Signal processing method and device, storage medium, chip system and program product

Abstract

The embodiment of the invention provides a signal processing method and device, a storage medium, a chip system and a program product, and relates to the technical field of communication. The method comprises the steps that first information sent by a sensing target is received, the first information is used for indicating a judgment threshold, and the judgment threshold is related to the building density and / or the height of the position where the sensing target is located. When the log-likelihood ratio LLR of the first path and the second path is greater than or equal to the judgment threshold, using the first path to send a first signal; when the LLR is smaller than the judgment threshold, the second path is used for sending the first signal, and the positioning error of the second path is larger than that of the first path. According to the method provided by the invention, the accuracy of identifying the LOS path and the NLOS path is improved.

Description

Technical Field

[0001] The present application relates to the field of communication technology, and in particular to a signal processing method, device, storage medium, chip system and program product. Background Art

[0002] With the rapid development of communication technology, Integrated Sensing and Communication (ISAC) has emerged as an emerging technological concept. ISAC deeply integrates communication and perception functions, aiming to leverage the same hardware and spectrum resources to achieve efficient information transmission while providing accurate perception of the surrounding environment. This will bring new application models and developments to numerous fields, including intelligent transportation, smart cities, and the Industrial Internet of Things. In ISAC, signal propagation paths include line-of-sight (LOS) and non-line-of-sight (NLOS) paths. Accurately identifying LOS and NLOS paths is crucial for ISAC. However, current methods for identifying LOS and NLOS paths suffer from low accuracy.

[0003] Therefore, how to improve the accuracy of identifying LOS paths and NLOS paths is an urgent problem to be solved. Summary of the Invention

[0004] Embodiments of the present application provide a signal processing method, apparatus, storage medium, chip system, and program product, which are applied to the field of communication technology to improve the accuracy of identifying LOS paths and NLOS paths.

[0005] In a first aspect, an embodiment of the present application provides a signal processing method. The method includes:

[0006] receiving first information sent by a sensing target, where the first information is used to indicate a decision threshold, where the decision threshold is correlated with a building density and / or height at a location where the sensing target is located;

[0007] When a log likelihood ratio (LLR) between the first path and the second path is greater than or equal to the decision threshold, sending the first signal using the first path;

[0008] When the LLR is less than the decision threshold, the first signal is sent using the second path, and the positioning error of the second path is greater than that of the first path.

[0009] Optionally, the first information includes a first parameter and / or a second parameter, the first parameter is used to determine the building density at the location of the perception target, and the second parameter is used to determine the height at the location of the perception target.

[0010] Optionally, the first parameter includes the positioning position of the perception target.

[0011] Optionally, the decision threshold is related to a first path a priori probability and a second path a priori probability, and the first path a priori probability is related to the first parameter and / or the second parameter.

[0012] Optionally, the second path prior probability is negatively correlated with the first path prior probability.

[0013] Optionally, the first parameter is related to multiple building densities within a target time window, and the second parameter is related to multiple heights within the target time window.

[0014] Optionally, the first parameter is related to a mean value of multiple building densities within a target time window, and the second parameter is related to a mean value of multiple heights within the target time window.

[0015] Optionally, the length of the target time window belongs to a target length interval.

[0016] Optionally, the lower limit of the target length interval is greater than or equal to a first value, and the first value is greater than or equal to the signal transmission delay of the perception target.

[0017] Optionally, the upper limit of the target length interval is less than or equal to a second value, and the second value is related to the movement speed of the perception target and the effective radius of the cell where the perception target is located.

[0018] Optionally, the second value is equal to the quotient of the effective radius and the movement speed.

[0019] Optionally, the LLR is related to an observation parameter of a second signal, where the second signal is a signal preceding the first signal.

[0020] Optionally, the observation parameters of the second signal include at least one of the following: skewness, kurtosis, peak-to-average ratio, arrival angle skewness, arrival angle variance, and time delay.

[0021] Optionally, when the arrival angle spread of the second signal is greater than or equal to a first preset threshold, the observation parameter includes the arrival angle variance;

[0022] In a case where the arrival angle spread of the second signal is less than a second preset threshold, the observation parameter includes the arrival angle deviation, and the second preset threshold is less than or equal to the first preset threshold.

[0023] Optionally, the first path prior probability is related to the first parameter, and the method further includes:

[0024] The first path prior probability is generated according to the first prior probability function corresponding to the first path and the first parameter, where the first prior probability function is related to a building density reference value and a building density attenuation factor.

[0025] Optionally, the first path prior probability is related to the second parameter, and the method further includes:

[0026] The first path prior probability is generated according to a second prior probability function corresponding to the first path and the second parameter, where the second prior probability function is related to an altitude reference value and an altitude attenuation factor.

[0027] Optionally, the first path prior probability is related to the first parameter and the second parameter, and the method further includes:

[0028] The first path prior probability is generated according to the first prior probability function corresponding to the first path, the first parameter, the second prior probability function, the second parameter, the first weight of the first prior probability function, and the second weight of the second prior probability function.

[0029] In a second aspect, an embodiment of the present application provides a signal processing device, the device comprising:

[0030] A receiving module, configured to receive first information sent by a sensing target, where the first information is used to indicate a decision threshold, where the decision threshold is correlated with a building density and / or height at a location where the sensing target is located;

[0031] A control module is configured to use the first path to send the first signal when the log likelihood ratio (LLR) between the first path and the second path is greater than or equal to the decision threshold; and use the second path to send the first signal when the LLR is less than the decision threshold, and the positioning error of the second path is greater than that of the first path.

[0032] In a third aspect, an embodiment of the present application provides a signal processing device, comprising a processor and a memory, wherein the memory is used to store computer-executable instructions, and the processor is used to run the computer-executable instructions stored in the memory to execute the method described in the first aspect or any possible implementation of the first aspect.

[0033] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, in which a computer program or instruction is stored. When the computer program or instruction is run on a computer, the computer executes the method described in the first aspect or any possible implementation of the first aspect.

[0034] In a fifth aspect, an embodiment of the present application provides a computer program product comprising a computer program, which, when executed, enables a computer to execute the method described in the first aspect or any possible implementation of the first aspect.

[0035] In a sixth aspect, the present application provides a chip or chip system, comprising at least one processor and a communication interface, wherein the communication interface and the at least one processor are interconnected via a line, and the at least one processor is configured to execute a computer program or instruction to perform the method described in the first aspect or any possible implementation of the first aspect. The communication interface in the chip may be an input / output interface, a pin, or a circuit.

[0036] In one possible implementation, the chip or chip system described above in this application further includes at least one memory, wherein instructions are stored in the at least one memory. The memory may be a storage unit within the chip, such as a register or cache, or a storage unit of the chip (such as a read-only memory or random access memory).

[0037] The signal processing method, device, storage medium, chip system and program product provided by the embodiments of the present application receive first information sent by the sensing target to indicate the decision threshold of the building density and / or height related to the location of the sensing target, and use the first path to send the first signal when the LLR of the first path and the second path is greater than or equal to the decision threshold; and use the second path to send the first signal when the LLR of the first path and the second path is less than the decision threshold. The method of the present application further introduces parameters with a high correlation with the accuracy of path identification, such as the building density and height corresponding to the location of the terminal, when identifying the path, thereby improving the accuracy of real-time path identification and making the path used to send the first signal more accurate, thereby improving the detection accuracy of the sensing target and the accuracy of positioning the sensing target. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 A schematic diagram of the architecture of a communication system provided in an embodiment of the present application;

[0039] Figure 2 A flowchart of a signal processing method provided in an embodiment of the present application;

[0040] Figure 3 A flowchart of another signal processing method provided in an embodiment of the present application;

[0041] Figure 4 A flowchart of another signal processing method provided in an embodiment of the present application;

[0042] Figure 5 A flowchart of another signal processing method provided in an embodiment of the present application;

[0043] Figure 6 A flowchart of another signal processing method provided in an embodiment of the present application;

[0044] Figure 7 A schematic structural diagram of a signal processing device provided in an embodiment of the present application;

[0045] Figure 8 A schematic structural diagram of another signal processing device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0046] In the embodiments of this application, terms such as "first" and "second" are used to distinguish between identical or similar items with substantially the same functions and effects. For example, the terms "first chip" and "second chip" are used solely to distinguish between different chips and do not define their order. Those skilled in the art will understand that terms such as "first" and "second" do not define the quantity or execution order, and do not necessarily define differences.

