reconstructs the clutter signal based on the projection matrix of the clutter subspace. The reconstruction method can be that the sensing node multiplies the channel estimation result by the projection matrix of the clutter subspace to obtain the clutter signal. The channel estimation result is where h i , i = 1, ..., N p is the channel estimation result corresponding to a single sensing signal. The sensing signal can be a symbol modulated based on an OFDM symbol, and z i , i = 1, ..., N s is the frequency-domain estimation result corresponding to a single subcarrier of the frequency-domain sensing signal. H can be obtained in the manner of S701 and S702 in Embodiment 3, which will not be elaborated here. The sensing node is based on N s projection matrices P of the clutter subspace i , i = 1, ..., N s , to obtain the clutter signal where where z i , i = 1, ..., N s is the frequency-domain estimation result corresponding to a single subcarrier of the frequency-domain sensing signal.

[0222] S903: The sensing node deletes the reconstructed clutter signal H c2 (the second clutter deletion) from the channel estimation result H to obtain the channel estimation result H r2 after clutter deletion. Specifically, the deletion method can be that H r2 = H - H c2 .

[0223] S904: The sensing node can apply the target detection algorithm to the channel estimation result H r2 to detect and estimate the parameters of the desired target object. Specifically, the sensing node can detect and estimate the parameters of the desired target object by performing operations on H rPerform the inverse discrete Fourier transform (IDFT) column by column to obtain the signal in the time delay domain, and perform the Fourier transform (DFT) row by row to obtain the signal in the Doppler domain. Through fixed threshold detection, the distance or speed (Doppler frequency) of single or multiple target objects is detected. Among them, the distance can be obtained by multiplying the time delay by the speed of light and dividing by 2, and the speed can be obtained by multiplying the Doppler frequency by the wavelength of the sensing signal and dividing by 2.

[0224] S905: Based on the target detection result of S904, the sensing node determines whether there is a target object that satisfies the first condition. If so, perform S906; if not, end.

[0225] Specifically, the first condition can be at least one of the following:

[0226] (1) Determine whether the power value p of the channel estimation result after the second clutter deletion of the target object i , i = 1,..., N t satisfies 0 < p1 < p i , and the Doppler f i > 0, where N t is the number of sensing targets detected by the communication and sensing node, p1 (unit: watt) is a fixed threshold. Specifically, a possible value of p1 is the noise floor power, and the value of the noise floor power can be a theoretical value or an actual measured value. The advantage of setting such conditions is that when the power value of the channel estimation result after the second clutter deletion of a real target object with non-zero Doppler is greater than p1, there may be false targets at the same distance. This condition can quickly screen out the real target objects that may produce false targets and reduce the impact on target detection due to clutter deletion.

[0227] (2) Determine whether the power value p of the channel estimation result after the second clutter deletion of the target object i , i = 1,..., N t satisfies 0 < p1 < p i , and the Doppler f i = kf Δ , k = 1, 2,..., N t , where N t is the number of sensing targets detected by the communication and sensing node, p1 (unit: watt) is a fixed threshold, f Δ(Unit: Hertz) is the Doppler resolution. Specifically, a possible value of p1 is the noise floor power, and the value of the noise floor power can be a theoretical value or a value obtained by actual measurement. The advantage of setting such conditions is that when the Doppler of a real target object with non-zero Doppler is an integer multiple of the Doppler resolution, the power value of the channel estimation result after deleting the second clutter of the corresponding false target at the same distance will be relatively high. This condition can effectively screen out the real target objects with relatively high power values of the channel estimation results after deleting the second clutter of the corresponding false targets, reduce the computational complexity of the clutter subspace projection matrix, and reduce the impact on target detection due to clutter deletion.

[0228] (3) It is detected that the power value of the channel estimation result after deleting the second clutter of the target object corresponding to at least one of the preset distance and the preset angle is greater than the noise floor power, and the detected Doppler frequency of the target object is not equal to 0.

[0229] (4) It is detected that the power value of the channel estimation result after deleting the second clutter of the target object corresponding to at least one of the preset distance and the preset angle is greater than the noise floor power, and the detected Doppler frequency of the target object is a non-zero integer multiple of the Doppler resolution.

[0230] It should be noted that when the power value of the channel estimation result after deleting the second clutter of the target object with non-zero Doppler frequency is relatively high, clutter deletion in S903 will cause the appearance of false targets, resulting in a high false alarm probability. Specifically, the Doppler frequency of the false target is 0, and it has the same distance as the detected sensing target object with non-zero Doppler frequency. The advantage of using the first condition to select the detection targets that meet the conditions is that it can simply screen out the sensing target objects that cause false targets to suppress the influence of the false targets they cause (step 906).

