An OFDM inductive integrated system that integrates a multi-station, multi-target matching algorithm and system for radial velocity.

Abstract

This application relates to a multi-station, multi-target matching algorithm and system for OFDM sensory integration system that integrates radial velocity. The method includes constructing sensor node groups among all sensor nodes; determining a set of candidate locked targets for each sensor node group; filtering sensor node groups belonging to the same candidate locked target to obtain a tracking node group; converting candidate locked targets matched with the tracking node group into locked targets; completing the pairing relationship between the remaining sensor nodes and locked targets; if candidate locked targets still exist, continuing to construct sensor node groups and perform matching until the candidate locked targets disappear; and relocating the locked targets using distance measurement information from all sensor nodes. The multi-station, multi-target matching algorithm and system for OFDM sensory integration system that integrates radial velocity, based on multi-point positioning and simultaneously introducing a multi-dimensional judgment method, achieves early elimination of infeasible associations, significantly improving association reliability while ensuring positioning accuracy.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a multi-station, multi-target matching algorithm and system for integrating radial velocity in an OFDM inductive system. Background Technology

[0002] In multi-station, multi-target integrated sensory communication (ISAC) systems, target association and localization typically require joint optimization within a high-dimensional combination space, with computational complexity increasing exponentially with the number of targets and stations. To achieve optimal performance, exhaustive search methods can traverse all possible association combinations and obtain the global optimum through the minimum residual criterion. However, this method becomes impractical when the number of targets is even slightly large.

[0003] There are also algorithms based on grid search. The idea behind the grid method is to divide the possible area of ​​the target into grids, and then use the grids to match the detection values ​​of each station. The disadvantage of this type of algorithm is that its accuracy depends on the size of the grid, and its computational cost is also very high.

[0004] To reduce complexity, most existing approximation algorithms rely on distance consistency constraints for pruning and searching. However, in scenarios with high noise or dense targets, association ambiguity and performance degradation are likely to occur. Summary of the Invention

[0005] This application provides a multi-station, multi-target matching algorithm and system for OFDM integrated sensing system that integrates radial velocity. Based on multi-point positioning, it introduces a multi-dimensional judgment method to achieve early elimination of infeasible associations, which significantly improves the reliability of association while ensuring positioning accuracy.

[0006] The above-mentioned objective of this application is achieved through the following technical solution:

[0007] Firstly, this application provides a multi-station, multi-target matching algorithm for integrating radial velocity in an OFDM inductive system, including:

[0008] Construct multiple perception node groups among all perception nodes.

[0009] Determine a candidate target set for the sensing node group. The candidate target set includes at least one candidate target, which is an unlocked target.

[0010] The sensing node groups belonging to the same candidate target are filtered to obtain the tracking node groups;

[0011] Convert candidate targets that match the tracking node group into locked targets;

[0012] Complete the pairing relationship between the remaining sensing nodes and the locked targets. The remaining sensing nodes are the other sensing nodes in the total number of sensing nodes, excluding the tracking node group.

[0013] If there are still candidate targets to lock, continue to build perception node groups and perform matching until the candidate targets to lock disappear;

[0014] The locked target is repositioned using distance measurement information from all sensing nodes.

[0015] In one possible implementation of the first aspect, a group of perception nodes belonging to the same candidate target is screened, including geometric relationship screening, distance screening and radial velocity screening, which are performed in sequence.

[0016] In one possible implementation of the first aspect, geometric relationship filtering of perception node groups belonging to the same candidate locking target includes:

[0017] Two sensing nodes are randomly selected from the sensing node group;

[0018] Calculate the geometric relationship between the two sensing nodes and the candidate locking targets corresponding to the sensing node group;

[0019] The sensing node groups that do not satisfy the geometric relationship are deleted;

[0020] In this process, geometric relationship calculations are performed on any two sensing nodes in the sensing node group.

