Target sensing method based on area trajectory, communication equipment, and storage medium

Abstract

The present application provides a target sensing method, a communication device, and a storage medium based on a partition trajectory, which includes the steps of: calculating a delay Doppler spectrum of a received signal received for each sensing signal period, and obtaining a comprehensive delay Doppler spectrum of each received signal containing all target information; partitioning the comprehensive delay Doppler spectrum, and preferentially selecting and extracting angle information to obtain a delay arrival angle spectrum of a prioritized multi-target of the partition; determining a movement trajectory according to a timing sequence of the delay arrival angle spectrum of a prioritized multi-target of the partition; and determining a moving target according to the movement trajectory.

Description

[Technical Field]

[0001] This application is filed based on a Chinese patent application with application number 202211182526.7 and filing date September 27, 2022, claiming priority from said Chinese patent application, and all contents of said Chinese patent application are incorporated into this application for reference.

[0002] The embodiments of this application relate to the field of communications, and more particularly to a target sensing method based on partition trajectories, a communications device, and a storage medium. [Background technology]

[0003] In related technologies, achieving ubiquitous sensing in complex environments is extremely difficult. Specifically, complex environments often have many reflectors, resulting in multiple propagation paths. The energy of the reflected signal may be greater than the energy of the echo signal from the moving object. Furthermore, the reflected signal and the echo signal are not well separated in terms of velocity, angle, and distance, making it difficult to accurately achieve wireless sensing of moving objects in complex environments. [Overview of the project] [Problems that the invention aims to solve]

[0004] Embodiments of the present invention provide a target sensing method, communication equipment, and storage medium based on a partition trajectory, aimed at accurately realizing wireless sensing of a moving target in a complex environment. [Means for solving the problem]

[0005] According to the first aspect, the embodiment of the present application is A step of calculating the delayed Doppler spectrum of the received signal received for each sensing signal period, and obtaining an overall delayed Doppler spectrum of each of the received signals including all target information, wherein the overall delayed Doppler spectrum includes angular information, and the step of The steps include: partitioning the aforementioned overall delayed Doppler spectrum, preferentially selecting and extracting the angle information to obtain the delayed arrival angle spectrum of the priority multi-target of the partition; The steps include determining the movement trajectory according to the timing sequence of the delayed arrival angle spectrum of the priority multi-target in the said section, The present invention provides a target sensing method based on a partition trajectory, which includes the step of determining a moving target according to the aforementioned movement trajectory.

[0006] According to the second aspect, the embodiment of the present application is At least one processor, It includes at least one memory for storing at least one program, The at least one program, when executed by at least one of the processors, provides a communication device that performs a target sensing method based on a partition trajectory as described in any one of the first embodiments.

[0007] According to the third aspect, the embodiments of the present application are as follows: The present invention provides a computer-readable storage medium that stores a processor-executable program which, when executed by the processor, performs a target sensing method based on a partition trajectory as described in any one of the first embodiments.

[0008] According to the fourth aspect, the embodiments of the present application are as follows: Includes computer programs or computer instructions stored on a computer-readable storage medium, The processor of the computer device reads the computer program or the computer instruction from the computer-readable storage medium and executes the computer program or the computer instruction, thereby causing the computer device to execute the target sensing method based on the partition trajectory described in any one of the first embodiments. [Effects of the Invention]

[0009] According to the target sensing method, communication equipment, and storage medium based on partition trajectories according to the embodiment of the present invention, first, the delayed Doppler spectrum of the received signal received for each sensing signal period is calculated to obtain an overall delayed Doppler spectrum of each received signal including all target information, and the overall delayed Doppler spectrum includes angular information. Next, the overall delayed Doppler spectrum is partitioned, and angular information is preferentially selected and extracted to obtain the delayed arrival angle spectrum of the priority multi-target in the partition. Subsequently, the movement trajectory is determined according to the timing sequence of the delayed arrival angle spectrum of the priority multi-target in the partition. Finally, the moving target is determined according to the movement trajectory. In the embodiment of the present invention, the delayed arrival angle spectrum of the priority multi-target in the partition can be obtained by calculating the delayed Doppler spectrum of the received signal for each sensing signal period, and by partitioning, preferentially selecting, and extracting the overall delayed Doppler spectrum. Since the angle information and timing sequence of the multi-target are recorded in the above delayed arrival angle spectrum, the movement trajectory and the corresponding moving target can then be determined according to the changing trend of the angle information and timing sequence corresponding to the multi-target in the multiple delayed arrival angle spectra, thereby eliminating the influence of interfering targets. This enables accurate wireless sensing of moving targets in complex environments. [Brief explanation of the drawing]