[0047] It should be noted that in the embodiments of this application, words such as "exemplary" or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described in this application as "exemplary" or "for example" should not be construed as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0048] In the embodiments of the present application, "at least one" refers to one or more, and "more" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can mean: the existence of A alone, the existence of A and B at the same time, and the existence of B alone, where A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following items" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can mean: a, b, c, ab, a--c, bc, or abc, where a, b, c can be single or plural.

[0049] Figure 1 This is a schematic diagram of the architecture of a communication system provided in an embodiment of the present application. Figure 1 As shown, the communication system 100 may include at least one network device (such as Figure 1110a, 110b, 110c in FIG), and may further include at least one terminal (such as Figure 1 120a-120g in the table).

[0050] The network device and the terminal device can communicate via a wireless link. When the network device acts as a communication transmitter, the terminal device can act as a communication receiver; when the network device acts as a communication receiver, the terminal device can act as a communication transmitter. The embodiment of the present application does not limit the number of network devices and terminal devices included in the communication system. In addition, it should be understood that Figure 1 This is just a schematic diagram. The communication system may also include other network devices, such as wireless relay devices and wireless backhaul devices, etc. This application does not limit this. Figure 1 Not drawn in the middle.

[0051] The network device provided in the embodiments of the present application may be a device that communicates with a terminal device. The network device may also be referred to as an access network device or a wireless access network device, and may be, for example, a base station, a Node B, an evolved Node B (eNodeB or eNB), a transmission reception point (TRP), a next generation Node B (gNB) in a fifth generation (5G) mobile communication system, an access network device in an open radio access network (O-RAN or open RAN), a base station in a future mobile communication system, and the network device may be a satellite base station in a non-terrestrial network (NTN), a base station in a future mobile communication system, or an access node in a wireless fidelity (WiFi) system. Alternatively, the network device may be a module or unit that performs part of the functions of a base station, for example, a centralized unit (CU), a distributed unit (DU), a centralized unit control plane (CU-CP) module, or a centralized unit user plane (CU-UP) module. The access network equipment can be a satellite base station (such as Figure 1 110a in ), or a macro base station (such as Figure 1 110b in the figure), the access network device can also be a micro base station or an indoor station (such as Figure 1110c in the figure), or a relay node or a donor node. This application does not limit the specific technology and device form used by the access network equipment. The 5G system can also be referred to as the new radio (NR) system.

[0052] The network in which the network device resides has strong computing capabilities. This computing capability can be provided by computing nodes included in the network or possessed by the network device itself. When this computing capability can be provided by computing nodes included in the network, the network device can connect to one or more computing nodes in the network and distribute task data received from terminal devices to the computing nodes so that the computing nodes process the task data. Examples of such computing nodes include edge computing servers (MECs), distributed cloud nodes, quantum computing nodes, and computing hosts. Within a computing node, one or more computing units can be included to enable concurrent processing of task data. Examples of such computing units include central processing units (CPUs) and graphics processing units (GPUs).

[0053] In one network structure, the network device may include a centralized unit (CU) node, a distributed unit (DU) node, a RAN device including a CU node and a DU node, or a RAN device including a control plane CU node (CU-CP node), a user plane CU node (CU-UP node), and a DU node.

[0054] Network equipment provides services for cells, and terminal devices communicate with the cells through transmission resources (for example, frequency domain resources, or spectrum resources) allocated by the network equipment. The cell can belong to a macro base station (for example, macro eNB or macro gNB) or a base station corresponding to a small cell. Small cells here can include: metrocells, microcells, picocells, femtocells, etc. These small cells have the characteristics of small coverage and low transmission power, and are suitable for providing high-speed data transmission services.

[0055] Alternatively, the aforementioned device and computing node that communicate with the terminal device can be regarded as a whole as the network device involved in this application.

[0056] The terminal device in the embodiment of the present application may also be referred to as: user equipment (UE), mobile station (MS), mobile terminal (MT), access terminal, user unit, user station, mobile station, mobile station, remote station, remote terminal, mobile device, user terminal, terminal, wireless communication device, user agent or user device, etc. The terminal can be widely used in various scenarios for communication. The scenario includes, but is not limited to, at least one of the following scenarios: enhanced mobile broadband (eMBB), ultra-reliable low-latency communication (URLLC), massive machine-type communications (mMTC), device-to-device (D2D), vehicle to everything (V2X), machine-type communication (MTC), Internet of Things (IOT), virtual reality, augmented reality, industrial control, autonomous driving, telemedicine, smart grid, smart furniture, smart office, smart wear, smart transportation, or smart city, etc. The terminal may be a mobile phone (such as Figure 1 Mobile phones 120a, 120d, 120f), tablet computers, computers with wireless transceiver functions (such as Figure 1 Computers (120g), wearable devices, vehicles (such as Figure 1 120b shown), UAV (Unmanned Aerial Vehicle), helicopter, airplane (such as Figure 1 120c in ), ships, robots, robotic arms, or smart home devices (such as Figure 1 The present application does not limit the specific technology and specific device form adopted by the terminal.

[0057] By way of example and not limitation, in this application, a terminal device may be a terminal device in an XR system. XR technology, a key area for future human-computer interaction and digital content presentation, integrates cutting-edge technologies such as VR, AR, and Mixed Reality (MR). Its primary technical feature is seamlessly connecting the digital and physical worlds through a highly immersive experience, enabling deep user interaction with virtual environments and real-world scenes. For example, the terminal device in the embodiments of this application may be an XR device. XR devices are a type of intelligent terminal designed specifically for immersive experiences. By integrating display, sensing, computing, and communication technologies, they overlay virtual content or augmented information onto the user's field of view or construct a completely virtual interactive space. XR devices include, but are not limited to, head-mounted displays, smart glasses, handheld interactive devices, and holographic projection devices. XR devices widely support cloud-based interaction, accessing high-precision models, dynamic scene data, or artificial intelligence (AI) inference services in real time over the network, thereby overcoming local computing power limitations and promoting the implementation of complex applications such as the metaverse and remote collaboration.

[0058] Currently, signal propagation paths can be divided into LOS and NLOS paths based on their characteristics. In LOS paths, signal propagation is direct, free of significant obstructions, and signal loss during propagation is relatively low. This low loss enables LOS environments to support high data rate transmission, meeting the demands of bandwidth-demanding applications such as high-definition video streaming and real-time data interaction. In contrast, in NLOS paths, various obstacles exist along the signal propagation path, forcing the signal to undergo complex processes such as reflection, refraction, and scattering before reaching the receiver. This complex propagation process significantly increases signal attenuation and introduces significant latency and multipath effects. Multipath effects cause the receiver to receive multiple signal copies with varying delays and amplitudes, resulting in signal distortion and interference, seriously impacting communication quality.

[0059] In telemetry-based scenarios, accurately identifying the current path (LOS or NLOS) is crucial. Network equipment can dynamically adjust signal transmission parameters, such as power control, modulation, and coding strategies, based on path identification. By properly adjusting these parameters, communication performance can be optimized, signal transmission reliability and efficiency can be improved, and bit error rates can be reduced.

[0060] In practical positioning and navigation applications, the accuracy of path identification significantly impacts positioning precision. Loss of sight (LOS) paths typically provide more accurate distance and angle information, which is essential for precisely calculating the target's position. However, the complexity of signal propagation in non-losable (NLOS) paths can introduce significant errors, leading to inaccurate positioning results. Therefore, by effectively identifying LOS / NLOS paths, network devices can select positioning algorithms that correspond to the actual path and eliminate signal paths with significant positioning errors, significantly improving positioning accuracy and meeting the requirements of applications such as intelligent transportation and drone navigation, which require extremely high positioning precision.