[0021] In one possible implementation of the first aspect, distance filtering of a group of perception nodes belonging to the same candidate target includes:

[0022] The target locations of candidate locked targets are calculated using a group of sensing nodes filtered by geometric relationships, resulting in multiple target locations;

[0023] Calculate and normalize the sum of squared residuals of the distance to the target location;

[0024] The perception node groups belonging to the same candidate locked target are filtered based on the distance residual constraint relationship;

[0025] Among them, the sum of squared distance residuals of the remaining sensing node groups should be less than or equal to the set reference value, and the remaining sensing node groups are the set of sensing nodes that were not selected after filtering.

[0026] In one possible implementation of the first aspect, radial velocity filtering of a group of sensing nodes belonging to the same candidate target includes:

[0027] Construct a radial velocity coefficient matrix assuming the target position is fixed;

[0028] Least-square velocity estimation is performed using radial velocity measurement vectors;

[0029] Calculate the sum of squared velocity residuals and normalize them;

[0030] The sensing node groups belonging to the same candidate locked target are screened based on the radial velocity residual constraint relationship;

[0031] Among them, the radial velocity residual of the remaining sensing node group should be less than or equal to the set reference value, and the remaining sensing node group is the set of sensing nodes that were not selected after filtering.

[0032] In one possible implementation of the first aspect, completing the pairing relationship between the remaining sensing nodes and the locked targets includes:

[0033] Generate predicted bistatic distances to locked targets;

[0034] The remaining sensing nodes are filtered using geometric relationship filtering, distance filtering, and radial velocity filtering. The remaining sensing nodes are then paired with the locked targets to complete the matching relationship.

[0035] In one possible implementation of the first aspect, when there are multiple remaining sensing nodes, the joint residual of each remaining sensing node is calculated, and the remaining sensing node corresponding to the smallest joint residual is selected to complete the pairing relationship with the locked target.

[0036] Secondly, this application provides a multi-station, multi-target matching device for integrating radial velocity in an OFDM inductive system, comprising:

[0037] The sensing node group construction unit is used to construct sensing node groups among all sensing nodes, and there are multiple sensing node groups.

[0038] A candidate target set construction unit is used to determine the candidate target set of the perception node group. The candidate target set includes at least one candidate target, which is an unlocked target.

[0039] The filtering unit is used to filter the perception node groups belonging to the same candidate locked target to obtain the tracking node group;

[0040] The target conversion unit is used to convert candidate locked targets that match the tracking node group into locked targets;

[0041] The relationship completion unit is used to complete the pairing relationship between the remaining sensing nodes and the locked targets. The remaining sensing nodes are the other sensing nodes in the total number of sensing nodes except for the tracking node group.

[0042] The loop processing unit is used to continue building and matching the perception node group when there are still candidate targets to be locked, until the candidate targets disappear.

[0043] The relocation unit is used to relocate the locked target using distance measurement information from all sensing nodes.

[0044] Thirdly, this application provides a multi-station, multi-target matching system for integrating radial velocity in an OFDM sensing system, the system comprising:

[0045] One or more memories for storing instructions; and

[0046] One or more processors are configured to retrieve and execute the instructions from the memory to perform a multi-station, multi-target matching algorithm for fusion radial velocity in an OFDM inductive system as described in the first aspect and any possible implementation thereof.

[0047] Fourthly, this application provides a computer-readable storage medium, the computer-readable storage medium comprising:

[0048] When the program is run by the processor, the OFDM inductive system fusion radial velocity multi-station multi-target matching algorithm described in the first aspect and any possible implementation thereof is executed.

[0049] Fifthly, this application provides a computer program product, including program instructions, which, when run by a computing device, execute the multi-station, multi-target matching algorithm for fusion radial velocity in the OFDM inductive system as described in the first aspect and any possible implementation thereof.

[0050] Sixthly, this application provides a chip system including a processor for implementing the functions involved in the above aspects, such as generating, receiving, transmitting, or processing data and / or information involved in the multi-station multi-target matching algorithm for fusing radial velocity in the OFDM inductive integrated system described above.

[0051] This chip system can consist of chips or include chips and other discrete components.