[0231] S906: The sensing node obtains a new clutter subspace projection matrix based on the target detection result, and then proceeds to S902. Specifically, the sensing node can be based on the clutter subspace is the reference signal corresponding to the k-th subcarrier in the frequency-domain signal of the i-th sensing signal, N p is the number of sensing signals, N s is the number of subcarriers of the frequency-domain signal of the sensing signal, and constructs a new clutter subspace where f i , i = 1,..., N v is the Doppler frequency corresponding to the N v target objects with non-zero Doppler detected in S905, T s is the signal duration of the sensing signal, N pis the number of sensed signals. Based on the new clutter subspace, the communication-sensing node can obtain the projection matrix of the new clutter subspace When the number of subcarriers is N s the communication-sensing node can obtain N s projection matrices of the new clutter subspace It should be noted that the advantage of constructing the clutter signal using the projection of the new clutter subspace is that it can effectively suppress the false targets generated by the target objects with high Doppler frequencies at the equidistant zero Doppler frequency, reducing the false alarm probability.

[0232] Embodiment 5

[0233] According to Embodiment 1, the communication-sensing node determines whether to perform the second clutter estimation and the method of the second clutter deletion based on the echo signal after the first clutter deletion as Figure 11 shown below:

[0234] S1001: The sensing node performs channel estimation on the echo signal to obtain the channel estimation result Specifically, one implementation manner of obtaining the channel estimation result is to perform a discrete Fourier transform (DFT) on the echo signal to obtain the time-domain expression Y of the echo signal f , and then use the least squares method (LS) for channel estimation to obtain where the positive integer N p is the number of echo signals received by the sensing node, the positive integer N s is the number of sampling points of a single echo, is the reference signal.

[0235] S1002: The sensing node performs the first clutter deletion on the channel estimation result to obtain the channel estimation result H after the first clutter deletion r1 . It should be noted that the reconstruction matrix used for reconstructing the first clutter in the first clutter deletion can be obtained based on the grouping method described in Embodiment 2 (specifically described in S504 and will not be elaborated here); it can also be obtained by using the traditional non-grouping method, based on the received multi-group echoes of N p sensed signals, and extracting the clutter characteristics of the echo signals to obtain the clutter reconstruction matrix.

[0236] S1003: The sensing node determines whether the second condition is satisfied based on the channel estimation result H after the first clutter deletion r1 . When the second condition is satisfied, S1004 is performed; when the second condition is not satisfied, S1005 is performed.

[0237] Specifically, the second condition can be at least one of the following:

[0238] (1) Channel estimation result H after the first clutter deletion r1 The noise floor power (unit: watt) is greater than p2 + Δ, where the value of p2 (unit: watt) can be the noise floor power value of the channel estimation result after the first clutter estimation and deletion based on the channel estimation result, and Δ (unit: watt) is a positive number greater than zero. Its value depends on the target object sensed in the environment. Specifically, when the distance of the target object is relatively close and / or its radar cross-section (RCS) is relatively high, Δ can take a larger value; when the distance of the target object is relatively far and / or its radar cross-section (RCS) is relatively small, Δ can take 0 or a smaller value. When H r1 's noise floor power is greater than p2 + Δ, it can be reflected that the first clutter estimation result reconstructed based on the first clutter reconstruction matrix can no longer reflect the clutter in the current environment. After the sensing node deletes the first clutter, the remaining clutter components will affect the detection of the actual target object.

[0239] (2) Channel estimation result h corresponding to the zero Doppler component after the first clutter deletion r1 The average power (unit: watt) is greater than p3 + Δ, where the value of p3 (unit: watt) can be the average power value of the zero Doppler component estimation result after the first clutter estimation and deletion based on the channel estimation result, and Δ (unit: watt) is a positive number greater than zero. Its value depends on the target object sensed in the environment. Specifically, when the distance of the target object is relatively close and / or its radar cross-section (RCS) is relatively high, Δ can take a larger value; when the distance of the target object is relatively far and / or its radar cross-section (RCS) is relatively small, Δ can take 0 or a smaller value. When h r1 's average power is greater than p3 + Δ, it can be reflected that the first clutter estimation result reconstructed based on the first clutter reconstruction matrix can no longer reflect the zero Doppler clutter component (static) in the current environment. After the sensing node deletes the first clutter, the remaining zero Doppler clutter components will affect the detection of the static actual target object.