[0010] [Figure 1] This is a schematic diagram illustrating human sensing in a ground scenario according to one embodiment of the present invention. [Figure 2] This is a schematic diagram illustrating vehicle sensing in a ground scenario according to one embodiment of the present invention. [Figure 3] This is a schematic diagram of a communication system for realizing a target sensing method based on a partition trajectory according to one embodiment of the present invention. [Figure 4] This is a flowchart of a target sensing method based on a partition trajectory according to one embodiment of the present invention. [Figure 5]It is a flowchart of a target sensing method based on a partitioned trajectory according to another embodiment of the present application. [Figure 6] It is a flowchart of a target sensing method based on a partitioned trajectory according to another embodiment of the present application. [Figure 7] It is a flowchart of a target sensing method based on a partitioned trajectory according to another embodiment of the present application. [Figure 8] It is a flowchart of a target sensing method based on a partitioned trajectory according to another embodiment of the present application. [Figure 9] It is a flowchart of a target sensing method based on a partitioned trajectory according to another embodiment of the present application. [Figure 10] It is a flowchart of a target sensing method based on a partitioned trajectory according to another embodiment of the present application. [Figure 11] It is a flowchart of a target sensing method based on a partitioned trajectory according to another embodiment of the present application. [Figure 12] It is a flowchart of a target sensing method based on a partitioned trajectory according to another embodiment of the present application. [Figure 13] It is a flowchart of a target sensing method based on a partitioned trajectory according to another embodiment of the present application. [Figure 14] It is a schematic diagram of the relationship between the sensing period, the sensing signal period, and the sensing signal length according to an embodiment of the present application. [Figure 15] It is a schematic diagram of the relationship between the partition width, the sensing signal length, and the sensing signal sampling period according to an embodiment of the present application. [Figure 16] It is a schematic diagram of the delay arrival angle spectrum of a sensing target according to an embodiment of the present application. [Figure 17] It is a schematic diagram of the delay arrival angle spectrum of a sensing target for a plurality of consecutive sensing periods according to an embodiment of the present application. [Figure 18] It is a schematic diagram showing the sensing of a moving target based on a curve fitting trajectory according to an embodiment of the present application. [Figure 19]This is a schematic diagram showing an implementation scenario in which a person walks according to one embodiment of the present invention. [Figure 20] A schematic diagram of Doppler domain data in an implementation scenario where a person walks, according to one embodiment of the present invention. [Figure 21] This is a schematic diagram of the trajectory drawn in an implementation scenario of a person walking according to one embodiment of the present invention. [Figure 22] This is a schematic diagram of an implementation scenario in which a drone flies according to one embodiment of the present invention. [Figure 23] This is a schematic diagram of the trajectory drawn in an implementation scenario in which a drone flies according to one embodiment of the present invention. [Figure 24] A schematic diagram of the configuration of a communication device according to one embodiment of the present invention. [Modes for carrying out the invention]

[0011] To further clarify the purpose, technical proposal and advantages of this application, the application will be described in more detail below with reference to the drawings and examples. The specific examples described herein are for interpretation purposes only and are not intended to limit this application.

[0012] Although the division of functional modules is shown in the schematic diagram of the device and the logical order is indicated in the flowchart, the division of modules in the device may differ in some cases, or the procedures shown or described may be performed in a different order than that shown in the flowchart. Terms such as "first," "second," etc., in this specification, claims, and the drawings above are terms used to distinguish similar objects and are not necessarily used to describe a specific order or priority.

[0013] In the embodiments of this application, terms such as “furthermore,” “exemplary,” or “optionally” are used as examples, illustrations, or descriptive phrases and should not be interpreted as being preferable or superior to other embodiments or design configurations. The use of terms such as “furthermore,” “exemplary,” or “optionally” is intended to specifically present the relevant concepts.

[0014] In related technologies, we are currently in the era of the Fourth Industrial Revolution, and a key characteristic of the Fourth Industrial Revolution is ubiquitous intelligence. On the one hand, intelligent technology is deeply integrated into people's lives, bringing great convenience and new experiences to their lives. On the other hand, intelligent technology is deeply integrated into all industries, enabling industrial upgrading and improved industrial efficiency through intelligence. Development in both directions will greatly liberate humanity and remove the constraints of low-level production.

[0015] Ubiquitous intelligent technology primarily includes ubiquitous sensing technology, ubiquitous computing technology, and product research and development. Therefore, ubiquitous intelligence requires ubiquitous systems, and among currently deployed systems, only wireless communication networks satisfy the ubiquitous requirement. Thus, realizing ubiquitous sensing and ubiquitous computing via wireless communication networks has become the primary technological means.

[0016] Currently, in the field of sensing, detailed research is mainly being conducted in the area of ​​radar, and current application scenarios for radar equipment mainly include aircraft sensing such as airport radar and short-range sensing such as vehicle-mounted radar. Both of these scenarios are simple. Ubiquitous sensing needs to be able to realize sensing in a variety of complex scenarios, such as indoor multipath environments, multiple buildings on the ground, multiple vehicle environments, and multiple-person environments in shopping malls. As shown in Figures 1 and 2, Figure 1 is a schematic diagram showing human sensing in a ground scenario according to one embodiment of the present invention. Figure 2 is a schematic diagram showing vehicle sensing in a ground scenario according to one embodiment of the present invention. Here, the shaded area within the dotted area in Figure 2 is the area where interference sensing signals are present, and it is difficult to accurately distinguish between moving targets and interference targets from this area. Currently, there is little research on sensing in complex environments, and even fewer sensing technologies that can be commercialized. Sensing in complex environments is a hot spot for research in both the standardization and academic communities and is in the early stages of technological breakthroughs.

[0017] Wireless sensing technology varies greatly depending on the scenario. In low-altitude environments such as drone sensing, the background is simple, so generally only the echo signal of the sensing target exists, and the signal separation in terms of distance, angle, and velocity is very good, making separation and identification easy. On the other hand, on the ground, there are many different reflectors, and generally, multipath is present, usually with more than 10 propagation paths, and in many cases the energy of the reflected signal from the environment is greater than the echo signal of the moving object. On the ground, the signal of the sensing target and the signal reflected from the environment are not very separated in terms of velocity, angle, and distance, and are often mixed, making conventional wireless sensing very difficult.

[0018] Based on this, the present application provides a target sensing method based on partition trajectories, communication equipment, a computer-readable storage medium, and a computer program product, which can determine a moving target among multiple targets in a complex scenario, eliminate the influence of interfering targets among multiple targets, and accurately realize wireless sensing of moving targets in a complex environment.

[0019] Figure 3 is a schematic diagram of a communication system for realizing a target sensing method based on a partition trajectory according to one embodiment of the present invention. The communication system includes a transmitting node 110, a sensing target 120, and a receiving node 130. The sensing target 120 includes, but is not limited to, a moving sensing target 121 whose position changes over time and an interference sensing target 122 whose position is fixed.