[0052] In one possible design, the chip system also includes a memory for storing necessary program instructions and data. The processor and the memory can be decoupled and located on different devices, connected via wired or wireless means, or the processor and the memory can be coupled to the same device. Attached Figure Description

[0053] Figure 1This is a flowchart illustrating the steps of a multi-station, multi-target matching algorithm provided in this application.

[0054] Figure 2 This is a structural schematic diagram of a sensory integration system involved in this application.

[0055] Figure 3 This is a schematic diagram illustrating the geometric relationship between a target and two sensing nodes provided in this application. Detailed Implementation

[0056] The technical solutions in this application will be further described in detail below with reference to the accompanying drawings.

[0057] This application discloses a multi-station, multi-target matching algorithm for fusing radial velocity in an OFDM inductive integrated system. In some examples, the multi-station, multi-target matching algorithm for fusing radial velocity in an OFDM inductive integrated system disclosed in this application includes the following steps:

[0058] S101, Construct a sensing node group among all sensing nodes, and there are multiple sensing node groups;

[0059] S102, determine the candidate target set of the sensing node group, the candidate target set includes at least one candidate target, and the candidate target is an unlocked target;

[0060] S103, filter the perception node groups belonging to the same candidate locked target to obtain the tracking node group;

[0061] S104, convert the candidate target to be locked that matches the tracking node group into a locked target;

[0062] S105, complete the pairing relationship between the remaining sensing nodes and the locked targets. The remaining sensing nodes are the other sensing nodes in the total sensing nodes except for the tracking node group.

[0063] S106, if there are still candidate targets to be locked, continue to build the perception node group and perform matching until the candidate targets to be locked disappear;

[0064] S107, use distance measurement information from all sensing nodes to relocate the locked target.

[0065] First, it should be noted that please refer to [the relevant documentation / reference]. Figure 1 The integrated communication and sensing system involved in this application consists of three parts: one transmitting node, S≥3 single-antenna sensing nodes, and a data fusion center. The transmitting node is responsible for transmitting integrated communication and sensing signals, and the single-antenna sensing nodes complete the reception of communication signals and the detection of targets, while acquiring bi-station distance and radial velocity information.

[0066] The data fusion center is responsible for target localization. It is connected to the transmitting node and all single-antenna sensing nodes and can collect target distance and radial velocity information from each single-antenna sensing node.

[0067] A single-antenna receiving node with fewer than three stations cannot uniquely determine the two-dimensional location of the target, therefore, it is assumed here that... .

[0068] The launch node location is fixed as ;

[0069] No. The positions of the sensing nodes are: ;

[0070] The scene contains The actual positions and velocities of the moving targets are as follows: .

[0071] Each sensing station After target detection, a set of measurements is output: ,

[0072] in, For sensing station The target quantity obtained,

[0073] The data model for the two-station distance and measurement data is as follows:

[0074] ,

[0075] here With a mean of 0 and a variance of Gaussian noise; The data model for dual-station radial velocity measurement is as follows:

[0076] ,

[0077] here With a mean of 0 and a variance of Gaussian noise; Let be a unit vector of direction, defined as:

[0078] .

[0079] To reduce the computational load of the search, this application adopts a positioning scheme with three core sensing nodes.

[0080] In each iteration, the best three-sensor node is selected from the currently unassigned measurement set as the core station, and the target location candidate is constructed and solved based on the distance measurement of the core station.

[0081] In a real-world environment, there may be more than three stations. The three selected stations should be well-located, unobstructed, not in a straight line, and have a large coverage area. Here, we assume the three stations are... , and .

[0082] In step S101, a group of sensing nodes is first constructed among all the sensing nodes. There are multiple groups of sensing nodes. In some possible implementations, each group of sensing nodes includes three sensing nodes.

[0083] In step S102, a candidate target set for the sensing node group is determined. The candidate target set includes at least one candidate target, which is an unlocked target. Then, in step S103, sensing node groups belonging to the same candidate target are filtered to obtain a tracking node group, which is the core station mentioned above.