[0240] (3) The highest power of the channel estimation result h corresponding to the zero Doppler component after the first clutter deletion r1 is greater than p4 + Δ, where the value of p4 (unit: watt) can be the highest power value of the zero Doppler component estimation result after the first clutter estimation and deletion based on the channel estimation result, and Δ (unit: watt) is a positive number greater than zero. Its value depends on the target object sensed in the environment. Specifically, when the distance of the target object is relatively close and / or its radar cross-section (RCS) is relatively high, Δ can take a larger value; when the distance of the target object is relatively far and / or its radar cross-section (RCS) is relatively small, Δ can take 0 or a smaller value. By determining h r1When the highest power is greater than p4 + Δ, it can be simply and quickly determined whether the first clutter estimation result reconstructed based on the first clutter reconstruction matrix can still reflect the zero-Doppler clutter component (static) in the current environment. As a result, after the sensing node deletes the first clutter, the remaining zero-Doppler clutter component will affect the detection of the static actual target object.

[0241] (4) The background noise power corresponding to at least one of the preset distance and the preset angle in the channel estimation result after the first clutter deletion is greater than or equal to a preset threshold.

[0242] (5) The power value of the channel estimation result corresponding to the zero-Doppler frequency corresponding to at least one of the preset distance and the preset angle in the channel estimation result after the first clutter deletion is greater than or equal to a preset threshold.

[0243] S1004: The sensing node performs second clutter estimation and second clutter deletion on the channel estimation result to obtain the channel estimation result H after the second clutter deletion. r2 Specifically, the specific methods of the second clutter estimation and the second clutter deletion can be the methods described in Embodiment 4; or it can be a traditional method of clutter estimation and deletion that does not include S905 and S906.

[0244] S1005: The sensing node can apply the target detection algorithm to the channel estimation result H r2 to detect and estimate the parameters of the target object required. Specifically, the sensing node can obtain the signal in the time-delay domain by performing an inverse discrete Fourier transform (IDFT) on H column by column, and obtain the signal in the Doppler domain by performing a Fourier transform (DFT) row by row. Through fixed-threshold detection, the distance or speed (Doppler frequency) of one or more target objects is detected, where the distance can be obtained by multiplying the time delay by the speed of light and dividing by 2, and the speed can be obtained by multiplying the Doppler frequency by the wavelength of the sensing signal and dividing by 2. r2 It should be noted that the optional solutions provided in the above multiple embodiments of the present disclosure can be implemented alone, or in different embodiments, the implementation steps of each embodiment can also be combined and implemented when the implementation steps do not conflict.

[0245] Based on the same principle as the method provided in the embodiments of the present disclosure, the embodiments of the present disclosure also provide a node, which may include a transceiver and at least one processor coupled to the transceiver, and the at least one processor may execute the solution provided in any optional embodiment of the present disclosure. The node can be any electronic device, such as a user device or a network node.

[0246]

[0247] ​Optionally, the above node may be a first node, and the at least one processor may be configured to execute any method executed by the first node provided in the embodiments of the present disclosure.

[0248] Embodiments of the present disclosure also provide an electronic device, which includes at least one transceiver and at least one processor coupled to the at least one transceiver. The at least one processor is configured to execute the method provided in any optional embodiment of the present disclosure.

[0249] Figure 12 FIG. shows a schematic structural diagram of an electronic device applicable to the embodiments of the present disclosure, as Figure 12 shown, Figure 12 The electronic device 4000 shown includes: a processor 4001 and a memory 4003. Among them, the processor 4001 and the memory 4003 are connected, such as through a bus 4002. Optionally, the electronic device 4000 may further include a transceiver 4004, and the transceiver 4004 may be used for data interaction between the electronic device and other electronic devices, such as data sending and / or data receiving, etc. It should be noted that in practical applications, the transceiver 4004 is not limited to one, and the structure of the electronic device 4000 does not constitute a limitation to the embodiments of the present disclosure. Optionally, the electronic device may be a node in a wireless communication system, such as a first node. The node in the network may be a user equipment, or a base station or other network nodes.

[0250] The processor 4001 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It may implement or execute various exemplary logic blocks, modules, and circuits described in conjunction with the content of the present disclosure. The processor 4001 may also be a combination that implements a computing function, such as a combination including one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0251] The bus 4002 may include a path for transmitting information between the above components. The bus 4002 may be a PCI (Peripheral Component Interconnect) bus, an EISA (Extended Industry Standard Architecture) bus, or the like. The bus 4002 may be divided into an address bus, a data bus, a control bus, etc. For the sake of representation, Figure 12 only a thick line is used in Figure 12 , but it does not mean that there is only one bus or one type of bus.