[0020] In one embodiment, as shown in Figure 3, the radio signal from the transmitting node 110 is transmitted directly to the receiving node 130 via the mobile sensing target 121, or indirectly to the receiving node 130 via the interference sensing target 122. Alternatively, in one embodiment, the radio signal from the transmitting node 110 may be transmitted directly to the receiving node 130 via the mobile sensing target 121, or indirectly to the receiving node 130 sequentially via the mobile sensing target 121 and the interference sensing target 122. In the embodiments of this application, the paths and methods by which the radio signals between the transmitting node 110 and the receiving node 130 pass through the mobile sensing target 121 and the interference sensing target 122, respectively, are not particularly limited.

[0021] The number of moving sensing targets 121 in the embodiment of the present application may be one or more, and the number of interference sensing targets 122 may also be one or more, and the number of moving sensing targets 121 and interference sensing targets 122 in the embodiment of the present application is not particularly limited.

[0022] The technical solutions of the embodiments of this application can be applied to various communication systems, such as wideband code division multiple access (WCDMA®), evolved universal terrestrial radio access network (E-UTRAN) systems, next generation radio access network (NG-RAN) systems, long-term evolution (LTE) systems, worldwide interoperability for microwave access (WiMAX) communication systems, 5th generation (5G) systems, new radio access technology (NR), and future communication systems such as 6G systems.

[0023] The technical solutions of the embodiments of this application can be applied to various communication technologies such as microwave communication, optical communication, and millimeter-wave communication. The embodiments of this application do not limit the specific forms of the technologies or equipment to be employed.

[0024] The transmitting node 110 in the embodiment of the present application may be an evolved base station (eNB), a transmission reception point (TRP), a next-generation base station (gNB) in an NR system, a base station in other future mobile communication systems, or a transmitting node in a wireless fidelity (WiFi) system. The embodiment of the present application is not limited to the specific technologies or specific forms of equipment employed by the transmitting node.

[0025] The mobile sensing target 121 in the embodiment of this application is a user-side entity such as a mobile phone for transmitting and receiving signals. It may also be called a receiving node device (terminal), user equipment (UE), mobile station (MS), mobile receiving node device (MT), etc. The receiving node device may be an automobile with communication functions, a smart car, a mobile phone, a wearable device, a tablet computer (Pad), a computer with wireless transmission and reception functions, a virtual reality (VR) receiving node device, an augmented reality (AR) receiving node device, a wireless receiving node device in industrial control, a wireless receiving node device in self-driving, a wireless receiving node device in transportation safety, a wireless receiving node device in a smart city, etc. The embodiment of this application does not limit the specific forms of the specific technologies and equipment used in the mobile sensing target.

[0026] The receiving node 130 in the embodiment of the present application may be an evolved base station (eNB), a transmission reception point (TRP), a next-generation base station (gNB) in an NR system, a base station in other future mobile communication systems, or a receiving node in a wireless fidelity (WiFi) system. The embodiment of the present application does not limit the specific technologies or specific forms of equipment employed by the receiving node.

[0027] Below, we propose various embodiments of the target sensing method based on partition trajectories according to the embodiments of this application, based on the communication system of any of the embodiments described above.

[0028] As shown in Figure 4, Figure 4 is a flowchart of a target sensing method based on a partition trajectory according to one embodiment of the present invention. This target sensing method based on a partition trajectory may include, but is not limited to, the following steps S100, S200, S300, and S400.

[0029] Step S100: The delayed Doppler spectrum of the received signal is calculated for each sensing signal period, and an overall delayed Doppler spectrum of each received signal containing all target information is obtained, and the overall delayed Doppler spectrum contains angular information.

[0030] Step S200: The overall delayed Doppler spectrum is partitioned, and angular information is selectively selected and extracted to obtain the delayed arrival angle spectra of the priority multi-targets in the partition.

[0031] Step S300: Determine the movement trajectory according to the timing sequence of the delayed arrival angle spectra of the priority multi-targets in the section.

[0032] Step S400: Determine the movement target according to the movement trajectory.

[0033] In one embodiment of the present invention, the delayed Doppler spectrum of the received signal is calculated for each sensing signal period to obtain a comprehensive delayed Doppler spectrum including angular information. By partitioning, preferentially selecting, and extracting the comprehensive delayed Doppler spectrum, the delayed arrival angle spectra of the priority multi-targets in the partition can be obtained. Since the angular information and timing sequence of the multi-targets are recorded in the above delayed arrival angle spectra, the movement trajectory and corresponding moving target can then be determined according to the changing trends of the angular information and timing sequence corresponding to the multi-targets in the multiple delayed arrival angle spectra, thereby eliminating the influence of interfering targets. This enables accurate wireless sensing of moving targets in complex environments.

[0034] In one embodiment, there are multiple sensing signal cycles, and each sensing signal cycle includes at least one received signal; however, in the embodiments of the present application, there are no particular limitations on the number of sensing signal cycles and received signals.

[0035] In one embodiment, the overall delayed Doppler spectrum corresponds to all target information, while the delayed arrival angle spectra of the priority multi-targets in a section correspond to a portion of the target information because they have undergone section processing and preferential selection processing. This portion of target information is the target information after screening and includes, but is not limited to, the moving target information and the interfering target information after screening.

[0036] In one embodiment of the present invention, one or more movement trajectories may be estimated according to the timing sequence of multiple delayed arrival angle spectra. Specifically, since the position of the moving target changes with time, the timing sequence of the multiple delayed arrival angle spectra also changes accordingly, and therefore, the embodiment of the present invention can obtain the movement trajectory of the moving target according to the timing sequence of multiple delayed arrival angle spectra. Here, each movement trajectory corresponds to one moving target, and the presence of multiple movement trajectories indicates the presence of multiple moving targets.