[0061] The inventors have discovered that some terminals (such as UAVs) currently have high mobility and their positions change frequently, resulting in frequent switching of signal propagation paths between LOS and NLOS. The current accuracy of identifying LOS and NLOS is low, resulting in low terminal detection accuracy at the perception level. This makes it impossible to perceive the terminal in a timely manner in complex environments, which also leads to low accuracy in terminal positioning. On the other hand, at the communication level, low LOS / NLOS recognition accuracy also has a certain impact on beamforming and path optimization. For example, low LOS / NLOS recognition accuracy leads to poor accuracy in beam direction adjustment and propagation path selection, which in turn leads to low link quality, more signal interruptions and fading, and poor communication stability and reliability.

[0062] In view of this, the present application provides a signal processing method, which receives first information sent by a sensing target for determining a decision threshold for path identification and is related to the building density and / or height at the location of the sensing target, and based on the first information and the comparison result of the log-likelihood ratio of the first path and the second path, determines whether the path adopted by the first signal for sending a communication sensing function is the first path or the second path. Through the method of the present application, when identifying the path, parameters with a high correlation with the accuracy of path identification, such as the building density and height corresponding to the location of the terminal, are further introduced, thereby improving the accuracy of real-time path identification, making the accuracy of the path adopted for sending the first signal higher, thereby improving the detection accuracy of the sensing target and the accuracy of positioning the sensing target, and also improving the accuracy of beam direction adjustment and propagation path selection during communication, thereby improving the stability of communication.

[0063] The signal processing method of the present application is described in detail below with reference to the accompanying drawings. The execution subject of the embodiments shown in this application is a network device. The network device in the embodiments of this application can be the network device itself, or it can be a chip, chip system or processor that supports the network device to implement the task processing method, or it can be a logic module or software that can implement all or part of the network device functions. This application does not impose specific restrictions on this.

[0064] Figure 2 A flow chart of a signal processing method provided in an embodiment of the present application. Figure 2 As shown, the method may include:

[0065] S201: Receive first information sent by a perception target.

[0066] The first information is used to indicate a decision threshold, which is related to the building density and / or height at the location of the perception target. The perception target involved in this application is a perception target in an outdoor scene, that is, the terminal mentioned above, such as a UAV or other highly maneuverable terminals such as vehicles, etc. This application does not impose any restrictions on this.

[0067] When the perceived target is a UAV or other flying terminal, the decision threshold can be related to the building density at the terminal's location, the terminal's altitude, or both. When the perceived target is a non-flying terminal such as a vehicle, the decision threshold can be related to the building density at the terminal's location. A higher building density indicates a greater probability of being in NLOS, while a higher altitude indicates a greater probability of being in LOS.

[0068] The decision threshold is used to determine whether the path used to transmit the first signal is the first path or the second path. The first path can be, for example, LOS or NLOS. When the first path is LOS, the second path is NLOS. When the first path is NLOS, the second path is LOS. Optionally, in addition to LOS and NLOS, the path used to transmit the first signal can also be a low-attenuation path, a high-attenuation path, or other path that affects the transmission parameters of the first signal. This application does not impose any restrictions on this. Subsequent embodiments will be described using the example of the first path being LOS and the second path being NLOS. That is, the positioning error of the second path (NLOS) is greater than that of the first path (LOS).

[0069] Specifically, the log-likelihood ratio (LLR) of the first and second paths can be calculated and compared with a decision threshold. If the LLR is greater than or equal to the decision threshold, it indicates that the perceived target is likely on the first path, and step S202 is executed. If the LLR is less than the decision threshold, it indicates that the perceived target is likely on the second path, and step S203 is executed.

[0070] S202: Send a first signal using a first path.

[0071] The first signal is a signal sent by a network device for both communication and perception. For example, it can communicate with a perception target (for example, the first signal can carry user data to enable information exchange between the network device and a perception target, such as a UAV or vehicle, or the first signal can include control instructions to guide the movement, operation, and other behaviors of the perception target). It can also be used to perceive the position and shape of a perception target that the network device needs to perceive (for example, the shape of a perception target, such as a UAV or vehicle, can be perceived by analyzing the propagation characteristics of the first signal, such as its arrival time, arrival angle, and signal strength). For example, the first signal can be a baseband signal, a radio frequency signal, a millimeter wave signal, a radar signal, or the like.

[0072] Specifically, using the first path to transmit the first signal refers to using transmission parameters corresponding to the first path to transmit the first signal. Taking LOS as an example, the transmission parameters may include one or more of transmit power, frequency, modulation mode, antenna gain, beamforming parameters, coding mode, and signal bandwidth. For example, if the transmission parameters corresponding to LOS are used to transmit the first signal, since the LOS path has low signal attenuation, the transmit power can be appropriately reduced to save energy; a high-frequency signal frequency can be selected to provide high bandwidth and resolution; the LOS path has a high signal-to-noise ratio, so high-order modulation can be selected to increase the data transmission rate; a high-gain directional antenna can be used to enhance the signal's directional propagation capability; precise beamforming can be used to concentrate signal energy in the target direction; the LOS path has a high signal-to-noise ratio, so efficient error correction coding can be used to improve data transmission efficiency; and the LOS path is suitable for broadband signals, providing a high data transmission rate.

[0073] S203: Send the first signal using the second path.

[0074] Specifically, using the second path to send the first signal means using the sending parameters corresponding to the second path to send the first signal. Taking the second path as NLOS as an example, the sending parameters may include one or more of the following: transmit power, frequency, modulation mode, antenna gain, beamforming parameters, coding mode, signal bandwidth, etc. For example, if the sending parameters corresponding to NLOS are used to send the first signal, since the signal attenuation of the NLOS path is large, the transmit power can be increased to compensate for the attenuation; the signal frequency in the low frequency band can be selected to improve signal penetration; the NLOS path has a low signal-to-noise ratio, and low-order modulation can be used to ensure signal reliability; the NLOS path needs to consider a wider coverage range, and omnidirectional or low-gain antennas can be used; the NLOS path can use wide beams or adaptive beamforming to cope with multipath effects; the NLOS path requires stronger error correction capabilities, and basic error correction coding can be used; the NLOS path has large signal attenuation, and using narrowband signals makes it easier to penetrate obstacles, etc.

[0075] The method provided in the embodiment of the present application receives first information sent by a sensing target to indicate a decision threshold related to the building density and / or height at the location of the sensing target. When the LLR between the first path and the second path is greater than or equal to the decision threshold, the first signal is sent using the first path; and when the LLR between the first path and the second path is less than the decision threshold, the first signal is sent using the second path. The method of the present application further introduces parameters with a high correlation with the accuracy of path identification, such as the building density and height corresponding to the location of the terminal, when identifying the path, thereby improving the accuracy of real-time path identification and making the path used to send the first signal more accurate, thereby improving the detection accuracy of the sensing target and the accuracy of positioning the sensing target.

[0076] Optionally, if the decision threshold is only related to the building density, the first information includes a first parameter for determining the building density at the location of the perception target. The first parameter may be, for example, the building density, or may be a parameter for determining the building density (for example, it may be the positioning position of the perception target). Taking the example where the first parameter includes the positioning position of the perception target, the network device may receive the positioning position of the perception target sent by the perception target, and analyze the number of buildings in the area where the perception target is located based on its positioning position to determine the occlusion situation (for example, when there are tall buildings in the city center, the probability of being in NLOS is higher; while in low-density or open areas, the probability of being in LOS will increase). Specifically, the number of buildings in the area around the positioning position can be obtained through an open source map website to calculate the building density. The specific method for calculating the building density can refer to the existing technology and will not be elaborated here.

[0077] If the decision threshold is only related to altitude, the first information includes a second parameter used to determine the altitude of the perceived target. This second parameter can be, for example, altitude, or a parameter used to determine altitude (e.g., the perceived target's location, air pressure sensor data, inertial measurement unit data, etc.). When the perceived target's altitude is low, the probability of it being in NLOS is higher due to the greater influence of ground obstacles. When the perceived target's altitude is high, the influence of ground obstacles is less, and the probability of it being in LOS is higher.

[0078] If the decision threshold is related to the building density and height, the first information includes the first parameter and the second parameter.

[0079] Based on the above embodiments, the following specific implementation methods for determining the decision threshold in different situations are introduced in detail.

[0080] Implementation method 1: The decision threshold is related to the building density.

[0081] Figure 3 This is a flow chart of another signal processing method provided in an embodiment of the present application. Figure 3 As shown, the method may include:

[0082] S301: Obtain a first priori probability function corresponding to a first path.