[0084] It should be noted that each sensing node will simultaneously sense multiple targets. If three sensing nodes detect... , and If there is a goal, then there is Such combinations. Clearly, if , and The values ​​are very large, especially when there are a large number of false alarms.

[0085] Therefore, in step S103, it is necessary to filter the perception node groups belonging to the same candidate target, which will result in a tracking node group.

[0086] Then, in step S104, the candidate target to be locked that matches the tracking node group is converted into a locked target. A locked target means that the target has been locked and can be continuously tracked in subsequent processes.

[0087] In step S105, it is necessary to complete the pairing relationship between the remaining sensing nodes and the locked targets. The remaining sensing nodes are the other sensing nodes in the total number of sensing nodes except for the tracking node group.

[0088] S106, if candidate targets still exist, continue building and matching the perception node group until the candidate targets disappear. At this point, assume there are K detected targets and the locked target is... If the target number is locked If the target is not found, the process returns to step S101 for further processing until all targets are locked. Finally, in step S107, the distance measurement information from all sensing nodes is used to relocate the locked targets.

[0089] The technical solution in this application can also be viewed as a cyclical processing scheme. Assuming the total number of targets is 10, 3 targets are locked in the first round of processing, 3 targets are locked in the second round of processing, and 4 targets are locked in the third round of processing. At this point, all targets are locked. In other words, in this application, the cutoff condition is that all targets are locked.

[0090] In some cases, groups of perception nodes belonging to the same candidate target are filtered using three methods: geometric relationship filtering, distance filtering, and radial velocity filtering. These three methods must be performed sequentially, as detailed below:

[0091] The method for geometric relationship filtering of perception node groups belonging to the same candidate target is as follows:

[0092] Two sensing nodes are randomly selected from the sensing node group;

[0093] Calculate the geometric relationship between the two sensing nodes and the candidate locking targets corresponding to the sensing node group;

[0094] The sensing node groups that do not satisfy the geometric relationship are deleted;

[0095] In this process, geometric relationship calculations are performed on any two sensing nodes in the sensing node group.

[0096] Specifically, there is a geometric relationship between the target and the two sensing nodes (the absolute value of the difference between any two sides of a triangle is always less than the third side). If the distance between sensing node 1 and the target is... The distance between sensing node 2 and the target is The distance between sensing node 1 and sensing node 2 is There will be ,like Figure 3 As shown.

[0097] Thus, it can be known that if sensing node 1 detects a target with a bistation distance of [missing information], The bi-station distance of the target detected by sensing node 2 is There must be

[0098] If the group of sensing nodes cannot satisfy this geometric relationship, it needs to be excluded.

[0099] The specific method for distance-based filtering of perception node groups belonging to the same candidate target is as follows:

[0100] The target locations of candidate locked targets are calculated using a group of sensing nodes filtered by geometric relationships, resulting in multiple target locations;

[0101] Calculate and normalize the sum of squared residuals of the distance to the target location;

[0102] The perception node groups belonging to the same candidate locked target are filtered based on the distance residual constraint relationship;

[0103] Among them, the sum of squared distance residuals of the remaining sensing node groups should be less than or equal to the set reference value, and the remaining sensing node groups are the set of sensing nodes that were not selected after filtering.

[0104] Specifically, the sensor node group, after being selected based on geometric relationships, first solves the three-station localization least squares problem:

[0105]

[0106] This model can be solved using the commonly used Gauss-Newton method. If the problem has a solution, the obtained target position is used. Calculate the sum of squared distance residuals:

[0107]

[0108] And normalize:

[0109]

[0110] Then, target selection is performed based on the distance residual constraint relationship. If the following conditions are met:

[0111]

[0112] Then this group of sensing nodes needs to be discarded. It is a pre-set distance residual threshold value, which can be obtained based on empirical values.