[0037] Furthermore, as shown in Figure 5, Figure 5 is a flowchart of a target sensing method based on a partition trajectory according to another embodiment of the present invention. Step S100, which calculates the delayed Doppler spectrum of the received signal received for each sensing signal period and obtains the overall delayed Doppler spectrum of each received signal including all target information, includes, but is not limited to, steps S510 and S520.

[0038] Step S510: Calculate the channel impulse response of the received signal for each sensing signal period received by each antenna, and obtain the channel impulse response vector corresponding to each antenna.

[0039] Step S520: Calculate the delayed Doppler spectra of all channel impulse response vectors corresponding to all antennas to obtain the overall delayed Doppler spectrum of each received signal, including all target information.

[0040] In one embodiment of the present invention, first, the channel impulse response vector corresponding to each antenna is calculated, and then, based on all the channel impulse response vectors corresponding to all the antennas, a delayed Doppler spectrum may be calculated to obtain an overall delayed Doppler spectrum.

[0041] In one embodiment, the antenna of the embodiment of the present application may be one or more, and the number of antennas is not particularly limited in the embodiment of the present application.

[0042] Furthermore, as shown in Figure 6, Figure 6 is a flowchart of a target sensing method based on partition trajectories according to another embodiment of the present invention. The above step S520, which calculates the delayed Doppler spectra of all channel impulse response vectors corresponding to all antennas and obtains the overall delayed Doppler spectrum of each received signal containing all target information, includes, but is not limited to, steps S610 and S620.

[0043] Step S610: Calculate the delayed Doppler spectrum of the channel impulse response vector corresponding to each antenna and obtain the initial delayed Doppler spectrum containing all target information corresponding to each antenna.

[0044] Step S620: Directional filtering is performed on all initial delayed Doppler spectra corresponding to all antennas to obtain the overall delayed Doppler spectrum of each received signal, including all target information.

[0045] In one embodiment of the present invention, first, an initial delayed Doppler spectrum containing all target information corresponding to each antenna may be calculated, and then, based on different azimuth directions, direction filtering may be performed on all initial delayed Doppler spectra corresponding to all antennas to screen for an overall delayed Doppler spectrum of each received signal containing all target information.

[0046] Furthermore, as shown in Figure 7, Figure 7 is a flowchart of a target sensing method based on a partition trajectory according to another embodiment of the present invention. The above step S510, which calculates the channel impulse response of the received signal for each sensing signal period received by each antenna and obtains a channel impulse response vector corresponding to each antenna, includes, but is not limited to, steps S710 and S720.

[0047] Step S710: Based on a preset sampling period, a sample is taken for each received signal received by each antenna, and multiple sampled data points are obtained.

[0048] Step S720: For each sensing signal period, a channel impulse response vector corresponding to each antenna is obtained based on multiple sampling data corresponding to each antenna and the sensing signal transmitted within the sensing signal period.

[0049] In one embodiment of the present invention, the channel impulse response vector corresponding to each antenna may be calculated by sampling. Specifically, first, for each received signal received by each antenna, a sample may be taken for a predetermined sampling period, and the channel impulse response vector corresponding to each antenna may be calculated based on the multiple sampled data and the sensing signal transmitted within the sensing signal period.

[0050] In one embodiment, the preset sampling period in the embodiment of the present application may be preset, and the specific time length of the preset sampling period is not particularly limited in the embodiment of the present application.

[0051] Furthermore, as shown in Figure 8, Figure 8 is a flowchart of a target sensing method based on a partition trajectory according to another embodiment of the present invention. Step S620 above, which involves performing directional filtering on all initial delayed Doppler spectra corresponding to all antennas to obtain an overall delayed Doppler spectrum of each received signal containing all target information, includes, but is not limited to, steps S810, S820, and S830.

[0052] Step S810: Obtain multiple angle information.

[0053] Step S820: For each sensing signal cycle, calculate the combined delayed Doppler spectrum at the angle corresponding to the angular information for the initial delayed Doppler spectra of all antennas.

[0054] Step S830: Screening is performed based on all the combined delayed Doppler spectra of the sensing signal period to obtain a comprehensive delayed Doppler spectrum containing all target information within the sensing signal period.

[0055] In one embodiment, the direction filtering process described above involves calculating a composite delayed Doppler spectrum of the initial delayed Doppler spectra of all antennas for each angle corresponding to the angle information for each sensing signal period, then screening all composite delayed Doppler spectra within the sensing signal period according to the amplitude, and obtaining a comprehensive delayed Doppler spectrum that includes all target information within the sensing signal period.

[0056] In one embodiment, the angle information may include horizontal angle information, vertical angle information, or both horizontal and vertical angle information; however, the present embodiment does not particularly limit the type of angle information.

[0057] In one embodiment, each delayed Doppler spectral binary data in the overall delayed Doppler spectrum is the delayed Doppler spectral binary data with the largest corresponding amplitude in the composite delayed Doppler spectrum that corresponds to all angles.

[0058] In one embodiment, in addition to the delayed Doppler spectral binary data with the largest amplitude described above, the delayed Doppler spectral binary data with the next largest amplitude may also be used, or delayed Doppler spectral binary data with other amplitudes may also be used, and in the embodiments of the present application, there are no particular limitations on the amplitude of the delayed Doppler spectral binary data.

[0059] Furthermore, as shown in Figure 9, Figure 9 is a flowchart of a target sensing method based on partition trajectories according to another embodiment of the present invention. Step S200, which partitions the overall delayed Doppler spectrum, preferentially selects and extracts angular information to obtain the delayed arrival angle spectrum of the priority multi-target of the partition, includes, but is not limited to, steps S910, S920, and S930.