[0252] The memory 4003 may be a ROM (Read Only Memory) or other type of static storage device that can store static information and instructions, a RAM (Random Access Memory) or other type of dynamic storage device that can store information and instructions, or it may also be an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory), or other optical disc storage, optical disc storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), magnetic disk storage media, other magnetic storage devices, or any other medium that can be used to carry or store computer programs and can be read by a computer, which is not limited herein.

[0253] The memory 4003 is used to store the computer program for implementing the embodiments of the present disclosure and is controlled by the processor 4001 to execute. The processor 4001 is used to execute the computer program stored in the memory 4003 to implement the steps shown in the foregoing method embodiments.

[0254] The embodiments of the present disclosure provide a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the steps and corresponding contents of the foregoing method embodiments can be implemented.

[0255] The embodiments of the present disclosure further provide a computer program product, including a computer program, and when the computer program is executed by a processor, the steps and corresponding contents of the foregoing method embodiments can be implemented.

[0256] The terms "first", "second", "third", "fourth", "1", "2", etc. (if any) in the description, claims and above-mentioned drawings of the present disclosure are used to distinguish similar objects and do not necessarily describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances so that the embodiments of the present disclosure described herein can be implemented in an order other than that shown or described in words.

[0257] It should be understood that although the flowcharts of the embodiments of the present disclosure indicate various operation steps by arrows, the execution order of these steps is not limited to the order indicated by the arrows. Unless there is a clear description in this article, in some implementation scenarios of the embodiments of the present disclosure, the implementation steps in each flowchart can be executed in other orders according to requirements. In addition, some or all of the steps in each flowchart may include multiple sub-steps or multiple stages based on the actual implementation scenario. Some or all of these sub-steps or stages can be executed at the same time, and each sub-step or stage of these sub-steps or stages can also be executed at different times respectively. In the scenario where the execution times are different, the execution order of these sub-steps or stages can be flexibly configured according to requirements, and the embodiments of the present disclosure do not limit this.

[0258] The above text and drawings are provided only as examples to assist the reader in understanding the present disclosure. They are not intended and should not be construed as limiting the scope of the present disclosure in any way. Although certain embodiments and examples have been provided, it will be apparent to those skilled in the art based on the content disclosed herein that, without departing from the scope of the present disclosure, changes can be made to the illustrated embodiments and examples, and other similar implementation means based on the technical idea of the present disclosure can be adopted, which also fall within the protection scope of the embodiments of the present disclosure.

Claims

1. A method performed by a first node in a communication system, characterized in that, The method includes: Sending a first signal, receiving an echo signal of the first signal, performing channel estimation based on the echo signal, and obtaining at least two groups of first channel estimation results; Obtaining a first clutter estimation result based on the at least two groups of first channel estimation results; Performing clutter deletion on the at least two groups of first channel estimation results based on the first clutter estimation result to obtain a second channel estimation result; Wherein, the at least two groups of first channel estimation results are obtained through at least one of the following operations: Performing channel estimation on the echo signal to obtain a third channel estimation result, and obtaining at least two groups of first channel estimation results obtained by dividing the channel estimation results by taking at least one third channel estimation result at intervals of a preset interval value in the third channel estimation result; Taking at least one echo signal at intervals of the interval value in the echo signal and dividing them into the same group to obtain at least two groups of echo signals, and performing channel estimation on the at least two groups of echo signals to obtain at least two groups of first channel estimation results obtained by dividing the echo signals.

2. The method according to claim 1, characterized in that, The interval value is determined based on the Doppler frequency distribution of non-target objects.

3. The method according to claim 1, wherein The method further includes that if it is determined to perform clutter estimation based on the second channel estimation result, then at least one of the following is executed: Performing clutter estimation based on at least one of the first signal and the third channel estimation result to obtain a second clutter estimation result, and performing clutter deletion on the third channel estimation result based on the second clutter estimation result to obtain a fourth channel estimation result, so as to perform target detection based on at least one of the second channel estimation result and the fourth channel estimation result; Performing clutter estimation based on at least one of the first signal and at least two groups of first channel estimation results obtained by dividing the echo signals to obtain a third clutter estimation result, and performing clutter deletion on at least two groups of first channel estimation results obtained by dividing the echo signals based on the third clutter estimation result to obtain a fifth channel estimation result, so as to perform target detection based on at least one of the second channel estimation result and the fifth channel estimation result.