[0113] The specific method for radial velocity screening of sensing node groups belonging to the same candidate target is as follows:

[0114] Construct a radial velocity coefficient matrix assuming the target position is fixed;

[0115] Least-square velocity estimation is performed using radial velocity measurement vectors;

[0116] Calculate the sum of squared velocity residuals and normalize them;

[0117] The sensing node groups belonging to the same candidate locked target are screened based on the radial velocity residual constraint relationship;

[0118] Among them, the radial velocity residual of the remaining sensing node group should be less than or equal to the set reference value, and the remaining sensing node group is the set of sensing nodes that were not selected after filtering.

[0119] Specifically, in fixed Under the given conditions, construct the radial velocity coefficient matrix:

[0120]

[0121] in .

[0122] Least-square velocity estimation using radial velocity measurement vectors:

[0123]

[0124] This is a linear overdetermined LS model with 3 equations and 2 unknowns, and has a closed-form solution:

[0125]

[0126] Then calculate the sum of squared velocity residuals pairwise:

[0127]

[0128] And normalize:

[0129]

[0130] Target trimming is performed based on the radial velocity residual constraint relationship, if the following conditions are met:

[0131]

[0132] This group of sensing nodes needs to be discarded. It is a pre-set radial velocity residual threshold value, which can be obtained based on empirical values.

[0133] In some cases, the pairing of remaining sensing nodes with locked targets is completed as follows:

[0134] Generate predicted bistatic distances to locked targets;

[0135] The remaining sensing nodes are filtered using geometric relationship filtering, distance filtering, and radial velocity filtering. The remaining sensing nodes are then paired with the locked targets to complete the matching relationship.

[0136] Specifically, the predicted bibase distance is first generated:

[0137]

[0138] For each locked target, and each of the three core stations... Determine whether the geometric constraints are met; if not, discard the candidate.

[0139]

[0140] For each locked target, calculate the distance residual using the following formula and determine whether it meets the residual threshold. If it does not meet the threshold, discard the candidate.

[0141]

[0142] Constructing the predicted radial velocity of the remaining sensing nodes in

[0143]

[0144] For each locked target, calculate the radial velocity residual using the following formula and determine whether it meets the residual threshold. If it does not meet the threshold, discard the candidate.

[0145] .

[0146] In some possible approaches, when there are multiple remaining sensing nodes, the joint residual of each remaining sensing node is calculated, and the remaining sensing node corresponding to the smallest joint residual is selected to complete the pairing relationship with the locked target.

[0147] Specifically as follows:

[0148] For each locked target Calculate the normalized joint residuals.

[0149]

[0150] The minimum joint residual is selected from all candidate measurements to determine the pairing relationship between the sensing node and the locked target.

[0151]

[0152] This application also provides a multi-station, multi-target matching device for integrating radial velocity in an OFDM inductive system, comprising:

[0153] The sensing node group construction unit is used to construct sensing node groups among all sensing nodes, and there are multiple sensing node groups.

[0154] A candidate target set construction unit is used to determine the candidate target set of the perception node group. The candidate target set includes at least one candidate target, which is an unlocked target.

[0155] The filtering unit is used to filter the perception node groups belonging to the same candidate locked target to obtain the tracking node group;

[0156] The target conversion unit is used to convert candidate locked targets that match the tracking node group into locked targets;

[0157] The relationship completion unit is used to complete the pairing relationship between the remaining sensing nodes and the locked targets. The remaining sensing nodes are the other sensing nodes in the total number of sensing nodes except for the tracking node group.

[0158] The loop processing unit is used to continue building and matching the perception node group when there are still candidate targets to be locked, until the candidate targets disappear.

[0159] The relocation unit is used to relocate the locked target using distance measurement information from all sensing nodes.

[0160] Furthermore, the perception node groups belonging to the same candidate target are screened, including geometric relationship screening, distance screening, and radial velocity screening, which are performed in that order.

[0161] Furthermore, geometric relationship screening of perception node groups belonging to the same candidate target includes:

[0162] Two sensing nodes are randomly selected from the sensing node group;

[0163] Calculate the geometric relationship between the two sensing nodes and the candidate locking targets corresponding to the sensing node group;

[0164] The sensing node groups that do not satisfy the geometric relationship are deleted;

[0165] In this process, geometric relationship calculations are performed on any two sensing nodes in the sensing node group.