[0083] The first prior probability function is related to the building density reference value and the building density attenuation factor. Taking the first path as LOS as an example, the first prior probability function corresponding to LOS can be obtained based on the analysis of building densities in cities or regions with different densities. For example, the first prior probability function can be determined as shown in the following formula (1) based on the actual situation analysis:

[0084] (1)

[0085] in, is the prior probability of the first path, is the first parameter (which may be building density, for example), is the building density attenuation factor, is the reference value for building density.

[0086] For example, assuming that according to actual analysis, the building density in high-density cities is about 800 buildings / km2, and the building density in low-density cities is about 200 buildings / km2, based on this analysis, the reference value of building density can be determined according to the actual situation. and building density attenuation factor For example, based on actual analysis, the reference value of building density is determined to be 300 buildings / km2 (i.e. ), the building density attenuation factor is 0.005 (i.e. In this example, the values of the first parameter and the first path prior probability corresponding to the first prior probability function can be referred to as shown in the following Table 1:

[0087] Table 1

[0088]

[0089] Among them, the first prior probability function can be pre-built and stored in the network device, or it can be sent to the network device by other devices (such as core network devices), or it can be pre-defined by the protocol, etc. This application does not impose any restrictions on this.

[0090] It should be understood that the above is only an exemplary first priori probability function provided for ease of understanding. In actual scenarios, the corresponding first priori probability function can be determined according to actual needs, and is not limited to the above first priori probability function.

[0091] S302: Generate a first path priori probability according to the first priori probability function and the first parameter.

[0092] In this step, based on the first prior probability function provided in step S301 above, the first parameter is substituted into the first prior probability function to calculate the first path prior probability. Alternatively, the first path prior probability corresponding to the first parameter can be obtained based on a mapping relationship between the first parameter and the first path prior probability. This mapping relationship is the mapping relationship represented by the first prior probability function described in step S301 above.

[0093] S303: Obtain a second path priori probability according to the first path priori probability.

[0094] Since one of the first and second paths is the target hypothesis and the other is the alternative hypothesis, that is, when the first path is the target hypothesis, the second path is the alternative hypothesis; when the second path is the target hypothesis, the first path is the alternative hypothesis. Therefore, to facilitate calculation, the prior probability of the first path can be set to be negatively correlated with the prior probability of the second path. For example, the sum of the prior probability of the first path and the prior probability of the second path can be set to 1.

[0095] After obtaining the first path prior probability in step S302, the second path prior probability can be calculated by calculating the correlation between the first and second path prior probabilities. For example, if the sum of the first and second path prior probabilities is 1, then if the first path prior probability is 0.5, then the second path prior probability is 1-0.5 = 0.5.

[0096] Optionally, you can also refer to the construction method of the first prior probability function, determine the prior probability function corresponding to the second path prior probability based on the actual scenario analysis, and then calculate the second path prior probability by substituting the first parameter into the prior probability function. This will not be repeated here.

[0097] S304: Determine a decision threshold according to the first path prior probability and the second path prior probability.

[0098] In this step, according to Bayesian theory, in order to make the error rate of path identification meet the actual needs, the optimal decision threshold is in accordance with the following formula (2):

[0099] (2)

[0100] in, is the decision threshold, is the first path prior probability (LOS prior probability), is the prior probability of the second path (NLOS prior probability). is the probability of occurrence of LOS path in real scenarios, is the probability of occurrence of NLOS path in real scenarios.

[0101] Implementation method 2: The decision threshold is related to the height.

[0102] Figure 4 A flow chart of another signal processing method provided in an embodiment of the present application. Figure 4 As shown, the method may include:

[0103] S401: Obtain a second priori probability function corresponding to the first path.

[0104] The second prior probability function is related to the altitude reference value and the altitude attenuation factor. Taking LOS as an example, the second prior probability function corresponding to LOS can be obtained based on the analysis of the flight terminal at different altitudes. For example, the second prior probability function can be determined as shown in the following formula (3) based on the actual situation analysis:

[0105] (3)

[0106] in, is the prior probability of the first path, is the second parameter (e.g. the height of the perceived target), is the height attenuation factor, is the height reference value.

[0107] For example, assuming that according to actual analysis, the height of urban buildings generally does not exceed 200 meters, when the height of the perceived target is 80 meters, the prior probability of the first path and the prior probability of the second path are both close to 0.5, that is, the probabilities of being in LOS and NLOS are close. Therefore, based on this analysis, the height reference value can be determined according to the actual situation. and height attenuation factor For example, according to actual analysis, the reference value of height is determined to be 80 meters (i.e. ), the height attenuation factor is 0.01 (i.e. In this example, the values of the second parameter corresponding to the second prior probability function and the first path prior probability can be referred to as shown in the following Table 2:

[0108] Table 2

[0109]

[0110] Among them, the second prior probability function can be pre-built and stored in the network device, or it can be sent to the network device by other devices (such as core network devices), or it can be pre-defined by the protocol, etc. This application does not impose any restrictions on this.

[0111] It should be understood that the above is only an exemplary second priori probability function provided for ease of understanding. In actual scenarios, the corresponding second priori probability function can be determined according to actual needs, and is not limited to the above second priori probability function.

[0112] S402: Generate a first path priori probability according to the second priori probability function and the second parameter.

[0113] In this step, based on the second prior probability function provided in step S401 above, the second parameter is substituted into the second prior probability function to calculate the first path prior probability. Alternatively, the first path prior probability corresponding to the second parameter can be obtained based on a mapping relationship between the second parameter and the first path prior probability. This mapping relationship is the mapping relationship represented by the second prior probability function mentioned in step S401 above.

[0114] S403: Obtain a second path priori probability according to the first path priori probability.

[0115] Since one of the first and second paths is the target hypothesis and the other is the alternative hypothesis, that is, when the first path is the target hypothesis, the second path is the alternative hypothesis; when the second path is the target hypothesis, the first path is the alternative hypothesis. Therefore, to facilitate calculation, the prior probability of the first path can be set to be negatively correlated with the prior probability of the second path. For example, the sum of the prior probability of the first path and the prior probability of the second path can be set to 1.

[0116] After obtaining the first path prior probability in step S402, the second path prior probability can be calculated by calculating the correlation between the first and second path prior probabilities. For example, if the sum of the first and second path prior probabilities is 1, then if the first path prior probability is 0.5, then the second path prior probability is 1-0.5 = 0.5.

[0117] Optionally, you can also refer to the construction method of the second prior probability function, determine the prior probability function corresponding to the second path prior probability based on the actual scenario analysis, and then calculate the second path prior probability by substituting the second parameter into the prior probability function. This will not be repeated here.

[0118] S404: Determine a decision threshold according to the first path prior probability and the second path prior probability.

[0119] The implementation of this step is the same as that of the aforementioned step S304 and will not be repeated here.

[0120] Implementation method 3: The decision threshold is related to building density and height.

[0121] Figure 5A flow chart of another signal processing method provided in an embodiment of the present application. Figure 5 As shown, the method may include:

[0122] S501: Obtain a first a priori probability function, a second a priori probability function, a first weight of the first a priori probability function, and a second weight of the second a priori probability function corresponding to a first path.

[0123] Among them, the first prior probability function is the aforementioned Figure 3 The first prior probability function mentioned in the above, the second prior probability function is Figure 4 The second prior probability function mentioned in . Since the influence of the building density and height of the sensing target location on the decision threshold needs to be considered simultaneously in this implementation, the mapping relationship between the first parameter and the second parameter and the prior probability of the first path can be determined by weighted fusion of the first prior probability function and the second prior probability function corresponding to the first path.

[0124] Optionally, the first weight and the second weight can both be set to 0.5, or can be adaptively adjusted according to the location of the perception target. For example, when the perception target is in an area with a high building density such as a high-density city or a city's core commercial area, the building density is the main factor for identifying LOS / NLOS, and the first weight can be set to a higher value and the second weight can be set to a lower value, for example, the first weight can be set to 0.8 and the second weight can be set to 0.2; when the perception target is in an area with a low building density such as a low-density city or an open area, the height is the main factor for identifying LOS / NLOS, and the second weight can be set to a higher value and the first weight can be set to a lower value, for example, the second weight can be set to 0.8 and the first weight can be set to 0.2.