[0060] Step S910: The overall delayed Doppler spectrum is partitioned to obtain multiple partitions containing binary data of multiple delayed Doppler spectra.

[0061] Step S920: Depending on the number of targets, prioritize selecting the preferred delayed Doppler spectral binary data corresponding to each segment from all the delayed Doppler spectral binary data corresponding to each segment.

[0062] Step S930: Angle information is extracted from all priority delayed Doppler spectral binary data in each section, and the delayed arrival angle spectrum of the priority multi-target in each section is obtained, with each priority delayed Doppler spectral binary data corresponding to one priority target.

[0063] In one embodiment of the present invention, the overall delayed Doppler spectrum is partitioned, and binary data of the number of targets is selected from each partition. Then, angular information of the preferred delayed Doppler spectrum binary data is extracted, and the delayed arrival angle spectra of the preferred multi-targets for each partition are generated.

[0064] In one embodiment, the number of targets may be one or more, and the number of targets is not particularly limited in the embodiments of this application.

[0065] Furthermore, as shown in Figure 10, Figure 10 is a flowchart of a target sensing method based on a partition trajectory according to another embodiment of the present invention. Step S920, which preferentially selects the priority delayed Doppler spectral binary data corresponding to each partition from all the delayed Doppler spectral binary data corresponding to each partition according to the number of targets, includes, but is not limited to, steps S1010 and S1020.

[0066] Step S1010: Sort the amplitudes of all delayed Doppler spectral binary data corresponding to each section in descending or ascending numerical order.

[0067] Step S1020: Depending on the number of targets, multiple sorted delayed Doppler spectral binary data corresponding to each section are selected in descending order to obtain preferred delayed Doppler spectral binary data corresponding to each section.

[0068] In one embodiment of the present invention, the priority delayed Doppler spectral binary data corresponding to each section may be selected from the delayed Doppler spectral binary data of the target number with the highest numerical value.

[0069] Furthermore, as shown in Figure 11, Figure 11 is a flowchart of a target sensing method based on a partition trajectory according to another embodiment of the present invention. The above step S300, which determines the movement trajectory according to the timing sequence of delayed arrival angle spectra of the priority multi-targets of the partition, includes, but is not limited to, steps S1110 and S1120.

[0070] Step S1110: Determine the timing sequence of all delayed arrival angle spectra based on the periods of multiple sensing signals.

[0071] Step S1120: Curve fitting is performed for all delayed arrival angle spectra according to the timing sequence to obtain at least one movement trajectory.

[0072] In one embodiment of the present invention, curve fitting may be performed according to the timing sequence of multiple delayed arrival angle spectra to obtain one or more movement trajectories. Specifically, since the position of the moving target changes with time, the timing sequence of the multiple delayed arrival angle spectra also changes accordingly, and therefore, the movement trajectory of the moving target can be obtained according to the timing sequence of the multiple delayed arrival angle spectra. Here, each movement trajectory corresponds to one moving target, and the presence of multiple movement trajectories indicates the presence of multiple moving targets.

[0073] Furthermore, as shown in Figure 12, Figure 12 is a flowchart of a target sensing method based on a partition trajectory according to another embodiment of the present invention. This target sensing method based on a partition trajectory further includes, but is not limited to, steps S1210 and S1220 after step S400, in which a moving target is determined according to the movement trajectory.

[0074] Step S1210: Determine the inertial movement tendency of the moving target according to the movement trajectory.

[0075] Step S1220: Determine the position information of the moving target based on the inertial movement tendency and the delayed arrival angle spectrum of the priority multi-target in the section corresponding to the next sensing signal cycle.

[0076] In one embodiment of the present invention, by estimating the future inertial movement tendency of the moving target according to its movement trajectory, the position information of the moving target can be preferentially determined from the delayed arrival angle spectrum of the priority multi-target in the section corresponding to the next sensing signal cycle.

[0077] In one embodiment, the inertial movement tendency of the moving target in the embodiment of the present invention may mean the position the moving target is about to arrive at or the trajectory it is about to move along.

[0078] Furthermore, as shown in Figure 13, Figure 13 is a flowchart of a target sensing method based on a partition trajectory according to another embodiment of the present invention. This target sensing method based on a partition trajectory further includes, but is not limited to, step S1300 after step S1120 above, which obtains at least one movement trajectory.

[0079] Step S1300: In the combined delayed Doppler spectra of multiple targets, if there is a target among the partitioned priority multi-targets whose relative position change is smaller than a preset threshold, that target is determined to be an interference target.

[0080] In one embodiment, if there is a target among the partitioned priority multi-targets whose relative position change is smaller than a preset threshold, it indicates that the target's position has not changed over time. In this case, in the embodiment of the present invention, that target can be considered an interference target.

[0081] Below, based on the target sensing method based on a partition trajectory according to one of the embodiments described above, we propose specific embodiments of the target sensing method based on a partition trajectory according to the embodiments of this application.

[0082] In specific embodiments, the target sensing method based on this section trajectory includes, but is not limited to, the following steps 1 to 8.

[0083] Step 1: The sensing signal transmitting base station transmits a sensing signal S on the configured radio resource within a3 according to the sensing signal period a2, according to the sensing period a3.

[0084] Step 2: The sensing signal receiving base station receives the current sensing signal y for the current sensing period z. Each received antenna signal R m.n For (t), sampling is performed according to the sampling period a4, and sampling is performed continuously within the symbol time length a1 to obtain the sampled data R of one symbol. m.n The equation (i,y), i∈[0,a1 / a4) is formed, where m and n are oscillator numbers in the antenna array, m∈[0,M-1] and n∈[0,N-1], and y is the currently received sensing signal period number, y∈[0,a3 / a2]. The relationship between the sensing period, sensing signal period, and sensing signal length is shown in Figure 14.