[0166] Furthermore, distance filtering for perception node groups belonging to the same candidate target includes:

[0167] The target locations of candidate locked targets are calculated using a group of sensing nodes filtered by geometric relationships, resulting in multiple target locations;

[0168] Calculate and normalize the sum of squared residuals of the distance to the target location;

[0169] The perception node groups belonging to the same candidate locked target are filtered based on the distance residual constraint relationship;

[0170] Among them, the sum of squared distance residuals of the remaining sensing node groups should be less than or equal to the set reference value, and the remaining sensing node groups are the set of sensing nodes that were not selected after filtering.

[0171] Furthermore, radial velocity filtering of perception node groups belonging to the same candidate target includes:

[0172] Construct a radial velocity coefficient matrix assuming the target position is fixed;

[0173] Least-square velocity estimation is performed using radial velocity measurement vectors;

[0174] Calculate the sum of squared velocity residuals and normalize them;

[0175] The sensing node groups belonging to the same candidate locked target are screened based on the radial velocity residual constraint relationship;

[0176] Among them, the radial velocity residual of the remaining sensing node group should be less than or equal to the set reference value, and the remaining sensing node group is the set of sensing nodes that were not selected after filtering.

[0177] Furthermore, completing the pairing relationship between the remaining sensing nodes and the locked targets includes:

[0178] Generate predicted bistatic distances to locked targets;

[0179] The remaining sensing nodes are filtered using geometric relationship filtering, distance filtering, and radial velocity filtering. The remaining sensing nodes are then paired with the locked targets to complete the matching relationship.

[0180] Furthermore, when there are multiple remaining sensing nodes, the joint residual of each remaining sensing node is calculated, and the remaining sensing node corresponding to the smallest joint residual is selected to complete the pairing relationship with the locked target.

[0181] In one example, the unit in any of the above devices may be one or more integrated circuits configured to implement the above methods, such as one or more application-specific integrated circuits (ASICs), or one or more digital signal processors (DSPs), or one or more field-programmable gate arrays (FPGAs), or a combination of at least two of these integrated circuit forms.

[0182] For example, when the units in the device can be implemented through a processing element scheduler, the processing element can be a general-purpose processor, such as a central processing unit (CPU) or other processor capable of calling programs. Alternatively, these units can be integrated together to form a system-on-a-chip (SOC).

[0183] In this application, various objects such as messages / information / devices / network elements / systems / apparatus / actions / operations / processes / concepts may be named. It is understood that these specific names do not constitute a limitation on the relevant objects. The names may be changed depending on the scenario, context, or usage habits. The understanding of the technical meaning of the technical terms in this application should be mainly determined from their functions and technical effects embodied / performed in the technical solution.

[0184] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0185] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0186] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0187] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0188] It should also be understood that in the various embodiments of this application, the terms "first," "second," etc., are merely to indicate that multiple objects are different. For example, a first time window and a second time window are only to indicate different time windows. They should not have any effect on the time windows themselves, and the aforementioned terms "first," "second," etc., should not impose any limitations on the embodiments of this application.

[0189] It should also be understood that, in the various embodiments of this application, unless otherwise specified or in case of logical conflict, the terms and / or descriptions between different embodiments are consistent and can be referenced by each other, and the technical features in different embodiments can be combined to form new embodiments according to their inherent logical relationships.

[0190] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a computer-readable storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned computer-readable storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0191] This application also provides a multi-station, multi-target matching system for integrating radial velocity in an OFDM sensing integrated system, the system comprising:

[0192] One or more memories for storing instructions; and

[0193] One or more processors are configured to retrieve and execute the instructions from the memory to perform the multi-station, multi-target matching algorithm for fused radial velocity in an OFDM inductive system as described above.