[0125] Optionally, any one of the first a priori probability function, the second a priori probability function, the first weight, and the second weight may be pre-built and stored in the network device, or may be issued to the network device by another device (e.g., a core network device), or may be pre-defined by a protocol, etc. This application does not impose any restrictions on this. That is, for each of the first a priori probability function, the second a priori probability function, the first weight, and the second weight, they may be obtained in the same manner, or at least two of them may be obtained in different manners.

[0126] S502: Generate a first path priori probability according to the first priori probability function, the second priori probability function, the first weight, the second weight, the first parameter, and the second parameter.

[0127] Taking the aforementioned first prior probability function and second prior probability function as an example, the mapping relationship between the first path prior probability and the first parameter and the second parameter can be shown as the following formula (4):

[0128] (4)

[0129] in, is the prior probability of the first path, is the first weight, is the second weight, is the first parameter, is the second parameter.

[0130] In this step, the first weight, the second weight, the first parameter, and the second parameter are substituted into the above formula (4) to calculate the first path prior probability (i.e., the LOS prior probability).

[0131] S503: Obtain a second path priori probability according to the first path priori probability.

[0132] In this step, the method of obtaining the second path prior probability is the same as the method of obtaining the second path prior probability according to the first path prior probability in the aforementioned steps S303 and S403, and will not be repeated here.

[0133] S504: Determine a decision threshold according to the first path prior probability and the second path prior probability.

[0134] The implementation of this step is the same as that of the aforementioned steps S304 and S404, and will not be repeated here.

[0135] The three methods for generating decision thresholds provided in the embodiments of the present application are to obtain the first path prior probability by combining the mapping relationship between the first parameter and / or the second parameter determined according to the actual situation and the first path prior probability with the first parameter and / or the second parameter extracted from the first information, and to obtain the second path prior probability based on the first path prior probability, so as to determine the decision threshold based on the first path prior probability and the second path prior probability. This method further introduces parameters with a high correlation with the accuracy of path identification, such as the building density and height corresponding to the location of the terminal, when identifying the path. According to the location of the perceived target, the corresponding decision threshold is determined dynamically in real time, so that the decision threshold is dynamically correlated with the current state of the perceived target, thereby improving the accuracy of real-time path identification, making the accuracy of the path used to send the first signal higher, thereby improving the detection accuracy of the perceived target and the accuracy of positioning the perceived target.

[0136] In one possible implementation, when dynamically determining the decision threshold, a time window related to the communication environment in which the perception target is located can be introduced to further improve the accuracy of the decision threshold and reduce misjudgment. The time window related to the communication environment in which the perception target is located can also be aligned with the retransmission rhythm of the physical layer in the communication system to improve the adaptability of the dynamically determined decision threshold to the communication system, thereby optimizing the judgment process of the decision threshold and improving the accuracy and applicability of path identification.

[0137] In this implementation, the decision threshold may be related to the first parameter, the second parameter, or both. For example, if the decision threshold is related to both the first and second parameters, the first parameter may be related to the densities of multiple buildings within a target time window, and the second parameter may be related to the heights of multiple buildings within the target time window.

[0138] The target time window may correspond to the communication environment of the sensing target, for example, the cell in which the sensing target is currently located, the movement speed of the sensing target, the signal transmission delay of the communication environment in which the sensing target is located, etc. For example, the length of the target time window belongs to a target length interval, and the target length interval may be determined according to the communication environment in which the sensing target is located and based on actual judgment requirements.

[0139] Exemplarily, the lower limit of the target length interval may be greater than or equal to a first value, and the first value may be greater than or equal to the signal transmission delay of the perception target. The signal transmission delay of the perception target refers to the physical layer delay from the sending of a signal data packet to the receipt of the ACK / NACK feedback corresponding to the signal data packet when data is transmitted between the network device and the perception target. The signal transmission delay characterizes that a complete HARQ cycle (for example, including the sending, receiving, feedback and possible retransmission process of the data packet) must be covered within the target time window. If the target length interval is set too short, for example, so short that it cannot cover a complete HARQ cycle, it may cause the signal data packet to be lost or retransmitted in time, thereby reducing the reliability of communication. Therefore, by setting the lower limit of the target length interval to be greater than or equal to the first value, it can ensure that the target length interval can cover a complete HARQ cycle, thereby improving the stability of communication, thereby further improving the stability and accuracy of determining the decision threshold.

[0140] Optionally, the upper limit of the target length interval may be less than or equal to a second value, where the second value is related to the target's speed and the effective radius of the cell in which the target resides. For example, the second value may be calculated based on the target's speed and the effective radius of the cell in which the target resides, such as by dividing the effective radius of the cell in which the target resides by the target's speed (i.e., the second value is equal to the quotient of the effective radius and the speed), or by dividing the effective radius of the cell in which the target resides by the target's speed. The second value indicates that the target should not leave its current cell within a target time window. If the target leaves its current cell within a target time window, communication interruption or frequent cell handovers may result, increasing communication complexity and instability and reducing the stability and accuracy of the decision threshold. By setting the upper limit of the target time window to the point where the target should not leave its current cell within the target time window, the complexity and instability caused by communication interruption or cell handovers are reduced, thereby further improving the stability and accuracy of the decision threshold.

[0141] In the target time window, multiple building densities and multiple heights of the perceived target can be obtained from the perceived target, and then the first parameter can be determined based on the multiple building densities, and the second parameter can be determined based on the multiple heights. For example, the multiple building densities within the target time window can be fused and calculated, and the fusion calculation result can be used as the first parameter. The multiple heights within the target time window can be fused and calculated, and the fusion calculation result can be used as the second parameter. The fusion calculation can, for example, be the calculation of the mean, median, etc. of multiple data, and this application does not impose any restrictions on this. In addition, the fusion calculation methods used for the first parameter and the second parameter can be the same or different.

[0142] After determining the first parameter and the second parameter corresponding to the target time window, the Figure 5 In the manner shown, a first path prior probability related to the first parameter and the second parameter is obtained, and a corresponding second path prior probability is obtained based on the first path prior probability. Then, a decision threshold is determined based on the first path prior probability and the second path prior probability. The decision threshold can be used for path identification of the first signal sent in a subsequent time window, for example, it can be used for path identification of the first signal sent in the next time window.

[0143] Next, how to determine the LLRs of the first path and the second path mentioned in the above embodiment is described in detail.

[0144] Figure 6 A flow chart of another signal processing method provided in an embodiment of the present application. Figure 6 As shown, the method may further include:

[0145] S601: Obtain observation parameters of a sample signal.

[0146] The sample signals include the sample signals under the first path and the sample signals under the second path. In the case where the first path is LOS and the second path is NLOS, the sample signals include the sample signals under LOS and the sample signals under NLOS.

[0147] The observation parameters of the sample signal include one or more of skewness, kurtosis, peak-to-average ratio, angle of arrival skewness, angle of arrival variance, and time delay. The sample signal and the observation parameters of the sample signal may be pre-collected and obtained, and may be obtained from a database or storage device storing the observation parameters of the sample signal.

[0148] Skewness refers to the degree of asymmetry in the sample signal amplitude distribution. In LOS (Loss of Sight) paths, signal transmission is relatively direct, with a distinct main path. The signal amplitude is relatively concentrated around this main path, resulting in less asymmetry and, therefore, a smaller skewness. In NLOS (Non-Low-Sight) paths, however, the signal reaches the receiver via multiple paths, including reflection and scattering. The signal amplitudes along these paths vary significantly, leading to a more dispersed sample signal amplitude distribution, increased asymmetry, and a relatively higher skewness.

[0149] Kurtosis reflects the degree of kurtosis in the amplitude distribution of the sample signal. In LOS paths, due to the presence of the main path, the signal amplitude is concentrated around a specific value, resulting in a sharp peak and a heavy tail, a characteristic known as high kurtosis. This indicates that some components in the signal have strong amplitudes significantly higher than others. In NLOS paths, multipath effects make the signal amplitude distribution more uniform, with no noticeable peaks, a relatively flat distribution, and a lower kurtosis value.