[0085] For each antenna, signal sampling data is received, and based on the transmitted signal s, the channel impulse response vector c of each antenna is calculated. m.n We obtain (i, y).

[0086] Step 3: At the current sensing period z, the delay-Doppler spectrum d of the current sensing period is calculated for the channel impulse response of all received sensing signals (y from 0 to a3 / a2-1). m.n We calculate (i,x), where i is the sample number and x is the Doppler frequency, and x ∈ [0, a3 / a2).

[0087] Step 4: All d m.n Directional filtering is performed on (i,x) to obtain the maximum value delayed-Doppler spectrum M(i,x), and the angle corresponding to each (i,x) is recorded. Here, the value of M(i,x) is the value at which the maximum amplitude value is M(i,x) in omnidirectional filtering, and the angle corresponding to the maximum amplitude value is the angle corresponding to M(i,x).

[0088] Step 5: M(i,x) is partitioned according to the delay partition width a5, and the number of detection targets in each partition is K. The top k largest peak binaries among all (i,x) in each partition of M(i,x) with the current sensing period z are selected as the k sensing targets in that partition. For example, the k1st largest target to the k2nd largest target are selected, and K2-K1=K. Figure 15 shows the relationship between partition width, sensing signal length, and sensing signal sampling period.

[0089] Each section has a k*a1 / a5 target. For all sensing targets, the delayed arrival angle spectrum P of the sensing target for that sensing period is determined according to the corresponding angle and delayed sample number. z A (i,β) is formed, and the spectrum contains k*a1 / a5 targets, with 0 assigned where there are no targets. The delayed arrival angle spectrum of the sensing target is shown in Figure 16.

[0090] The delayed arrival angle spectrum of the sensing target for this period may be selectively transmitted to the sensing server.

[0091] Step 6: Repeat Steps 1-5 to obtain the delayed arrival angle spectrum P of the sensing target for the next sensing period. z+1 (i,β) is formed. The process continues, and delayed arrival angle spectra of sensing targets with continuous sensing periods are obtained. The delayed arrival angle spectra of sensing targets with multiple continuous sensing periods are shown in Figure 17.

[0092] Step 7: Perform curve fitting on the continuous delayed arrival angle spectra, determine the number of sensing targets based on the fitting curve, determine the movement trajectory of each sensing target, and determine the interval of the interference region.

[0093] Here, a smooth, continuous curve may be formed to create one motion sensing target, or multiple smooth, continuous curves may be formed to create multiple motion sensing targets.

[0094] The interference region is defined as the sensing target area where the angle and delay are relatively fixed.

[0095] Curve fitting is optionally performed on the sensing server. A schematic diagram of the curve-fitted trajectory sensing moving target is shown in Figure 18.

[0096] Step 8: Based on the inertial tendency determined by the movement trajectory of the sensing target, the current position of the sensing target is preferentially determined from the delayed arrival angle spectrum of the sensing target for the subsequent sensing period.

[0097] Based on the steps 1 through 8 described above, some of the steps from steps 1 through 8 will be explained in detail below.

[0098] "For the received signal sampling data of each antenna, the channel impulse response vector c of each antenna is calculated based on the transmitted signal s." m.n Step 2, "Obtain (i,y)", This includes converting the sampling data from each antenna into the frequency domain, then dividing each subcarrier received data by each transmitted subcarrier signal to obtain the frequency domain impulse response of each antenna, and then converting it into the time domain to obtain the time domain impulse response.

[0099] Also, for the step 3 of "calculating the delay-Doppler spectrum of the current sensing period", specifically, it is as follows. After obtaining the time-domain impulse responses of all sensing signals within one sensing period of each antenna, starting from the delay sample number 0, the same delay sample number value is taken from the channel impulse responses of all sensing signals of the current sensing period respectively, and then, Fourier transform is performed to obtain the delay-Doppler spectrum of the current sensing period.

Number

[0100] Also, for the step 4 of "performing direction filtering on all d m.n (i, x) to obtain the maximum delay-Doppler spectrum M(i, x) and record the angle corresponding to each (i, x)", it includes performing direction filtering at the step size Δθ in the azimuth angle interval [θ1, θ2] and at the step size Δβ in the pitch angle interval [β1, β2].

Number

[0101] Also, the "top k large peaks" in step 5 means sorting the amplitudes corresponding to all (i, x) binaries within the section of M(i, x) in descending order and taking the first K as the sensing targets.

[0102] According to the technical proposals in steps 1 to 8 described above, compared to conventional technologies, it is possible to solve problems such as large sensing errors due to interference in complex environments, mutual interference between the environment and the sensing target, and mutual interference between multiple targets. In the embodiments of this application, sensing of multiple targets and interference intervals in complex environments can be realized by trajectory sensing.

[0103] In another embodiment, in a multi-target sensing implementation scenario, there are two test paths 1 and 2 perpendicular to each other within an area with a sensing signal transmission period of 5 ms, a frequency of 4.9 GHz, a length of 160 m, and a width of 90 m, and as shown in Figure 19, two testers (targets) walk along path 1 and path 2 respectively.

[0104] Therefore, the processing steps of the embodiment are as follows.

[0105] The LFM sensing signal has a transmission period of 5 ms and a bandwidth of 100 MHz.

[0106]

number

[0107]

number

[0108] The angles are determined, that is, spatial filtering is performed in the range of -90° to 90° in the horizontal direction and -90° to 90° in the vertical direction, a steering vector is constructed for each angle, the dot product is calculated with 32 elements of Doppler domain data, the sum is calculated, and M m.n We obtain (i,x). M m.n The maximum horizontal and vertical angles of (i,x) represent the direction of the incoming signal.