[0194] This application also provides a computer program product including instructions that, when executed, cause the terminal device and the network device to perform operations corresponding to the multi-station multi-target matching algorithm for the fused radial velocity of the OFDM inductive system described above.

[0195] This application also provides a chip system including a processor for implementing the functions involved in the above description, such as generating, receiving, transmitting, or processing the data and / or information involved in the multi-station multi-target matching algorithm for radial velocity fusion in the OFDM inductive integrated system described above.

[0196] This chip system can consist of chips or include chips and other discrete components.

[0197] The processor mentioned above can be a CPU, a microprocessor, an ASIC, or one or more integrated circuits that execute a program to control the method of transmitting the feedback information described above.

[0198] In one possible design, the chip system also includes a memory for storing necessary program instructions and data. The processor and the memory can be decoupled and located on different devices, connected via wired or wireless means to support the chip system in implementing the various functions described in the above embodiments. Alternatively, the processor and the memory can also be coupled to the same device.

[0199] Optionally, the computer instructions are stored in memory.

[0200] Optionally, the memory can be a storage unit within the chip, such as a register or cache. Alternatively, the memory can be a storage unit located outside the chip within the terminal, such as a ROM or other types of static storage devices that can store static information and instructions, such as RAM.

[0201] It is understood that the memory in this application may be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory.

[0202] Non-volatile memory can be ROM, programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory.

[0203] Volatile memory can be RAM, which is used as an external cache. There are many different types of RAM, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct memory bus random access memory.

[0204] The embodiments described in this specific implementation are preferred embodiments of this application and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the structure, shape and principle of this application should be covered within the scope of protection of this application.

Claims

1. A multi-station, multi-target matching method for integrating radial velocity in an OFDM sensing integrated system, characterized in that, include: Construct multiple perception node groups among all perception nodes. Determine a candidate target set for the sensing node group. The candidate target set includes at least one candidate target, which is an unlocked target. The sensing node groups belonging to the same candidate target are filtered to obtain the tracking node groups; Convert candidate targets that match the tracking node group into locked targets; Complete the pairing relationship between the remaining sensing nodes and the locked targets. The remaining sensing nodes are the other sensing nodes in the total number of sensing nodes, excluding the tracking node group. If there are still candidate targets to lock, continue to build perception node groups and perform matching until the candidate targets to lock disappear; The locked target is repositioned using distance measurement information from all sensing nodes; The sensing node groups belonging to the same candidate target are filtered, including geometric relationship filtering, distance filtering, and radial velocity filtering, and the geometric relationship filtering, distance filtering, and radial velocity filtering are performed in sequence. Geometric relationship screening for perception node groups belonging to the same candidate target includes: Two sensing nodes are randomly selected from the sensing node group; Calculate the geometric relationship between the two sensing nodes and the candidate locking targets corresponding to the sensing node group; The sensing node groups that do not satisfy the geometric relationship are deleted; In this process, geometric relationship calculations are performed on any two sensing nodes in the sensing node group. Distance filtering for perception node groups belonging to the same candidate target includes: The target locations of candidate locked targets are calculated using a group of sensing nodes filtered by geometric relationships, resulting in multiple target locations; Calculate and normalize the sum of squared residuals of the distance to the target location; The perception node groups belonging to the same candidate locked target are filtered based on the distance residual constraint relationship; Among them, the sum of squared distance residuals of the remaining sensing node groups should be less than or equal to the set reference value, and the remaining sensing node groups are the set of sensing nodes that were not selected after filtering. Radial velocity filtering for sensing node groups belonging to the same candidate target includes: Construct a radial velocity coefficient matrix assuming the target position is fixed; Least-square velocity estimation is performed using radial velocity measurement vectors; Calculate the sum of squared velocity residuals and normalize them; The sensing node groups belonging to the same candidate locked target are screened based on the radial velocity residual constraint relationship; Among them, the radial velocity residual of the remaining sensing node group should be less than or equal to the set reference value, and the remaining sensing node group is the set of sensing nodes that were not selected after filtering. Completing the pairing relationships between the remaining sensing nodes and the locked targets includes: Generate predicted bistatic distances to locked targets; The remaining sensing nodes are filtered using geometric relationship filtering, distance filtering, and radial velocity filtering. The remaining sensing nodes are then paired with the locked targets to complete the matching relationship. When there are multiple remaining sensing nodes, calculate the joint residual of each remaining sensing node and select the remaining sensing node corresponding to the smallest joint residual to complete the pairing relationship with the locked target.