[0150] The peak-to-average ratio (CPAR) is the ratio of the peak amplitude of a sample signal to its average amplitude. It reflects the fluctuations in the instantaneous power of the signal. In LOS paths, the presence of significant strong paths can cause large peaks at certain times, while the average amplitude is relatively small, resulting in a high PAR. In NLOS paths, due to the superposition of multipath, the signal power is more evenly distributed, with a small difference between the peak and average amplitudes, resulting in a low PAR.

[0151] The Angle of Arrival (AoA) is the angle between the signal propagation direction and the reference axis of the receiving antenna array at the receiving end (e.g., the sensing target in this application). The AoA deviation measures the asymmetry of the sample signal's AoA distribution. In LOS paths, signals propagate primarily in a relatively fixed direction. When they reach the receiving antenna array, the AoA is concentrated near a specific angle, resulting in a relatively concentrated distribution and low asymmetry, leading to a small AoA deviation. In NLOS paths, however, due to multipath effects, signals arrive at the receiving antenna array from multiple different directions, resulting in a more dispersed AoA distribution, high asymmetry, and a large AoA deviation.

[0152] The angle of arrival variance measures the dispersion of the sample signal's angle of arrival distribution. In LOS paths, signal arrival angles are concentrated within a relatively small angular range, with individual angles of arrival differing slightly from the average angle of arrival, resulting in a smaller angle of arrival variance. In contrast, in NLOS paths, multipath effects cause the signal arrival angles to spread across multiple angles, resulting in larger differences between the angles of arrival and the average angle of arrival, leading to a larger angle of arrival variance. A larger angle of arrival variance indicates a greater dispersion of the signal's arrival direction.

[0153] Time delay reflects the time it takes for a sample signal to propagate from the transmitter (e.g., the network device in this application) to the receiver (e.g., the sensing target in this application). In a LOS path, the signal travels directly from the transmitter to the receiver, resulting in a shorter propagation path and a smaller time delay. In an NLOS path, however, the signal undergoes reflections and scattering, resulting in a longer propagation path and a larger time delay.

[0154] For example, the calculation method of skewness, kurtosis, and peak-to-average ratio can refer to the following formulas (5) to (7):

[0155] (5)

[0156] (6)

[0157] (7)

[0158] Among them, formula (5) is the calculation formula for skewness, formula (6) is the calculation formula for kurtosis, and formula (7) is the calculation formula for peak-to-average ratio. is the amplitude value of the sample signal, is the mean amplitude of the sample signal, is the amplitude standard deviation of the sample signal, and N is the number of sample signals.

[0159] For the arrival angle variance, the arrival angle can be extracted by using the Fast Fourier Transform (FFT) and then peak search method when the receiving end (i.e., the sensing target) is a uniform linear array. , according to the extracted arrival angle The arrival angle variance is calculated. The calculation method can refer to the existing technology and will not be described here. The skewness of the distribution is calculated. Since the angle value is generally discrete, it is assumed that the arrival angle The value of ,in Characterization The arrival angle of the sample signal is taken. Then according to Calculate the mean , and then the arrival angle deviation is obtained based on the following formula (8):

[0160] (8)

[0161] The time delay can be obtained based on the following two methods:

[0162] Method 1: Use correlation analysis of the auxiliary signal to extract the time delay (also known as the delay difference).

[0163] In this method, there is no restriction on the type of sample signal, that is, the sample signal can be a cyclostationary signal or a non-cyclostationary signal. The auxiliary signal is a known signal (for example, a transmission signal) sent by the transmitter (which can be a network device or a sensing target). ), the transmission signal can be, for example, a pseudo-random sequence signal or a pulse signal. The auxiliary signal received is recorded at the receiving end (for example, a received signal ). By calculating the received signal Sending a signal The cross-correlation function of is used to obtain the time delay of the sample signal.

[0164] The cross-correlation function is shown in the following formula (9):

[0165] (9)

[0166] By determining the cross-correlation function shown in the above formula (9) The maximum value corresponding to , the time delay can be determined as .

[0167] Method 2: Obtain the time delay by calculating the cyclic autocorrelation function of the sample signal.

[0168] This method is used when the sample signal is a cyclostationary signal (for example, a linear frequency modulation signal). , its cyclic autocorrelation function is shown in the following formula (10):

[0169] (10)

[0170] in, is the time delay, is the cycle frequency of the sample signal, is the length of the time interval, is an imaginary unit. Under this condition, the cyclic autocorrelation function of the sample signal will have a significant peak, and the time delay corresponding to the peak position is the time delay of the sample signal.

[0171] The above two methods for calculating the time delay of the sample signal are only briefly introduced here. For details, please refer to the existing technology and will not be further described here.

[0172] S602: Based on the observation parameters of the sample signal, generate at least two candidate Gaussian mixture models by fitting.

[0173] In this step, it is assumed that the distribution of the observation parameters of the sample signal conforms to a Gaussian mixture model (GMM) composed of a weighted linear combination of multiple Gaussian distributions. Based on the observation parameters of the sample signal, Gaussian mixture models composed of different numbers of Gaussian distributions, as well as the parameters of each Gaussian distribution in each Gaussian mixture model (e.g., the mean, variance, and weight of each Gaussian distribution), are estimated to fit the observation parameters of the sample signal. At least two candidate Gaussian mixture models corresponding to the observation parameters of the sample signal are obtained, where the at least two candidate Gaussian mixture models include a different number of Gaussian distributions.

[0174] The Gaussian mixture model fitting formula is shown in the following formula (11):

[0175] (11)

[0176] in, is the probability density of the sample point corresponding to the sample signal, is each sample signal (i.e., the observation parameter of the sample signal), is the Gaussian mixture model Gaussian distribution weights, is the Gaussian mixture model The mean of a Gaussian distribution, is the Gaussian mixture model The standard deviation of a Gaussian distribution.

[0177] S603 : Determine a target Gaussian mixture model from the candidate Gaussian mixture models based on the Bayesian Information Criterion.

[0178] The Bayesian Information Criterion (BIC) is a model selection criterion used to balance model complexity and goodness of fit when fitting statistical models. Based on the BIC, the optimal number of Gaussian components in a Gaussian mixture model can be determined.

[0179] In this step, the BIC value The calculation formula can refer to the following formula (12):

[0180] (12)

[0181] in, is the maximum likelihood estimate of the candidate Gaussian mixture model, indicating the parameters of the candidate Gaussian mixture model The likelihood value of the observation parameter of the sample signal, parameter Refers to the weights of the Gaussian distributions included in the candidate Gaussian mixture model , mean , standard deviation . is the number of parameters in the candidate Gaussian mixture model, for example, the candidate Gaussian mixture model includes Gaussian distribution, then the parameters of the candidate Gaussian mixture model include indivual 、 indivual 、 indivual ,Right now . is the number of sample signals.

[0182] In formula (12), It is used to measure the goodness of fit of the candidate Gaussian mixture model to the observation parameters of the sample signal. The larger the likelihood value of the candidate Gaussian mixture model, The smaller it is, the better the candidate Gaussian mixture model fits the data (i.e., the observed parameters of the sample signal). It is a penalty term for the complexity of the candidate Gaussian mixture model. The more parameters the candidate Gaussian mixture model has, the larger the penalty value is, so as to prevent overfitting of the candidate Gaussian mixture model.

[0183] Generally, it will first drop to a minimum value as the number of Gaussian distributions included in the candidate Gaussian mixture model increases, and then Then grow. In this process, select the smallest The candidate Gaussian mixture model corresponding to the corresponding number of Gaussian distributions is used as the target Gaussian mixture model, thereby reducing overfitting while ensuring the fitting effect of the Gaussian mixture model on the observation parameters of the sample signal.

[0184] S604: Generate LLR based on the observation parameters of the second signal and the target Gaussian mixture model.

[0185] Among them, the second signal is a signal before the first signal, for example, it can be the last signal sent before sending the first signal for communicating with the perception target or perceiving the perception target, or it can be the first multiple signals sent before sending the first signal for communicating with the perception target or perceiving the perception target, etc.

[0186] The observation parameters of the second signal include at least one of the following: skewness, kurtosis, peak-to-average ratio, arrival angle skewness, arrival angle variance, and time delay. The observation parameters can refer to the above introduction to the observation parameters of the sample signal and will not be repeated here.