[0109] The distance is calculated, that is, the Doppler domain data M m.n(i,x) has a distance range of 0 to 4096Ts and a frequency range of 0 to 200Hz. The time interval width is set to m=8 and the number of detection targets is set to k=4. Within the range of 0 to 4096Ts, one interval is defined for every mTs, and within this interval, the k largest Doppler amplitude data points are searched for and their distance T values ​​are recorded. Then, the corresponding angle value is determined according to 4. The above is the sensing result of distance and angle at the first second.

[0110] Drawing the trajectory: Repeat the above steps to obtain sensing results for one minute, and draw the trajectory as shown in Figure 21.

[0111] In another embodiment, the implementation scenario for interference region sensing is as follows: The transmission period of the sensing signal is 5 ms, the frequency is 4.9 GHz, and the drone must pass through two interference regions on its flight path, one with interference from a nearby fan operating and the other with interference from a distant number of leaves swaying. The flight path is shown in Figure 22.

[0112] Therefore, the processing steps of the embodiment are as follows.

[0113] The LFM sensing signal has a transmission period of 5 ms and a bandwidth of 100 MHz.

[0114]

number

[0115]

number

[0116] The angles are determined, that is, spatial filtering is performed in the range of 40° to 80° horizontally and 0° to 20° vertically, a steering vector is constructed for each angle, the dot product is calculated with 32 elements of Doppler domain data, the sum is calculated, and M m.n We obtain (i,x). M m.n The maximum horizontal and vertical angles of (i,x) represent the direction of the signal's arrival.

[0117] The distance is calculated, that is, the Doppler domain data M m.n (i,x) has a distance range of 0 to 4096 Ts and a frequency range of 0 to 200 Hz. m=9 is the time interval width, and k=3 is the number of detection targets. Within the range of 0 to 4096 Ts, one interval is defined for every mTs, and within this interval, k of the largest Doppler amplitude data points are searched for and their distance T values ​​are recorded. Then, the corresponding angle value is determined according to 4. The above is the sensing result of distance and angle at 1 s second.

[0118] Drawing the trajectory: Repeat the above steps to obtain sensing results for one minute and draw the trajectory as shown in Figure 23. The trajectory diagram clearly shows the drone's trajectory A and two interference regions, where region B is the distant leaf interference region and region C is the nearby fan interference region.

[0119] Below, we propose embodiments of communication equipment, computer-readable storage media, and computer program products according to the embodiments of this application, based on the target sensing method based on partition trajectories according to any of the embodiments described above.

[0120] Figure 24 is a schematic diagram of the configuration of a communication device according to one embodiment of the present invention. As shown in Figure 24, this communication device 200 includes a memory 210 and a processor 220. The number of memory 210 and processor 220 may be one or more, but Figure 24 illustrates one memory 210 and one processor 220. The memory 210 and processor 220 of the device may be connected via a bus or by other means, but Figure 24 illustrates a connection via a bus.

[0121] Memory 210 can be used as a computer-readable storage medium to store software programs, computer-executable programs, and modules such as program instructions / modules corresponding to the partition trajectory-based target sensing method according to any embodiment of the present application. The processor 220 realizes the partition trajectory-based target sensing method by executing the software programs, instructions, and modules stored in memory 210.

[0122] Memory 210 may primarily include a program memory area capable of storing applications necessary for at least one function, and a data memory area. Furthermore, memory 210 may include high-speed random-access memory and non-volatile memory such as at least one magnetic disk memory device, flash memory device, or other non-temporary solid-state storage device. In some examples, memory 210 may include memory remotely located relative to the processor 220, and these remote memories may be connected to the device via a network. Examples of the network include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0123] One embodiment of the present invention further provides a computer-readable storage medium storing computer-executable instructions for performing a target sensing method based on a partition trajectory according to any embodiment of the present invention.

[0124] One embodiment of the present invention further provides a computer program product that includes a computer program or computer instruction stored in a computer-readable storage medium, wherein the processor of the computer device reads the computer program or computer instruction from the computer-readable storage medium, and the processor executes the computer program or computer instruction, thereby causing the computer device to execute a target sensing method based on a partition trajectory according to any embodiment of the present invention.

[0125] The system architectures and application scenarios described in the embodiments of this application are intended to provide a clearer explanation of the technical proposals of the embodiments and do not limit the technical proposals of the embodiments. The technical proposals of the embodiments are similarly applicable to similar technical modifications arising from the evolution of system architectures and the emergence of new application scenarios.

[0126] All or some steps of the methods disclosed above, the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or appropriate combinations thereof.

[0127] In hardware embodiments, the division between functional modules / units described above does not necessarily correspond to the division of physical components. For example, one physical component may have multiple functions, and one function or step may be performed in conjunction with multiple physical components. Some or all of the physical components may be implemented as software executed by a processor such as a central processor, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit such as an application-specific integrated circuit. Such software may be distributed on computer-readable media, which may include computer storage media (or non-temporary media) and communication media (or temporary media). As is well known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technique for storing information (e.g., computer-readable instructions, data structures, program modules, or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disk (DVD) or other optical disk storage devices, magnetic cartridges, magnetic tapes, magnetic disk storage devices or other magnetic storage devices, or any other media that can be used to store desired information and can be accessed by a computer. Furthermore, it is well known to those skilled in the art that communication media may include any information distribution media, typically containing computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms.