4. The method according to claim 3, wherein The performing clutter estimation based on at least one of the first signal and the third channel estimation result to obtain a second clutter estimation result, and performing clutter deletion on the third channel estimation result based on the second clutter estimation result to obtain a fourth channel estimation result includes: Constructing a first matrix of a clutter subspace based on at least one of the frequency-domain signal of the first signal and the third channel estimation result; Performing signal reconstruction based on the first matrix and the third channel estimation result to obtain a reconstructed second clutter estimation result; Performing clutter deletion on the third channel estimation result based on the second clutter estimation result to obtain a fourth channel estimation result; The performing clutter estimation based on at least one of the first signal and at least two groups of first channel estimation results obtained by dividing the echo signals to obtain a third clutter estimation result, and performing clutter deletion on at least two groups of first channel estimation results obtained by dividing the echo signals based on the third clutter estimation result to obtain a fifth channel estimation result includes: Construct a second matrix of the clutter subspace based on at least one of the frequency-domain signals of the first signal and at least two groups of first channel estimation results obtained by dividing the echo signals. Perform signal reconstruction based on the second matrix and at least two groups of first channel estimation results obtained by dividing the echo signals to obtain a reconstructed third clutter estimation result. Based on the third clutter estimation result, perform clutter deletion on at least two groups of first channel estimation results obtained by dividing the echo signals to obtain a fifth channel estimation result.

5. The method according to claim 4, characterized in that, Further include: Determine whether the target detection based on the fourth channel estimation result or the fifth channel estimation result satisfies a first condition. If the first condition is satisfied, construct a third matrix of the clutter subspace based on the result of the target detection. Perform signal reconstruction based on the third matrix and the third channel estimation result to obtain a reconstructed fourth clutter estimation result; or, perform signal reconstruction based on the third matrix and at least two groups of first channel estimation results obtained by dividing the echo signals to obtain a fifth clutter estimation result. Based on the fourth clutter estimation result, perform clutter deletion on the third channel estimation result to obtain a sixth channel estimation result. Or, based on the fifth clutter estimation result, perform clutter deletion on at least two groups of first channel estimation results obtained by dividing the echo signals to obtain a seventh channel estimation result. Wherein, the first condition includes at least one of the following: The power value of the fourth channel estimation result or the fifth channel estimation result of the detected target object is greater than the noise floor power, and the Doppler frequency of the detected target object is not equal to 0. The power value of the fourth channel estimation result or the fifth channel estimation result of the detected target object is greater than the noise floor power, and the Doppler frequency of the detected target object is an integer multiple of the Doppler resolution that is non-zero. The power value of the fourth channel estimation result or the fifth channel estimation result of the target object corresponding to at least one of the preset distance and the preset angle is greater than the noise floor power, and the Doppler frequency of the detected target object is not equal to 0. The power value of the fourth channel estimation result or the fifth channel estimation result of the target object corresponding to at least one of the preset distance and the preset angle is greater than the noise floor power, and the Doppler frequency of the detected target object is an integer multiple of the Doppler resolution that is non-zero.

6. The method according to claim 3, characterized in that, The determination of performing clutter estimation based on the second channel estimation result includes: Determine whether the second channel estimation result satisfies a preset second condition. If the second channel estimation result satisfies the second condition, determine to perform clutter estimation. If the second channel estimation result does not satisfy the second condition, perform target detection based on the second channel estimation result. Wherein, the second condition includes at least one of the following: The noise floor power of the second channel estimation result is greater than or equal to a preset threshold. The power of the channel estimation result corresponding to the zero Doppler frequency in the second channel estimation result is greater than or equal to a preset threshold. The noise floor power corresponding to at least one of the preset distance and the preset angle in the second channel estimation result is greater than or equal to a preset threshold; The power value of the channel estimation result corresponding to the zero Doppler frequency corresponding to at least one of the preset distance and the preset angle in the second channel estimation result is greater than or equal to a preset threshold.

7. The method according to claim 6, wherein The preset threshold is related to the distance of the detected target object and / or the radar cross section area of the target object.

8. The first node in a communication system, characterized in that, The first node includes a transceiver and at least one processor coupled to the transceiver, and the at least one processor is configured to execute the method according to any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that, A computer program is stored in the storage medium, and when the computer program is run by a processor, it executes the method according to any one of claims 1 to 7.

10. A computer program product, the product comprising a computer program, characterized in that, When the computer program is run by a processor, it executes the method according to any one of claims 1 to 7.

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