[0187] In this step, taking the first path as LOS, the second path as NLOS, and the observation parameters of the second signal including skewness, kurtosis, peak-to-average ratio, arrival angle skewness, and time delay as an example, the LLR generated based on the observation parameters of the second signal and the target Gaussian mixture model can refer to the following formula (13):

[0188] (13)

[0189] This formula (13) can be converted to formula (14) or formula (15):

[0190] (14)

[0191] (15)

[0192] in, is the LLR of the first and second paths that conform to the target Gaussian mixture model; , which is the prior LLR of the first and second paths that conform to the target Gaussian mixture model; , which is the posterior LLR of the first and second paths that conform to the target Gaussian mixture model; They correspond to the five observation parameters of the second signal, namely skewness, kurtosis, peak-to-average ratio, arrival angle skewness, and time delay.

[0193] The method provided in an embodiment of the present application obtains the observation parameters of the sample signal and, based on the observation parameters of the sample signal, fits and generates at least two candidate Gaussian mixture models. The target Gaussian mixture model is determined from the candidate Gaussian mixture models based on the Bayesian Information Criterion, and LLRs are generated based on the observation parameters and the target Gaussian mixture model based on the second signal. The method selects observation parameters with significantly different characteristics in the LOS path and the NLOS path, fits the target Gaussian mixture model that conforms to the signal distribution, and calculates the LLR for identifying the path where the perceived target is located based on the actual observation parameters of the second signal sent previously, thereby improving the accuracy of path identification.

[0194] Optionally, when the observation parameters of the second signal include angle of arrival deviation or angle of arrival variance, the use of angle of arrival deviation or angle of arrival variance as one of the observation parameters of the second signal can be determined based on the angle of arrival spread of the second signal. That is, the observation parameters of the second signal may include angle of arrival deviation in some cases and angle of arrival variance in other cases. The angle of arrival spread may, for example, be RMS angle of arrival spread.

[0195] In this implementation, when the angle of arrival spread is large, it indicates that the signal propagation process is rich in multipath and there is no significant main path. In this case, the angle of arrival variance changes significantly, which can more clearly assist in the identification of LOS and NLOS. When the angle of arrival spread is small, it indicates that the power and angle of arrival are concentrated on a main path during signal propagation. The angle of arrival shows a cluster of main paths and a few sparse reflections. In this case, the angle of arrival deviation changes more significantly, which can more clearly assist in the identification of LOS and NLOS.

[0196] Therefore, in this implementation, it is possible to determine whether to use the arrival angle deviation or the arrival angle variance in the observation parameters of the second signal by judging the comparison result between the arrival angle spread of the second signal and the preset threshold. For example, when the arrival angle spread of the second signal is greater than or equal to the first preset threshold, the observation parameters include the arrival angle variance; when the arrival angle spread of the second signal is less than the second preset threshold, the observation parameters include the arrival angle deviation. Among them, the second preset threshold is less than or equal to the first preset threshold, that is, the second preset threshold can be the same as the first preset threshold, or it can be any value less than the first preset threshold. The above preset thresholds can be determined according to actual needs, and this application does not impose any restrictions on this.

[0197] For example, taking the arrival angle spread as the RMS arrival angle spread as an example, the calculation formula of the RMS arrival angle spread can refer to the following formula (16):

[0198] (16)

[0199] in, , Indicates the The power of each path, Indicates the The arrival angle of each path, represents the power-weighted mean angle of arrival.

[0200] Exemplarily, assuming that the second preset threshold is equal to the first preset threshold, the first preset threshold can be 15° in the sub-6 GHz urban micro (UMi) scenario; the first preset threshold can be 8° in the millimeter wave small cell scenario.

[0201] Figure 7 This is a schematic diagram of the structure of a signal processing device provided in an embodiment of the present application. It is understandable that the signal processing device can implement the operations or steps of the network device in the aforementioned various method embodiments. The signal processing device can be a network device or a component that can be configured in a network device, such as a chip, a chip module, etc. Figure 7 As shown, the signal processing device may include: a receiving module 11 and a control module 12.

[0202] The receiving module 11 receives first information sent by the sensing target, where the first information is used to indicate a decision threshold. The decision threshold is related to the building density and / or height at the location of the sensing target.

[0203] The control module 12 transmits the first signal via the first path if a log likelihood ratio (LLR) between the first path and the second path is greater than or equal to a decision threshold, and transmits the first signal via the second path if the LLR is less than the decision threshold, and the positioning error of the second path is greater than that of the first path.

[0204] Optionally, the first information includes a first parameter and / or a second parameter, the first parameter is used to determine the building density at the location of the perception target, and the second parameter is used to determine the height of the location of the perception target.

[0205] Optionally, the first parameter includes a positioning position of the perception target.

[0206] Optionally, the decision threshold is related to the first path a priori probability and the second path a priori probability, and the first path a priori probability is related to the first parameter and / or the second parameter.

[0207] Optionally, the second path prior probability is negatively correlated with the first path prior probability.

[0208] Optionally, the first parameter is related to multiple building densities within the target time window, and the second parameter is related to multiple heights within the target time window.

[0209] Optionally, the first parameter is related to a mean value of multiple building densities within the target time window, and the second parameter is related to a mean value of multiple heights within the target time window.

[0210] Optionally, the length of the target time window belongs to a target length interval.

[0211] Optionally, the lower limit of the target length interval is greater than or equal to a first value, and the first value is greater than or equal to a signal transmission delay of the perception target.

[0212] Optionally, the upper limit of the target length interval is less than or equal to a second value, and the second value is related to the movement speed of the perceived target and the effective radius of the cell where the perceived target is located.

[0213] Optionally, the second value is equal to the quotient of the effective radius and the movement speed.

[0214] Optionally, the LLR is related to an observation parameter of a second signal, where the second signal is a signal preceding the first signal.

[0215] Optionally, the observation parameters of the second signal include at least one of the following: skewness, kurtosis, peak-to-average ratio, arrival angle skewness, arrival angle variance, and time delay.

[0216] Optionally, when the arrival angle spread of the second signal is greater than or equal to a first preset threshold, the observed parameter includes the arrival angle variance. When the arrival angle spread of the second signal is less than a second preset threshold, the observed parameter includes the arrival angle deviation, and the second preset threshold is less than or equal to the first preset threshold.

[0217] Optionally, when the first path prior probability is related to the first parameter, the control module 12 is also used to generate the first path prior probability based on the first prior probability function corresponding to the first path and the first parameter, and the first prior probability function is related to the building density reference value and the building density attenuation factor.

[0218] Optionally, when the prior probability of the first path is related to the second parameter, the control module 12 is further used to generate the prior probability of the first path based on the second prior probability function corresponding to the first path and the second parameter, where the second prior probability function is related to the altitude reference value and the altitude attenuation factor.

[0219] Optionally, when the prior probability of the first path is related to the first parameter and the second parameter, the control module 12 is also used to generate the prior probability of the first path based on the first prior probability function corresponding to the first path, the first parameter, the second prior probability function, the second parameter, the first weight of the first prior probability function, and the second weight of the second prior probability function.

[0220] The signal processing device provided in this embodiment can execute the actions of the network device in the aforementioned method embodiment. Its implementation principle and technical effects are similar and will not be described in detail here.

[0221] Optionally, the above-mentioned signal processing device may also include at least one storage module, which may include data and / or instructions. Other modules in the signal processing device (such as a receiving module, a sending module, a processing module, etc.) can read the data and / or instructions in the storage module to implement the corresponding method.

[0222] It should be noted that it should be understood that in each of the above embodiments, the sending module can be a transmitter when actually implemented, and the receiving module can be a receiver when actually implemented, or the sending module and the receiving module can be implemented through a transceiver, or the sending module and the receiving module can be implemented through a communication port. The processing module can be implemented in the form of software called by a processing element; it can also be implemented in the form of hardware. For example, the processing module can be at least one separately established processing element, or it can be integrated into a chip of the above-mentioned device for implementation. In addition, it can also be stored in the memory of the above-mentioned device in the form of program code, and called by a processing element of the above-mentioned device to perform the functions of the above-mentioned processing module. In addition, all or part of these modules can be integrated together, or they can be implemented independently. The processing element mentioned here can be an integrated circuit with signal processing capabilities. During implementation, each step of the above method or each of the above modules can be completed by an integrated logic circuit of hardware in the processor element or instructions in the form of software.