[0128] In this specification, terms such as “component,” “module,” and “system” are used to describe computer-related entities, hardware, firmware, hardware-software combinations, software, or running software. For example, a component may be, but is not limited to, a process running on a processor, a processor, an object, an executable file, an execution thread, a program, or a computer. As illustrated, both an application running on a computing device and the computing device itself may be components. One or more components may reside within a process or execution thread, and components may be located on one computer or distributed across two or more computers. Furthermore, these components may run from various computer-readable media storing various data structures. Components may communicate via local or remote processes, for example, by signals having one or more data packets (e.g., data from two components interacting with other components across a local system, a distributed system, or a network, e.g., the Internet interacting with other systems via signals).

Claims

1. A section-based trajectory target sensing method performed by a sensing signal receiving node, wherein the sensing signal receiving node includes an antenna array comprising a plurality of antennas, The trajectory target sensing method based on the aforementioned section is: A step of calculating a delayed Doppler spectrum for each of the antennas receiving the sensing signal within the current sensing period, and obtaining a first delayed Doppler spectrum for the current sensing period that includes all target reflected signals, wherein the first delayed Doppler spectrum includes delay information, frequency information, and amplitude information for each of the target reflected signals. The steps include: performing directional filtering on all of the first delayed Doppler spectra of the current sensing period within a preset directional angle range to obtain a maximum value delayed Doppler spectrum of the current sensing period that includes all target reflection signals; and recording the filtering angle information of the directional filtering corresponding to each of the target reflection signals in the maximum value delayed Doppler spectrum as the first angle information of each of the target reflection signals; A step of partitioning the delay of the maximum value delayed Doppler spectrum into first time partition widths to obtain M partitions, and for all target reflection signal information for each partition of the maximum value delayed Doppler spectrum, first information of a predetermined number of targets in the top partitions in descending order of amplitude information is obtained to obtain a partition delay arrival angle spectrum of the current sensing period, wherein the first information includes the delay information, the frequency information, the amplitude information and the first angle information. A step of determining the movement trajectory based on the partition delay arrival angle spectra of a series of the sensing periods, A method comprising the step of recognizing a moving target based on the aforementioned movement trajectory.

2. The step of calculating a delayed Doppler spectrum for each of the antennas receiving the sensing signal within the current sensing period, and obtaining a first delayed Doppler spectrum for the current sensing period that includes all target reflected signals, The steps include obtaining the channel impulse response vector corresponding to each antenna for the sensing signal for each sensing period of the current sensing period received by each antenna, The method according to claim 1, comprising the step of obtaining a first delayed Doppler spectrum for the current sensing period, including all target reflected signals, by calculating a delayed Doppler spectrum for all the channel impulse response vectors corresponding to all the antennas for the current sensing period.

3. The step of obtaining the channel impulse response vector corresponding to each antenna for each sensing signal period of the current sensing period received by each antenna is as follows: In the current sensing period, based on a preset sampling period, a sample is taken for each sensing signal received by each antenna to obtain multiple sampled data. The method according to claim 2, comprising the step of obtaining a channel impulse response vector corresponding to each antenna based on a plurality of sampling data corresponding to each antenna and the transmitted sensing signal in the current sensing period.

4. The step of performing directional filtering on all of the first delayed Doppler spectra of the current sensing period within a preset directional angle range to obtain a maximum value delayed Doppler spectrum of the current sensing period including all target reflection signals, and recording the filtering angle information of the directional filtering corresponding to each of the target reflection signals in the maximum value delayed Doppler spectrum as first angle information for each of the target reflection signals, The steps include: acquiring multiple filter angle information by traversing within a predetermined directional angle range with a predetermined angle step size; A step of calculating a second delayed Doppler spectrum for all of the antennas in the current sensing period, including all target reflected signals at the filter angle corresponding to each of the filter angle information, The method according to claim 1, comprising the step of, for each target reflection signal, selecting from the second delayed Doppler spectra at all the filter angles the filter angle information corresponding to the time when the amplitude information is maximum is set as the first angle information, and setting the maximum amplitude information as the amplitude information of the target reflection signal in the maximum delayed Doppler spectrum, thereby forming the maximum delayed Doppler spectrum.

5. With respect to the information of all target reflection signals for each section of the maximum value delayed Doppler spectrum, first information of a predetermined number of targets for each of the higher-order sections is obtained in descending order of amplitude information. The information of all target reflected signals for each section of the maximum value delayed Doppler spectrum is sorted in descending order of the numerical value of the amplitude information, and the information of a predetermined number of target reflected signals for the first section is extracted, or The method according to claim 1, comprising sorting the information of all target reflected signals for each section of the maximum value delayed Doppler spectrum in ascending order of the numerical values ​​of the amplitude information, and extracting a predetermined number of target reflected signal information for the last section.

6. The step of determining the movement trajectory based on the partition delay arrival angle spectra of a successive number of sensing periods is: The method according to claim 1, comprising the step of performing curve fitting on time to a plurality of consecutive partitioned delayed arrival angle spectra of sensing periods to obtain at least one movement trajectory.

7. After the step of recognizing the target as a moving target based on the aforementioned movement trajectory, A step of determining the inertial movement tendency of the moving target according to the movement trajectory, The method according to claim 1, further comprising the step of determining the position information of the moving target based on the inertial movement tendency and the section delayed arrival angle spectrum corresponding to the next sensing period.

8. After the step of obtaining at least one movement trajectory, The method according to claim 6, further comprising the step of determining, in the partitioned delayed arrival angle spectrum, a target whose relative position change is smaller than a preset threshold as an interference target.

9. At least one processor, It includes at least one memory for storing at least one program, A communication device wherein at least one of the programs, when executed by at least one of the processors, performs a target sensing method based on a partition trajectory according to any one of claims 1 to 8.

10. A computer-readable storage medium storing a processor-executable program that, when executed by the processor, performs a target sensing method based on a partition trajectory as described in any one of claims 1 to 8.

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