2. A multi-station, multi-target matching device for integrating radial velocity in an OFDM sensing integrated system, characterized in that, include: The sensing node group construction unit is used to construct sensing node groups among all sensing nodes, and there are multiple sensing node groups. A candidate target set construction unit is used to determine the candidate target set of the perception node group. The candidate target set includes at least one candidate target, which is an unlocked target. The filtering unit is used to filter the perception node groups belonging to the same candidate locked target to obtain the tracking node group; The target conversion unit is used to convert candidate locked targets that match the tracking node group into locked targets; The relationship completion unit is used to complete the pairing relationship between the remaining sensing nodes and the locked targets. The remaining sensing nodes are the other sensing nodes in the total number of sensing nodes except for the tracking node group. The loop processing unit is used to continue building and matching the perception node group when there are still candidate targets to be locked, until the candidate targets disappear. The relocalization unit is used to relocalize the locked target using distance measurement information from all sensing nodes; The sensing node groups belonging to the same candidate target are filtered, including geometric relationship filtering, distance filtering, and radial velocity filtering, and the geometric relationship filtering, distance filtering, and radial velocity filtering are performed in sequence. Geometric relationship screening for perception node groups belonging to the same candidate target includes: Two sensing nodes are randomly selected from the sensing node group; Calculate the geometric relationship between the two sensing nodes and the candidate locking targets corresponding to the sensing node group; The sensing node groups that do not satisfy the geometric relationship are deleted; In this process, geometric relationship calculations are performed on any two sensing nodes in the sensing node group. Distance filtering for perception node groups belonging to the same candidate target includes: The target locations of candidate locked targets are calculated using a group of sensing nodes filtered by geometric relationships, resulting in multiple target locations; Calculate and normalize the sum of squared residuals of the distance to the target location; The perception node groups belonging to the same candidate locked target are filtered based on the distance residual constraint relationship; Among them, the sum of squared distance residuals of the remaining sensing node groups should be less than or equal to the set reference value, and the remaining sensing node groups are the set of sensing nodes that were not selected after filtering. Radial velocity filtering for sensing node groups belonging to the same candidate target includes: Construct a radial velocity coefficient matrix assuming the target position is fixed; Least-square velocity estimation is performed using radial velocity measurement vectors; Calculate the sum of squared velocity residuals and normalize them; The sensing node groups belonging to the same candidate locked target are screened based on the radial velocity residual constraint relationship; Among them, the radial velocity residual of the remaining sensing node group should be less than or equal to the set reference value, and the remaining sensing node group is the set of sensing nodes that were not selected after filtering. Completing the pairing relationships between the remaining sensing nodes and the locked targets includes: Generate predicted bistatic distances to locked targets; The remaining sensing nodes are filtered using geometric relationship filtering, distance filtering, and radial velocity filtering. The remaining sensing nodes are then paired with the locked targets to complete the matching relationship. When there are multiple remaining sensing nodes, calculate the joint residual of each remaining sensing node and select the remaining sensing node corresponding to the smallest joint residual to complete the pairing relationship with the locked target.

3. A multi-station, multi-target matching system integrating radial velocity in an OFDM inductive system, characterized in that, The system includes: One or more memories for storing instructions; and One or more processors are configured to retrieve and execute the instructions from the memory to perform the multi-station, multi-target matching method for fusing radial velocity in an OFDM inductive system as described in claim 1.

4. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes: When the program is run by the processor, the multi-station, multi-target matching method for fusing radial velocity in an OFDM integrated sensing system as described in claim 1 is executed.

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