[0223] For example, the above modules may be one or more integrated circuits configured to implement the above methods, such as one or more application-specific integrated circuits (ASICs), one or more digital signal processors (DSPs), or one or more field programmable gate arrays (FPGAs). For another example, when a module is implemented by a processing element invoking program code, the processing element may be a general-purpose processor, such as a central processing unit (CPU) or other processor capable of invoking program code. For another example, the modules may be integrated together and implemented in the form of a system-on-a-chip (SOC).

[0224] Figure 8 This is a structural diagram of another signal processing device provided in an embodiment of the present application. Figure 8As shown, the signal processing device 800 may include: at least one processor 801, a memory 802, and a transceiver 803. The processor 801, the transceiver 803, and the memory 802 communicate with each other via an internal connection path. The memory 802 is used to store instructions, and the processor 801 is used to execute the instructions stored in the memory 802 to control the transceiver 803 to send and / or receive information.

[0225] It should be understood that the signal processing device may correspond to the network device in the above-described method embodiment. It may be used to execute the various steps and / or processes performed by the network device in the above-described method embodiment. Optionally, the memory 802 may include a read-only memory and a random access memory, and provide instructions and data to the processor 801. A portion of the memory 802 may also include a non-volatile random access memory. The memory 802 may be a separate device or integrated into the processor 801. The processor 801 may be used to execute instructions stored in the memory 802, and when the processor 801 executes the instructions stored in the memory, the processor 801 is used to execute the various steps and / or processes of the above-described method embodiment.

[0226] The transceiver 803 may include a transmitter and a receiver. The transceiver 803 may further include an antenna, which may be one or more. The processor 801, memory 802, and transceiver 803 may be integrated on different chips. For example, the processor 801 and memory 802 may be integrated in a baseband chip, and the transceiver 803 may be integrated in a radio frequency chip. The processor 801, memory 802, and transceiver 803 may also be integrated on the same chip. This application does not limit this.

[0227] Optionally, the signal processing device is a component configured in a network device, such as a chip, a chip system, etc.

[0228] The transceiver 803 may also be a communication interface, such as an input interface and / or output interface, circuit, etc. The transceiver 803, the processor 801 and the memory 802 may be integrated into the same chip, such as a baseband chip.

[0229] During implementation, each step of the above method can be completed by an integrated logic circuit of the hardware in the processor or by instructions in the form of software. The steps of the method disclosed in conjunction with the embodiments of the present application can be directly embodied as being executed by a hardware processor, or can be executed by a combination of hardware and software modules in the processor. The software module can be located in a storage medium mature in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in conjunction with its hardware. To avoid repetition, it will not be described in detail here.

[0230] It should be noted that the processor in the embodiments of the present application can be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above-described method embodiments can be completed by hardware integrated logic circuits in the processor or by software instructions. The above-described processor can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic device, discrete gate or transistor logic device, or discrete hardware components. The methods, steps, and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of the present application can be directly implemented and executed by a hardware decoding processor, or by a combination of hardware and software modules in the decoding processor. The software modules can be located in a storage medium well-known in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, etc. The storage medium is located in the memory, and the processor reads the information in the memory and, in conjunction with its hardware, completes the steps of the above-described method.

[0231] It is understood that the memory in the embodiments of the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. The non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), and direct RAM bus RAM (DR RAM). It should be noted that the memory described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0232] The present application also provides a chip system, including at least one processor and a communication interface, wherein the communication interface and the at least one processor are interconnected via a line, and the at least one processor is used to run a computer program or instruction to implement the method in the above embodiment.

[0233] The present application also provides a computer-readable storage medium, which may include: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, and other media that can store program codes. Specifically, the computer-readable storage medium stores program instructions, and when the program instructions are executed, the method in the above embodiment is implemented.

[0234] The present application also provides a computer program product, the program product including execution instructions, the execution instructions stored in a readable storage medium. At least one processor of a terminal or network device can read the execution instructions from the readable storage medium, and the at least one processor executes the execution instructions so that the terminal or network device implements the signal processing methods provided in the various embodiments described above.

[0235] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some or all of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A signal processing method, characterized in that: The method comprises: receiving first information sent by a sensing target, where the first information is used to indicate a decision threshold, where the decision threshold is correlated with a building density and / or height at a location where the sensing target is located; When a log likelihood ratio (LLR) between the first path and the second path is greater than or equal to the decision threshold, sending the first signal using the first path; When the LLR is less than the decision threshold, the first signal is sent using the second path, and the positioning error of the second path is greater than that of the first path.

2. The method according to claim 1, characterized in that The first information includes a first parameter and / or a second parameter, the first parameter is used to determine the building density at the location of the perception target, and the second parameter is used to determine the height of the location of the perception target.

3. The method according to claim 2, characterized in that The first parameter includes the positioning position of the sensing target.

4. The method according to claim 2, characterized in that The decision threshold is related to a first path a priori probability and a second path a priori probability, and the first path a priori probability is related to the first parameter and / or the second parameter.

5. The method according to claim 4, characterized in that The second path prior probability is negatively correlated with the first path prior probability.

6. The method according to claim 5, characterized in that The first parameter is related to a plurality of building densities within a target time window, and the second parameter is related to a plurality of heights within the target time window.

7. The method according to claim 6, characterized in that The first parameter is related to a mean value of multiple building densities within a target time window, and the second parameter is related to a mean value of multiple heights within the target time window.

8. The method according to claim 7, characterized in that The length of the target time window belongs to a target length interval.

9. The method according to claim 8, characterized in that The lower limit of the target length interval is greater than or equal to a first value, and the first value is greater than or equal to a signal transmission delay of the perception target.

10. The method according to claim 9, characterized in that The upper limit of the target length interval is less than or equal to a second value, and the second value is related to the movement speed of the perception target and the effective radius of the cell where the perception target is located.

11. The method according to claim 10, characterized in that The second value is equal to a quotient of the effective radius and the movement speed.

12. The method according to any one of claims 1 to 11, characterized in that The LLR is related to an observation parameter of a second signal, where the second signal is a signal preceding the first signal.

13. The method according to claim 12, characterized in that The observation parameters of the second signal include at least one of the following: skewness, kurtosis, peak-to-average ratio, arrival angle skewness, arrival angle variance, and time delay.

14. The method according to claim 13, characterized in that In a case where the arrival angle spread of the second signal is greater than or equal to a first preset threshold, the observation parameter includes the arrival angle variance; In a case where the arrival angle spread of the second signal is less than a second preset threshold, the observation parameter includes the arrival angle deviation, and the second preset threshold is less than or equal to the first preset threshold.

15. The method according to claim 5, characterized in that The first path prior probability is related to the first parameter, and the method further includes: The first path prior probability is generated according to the first prior probability function corresponding to the first path and the first parameter, where the first prior probability function is related to a building density reference value and a building density attenuation factor.

16. The method according to claim 5, characterized in that The first path prior probability is related to the second parameter, and the method further includes: The first path prior probability is generated according to a second prior probability function corresponding to the first path and the second parameter, where the second prior probability function is related to an altitude reference value and an altitude attenuation factor.

17. The method according to claim 5, characterized in that The first path prior probability is related to the first parameter and the second parameter, and the method further includes: The first path prior probability is generated according to the first prior probability function corresponding to the first path, the first parameter, the second prior probability function, the second parameter, the first weight of the first prior probability function, and the second weight of the second prior probability function.

18. A signal processing device, characterized in that: include: processor and memory; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to perform the method according to any one of claims 1 to 17.

19. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 17 is implemented.

20. A chip system, characterized in that: The system comprises at least one processor and a communication interface, wherein the communication interface and the at least one processor are interconnected via a line, and the at least one processor is configured to run a computer program or instruction to execute the method according to any one of claims 1 to 17.

21. A computer program product, characterized in that The method comprises a computer program which, when being executed, causes a computer to execute the method according to any one of claims 1 to 17.

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