A distributed radar angle estimation method and device

Abstract

The application provides a distributed radar angle estimation method and device, more than two radar signals are acquired, and independent processing is performed respectively to obtain corresponding first data; all the first data are acquired, and centralized correlation processing is performed to obtain second data; the angle of each radar is estimated based on the second data to obtain corresponding initial angle estimation values; a distributed radar observation model is constructed based on the second data and all the initial angle estimation values, and iterative processing is performed to obtain final angle estimation values. Based on a target-driven self-calibration mechanism, high-precision angle estimation of a distributed radar system is realized without accurate time and phase synchronization, multiple radar nodes do not need to be high-precision time and phase synchronized, no additional synchronization hardware or complex calibration process is needed, and phase alignment between the radar nodes can be realized only through an algorithm, so that the system design complexity and cost are significantly reduced, and the engineering realizability is improved.

Description

Technical Field

[0001] This invention relates to the field of radar signal processing technology, and in particular to a distributed radar angle estimation method and apparatus. Background Technology

[0002] With the rapid development of advanced driver assistance systems and autonomous driving technologies, vehicle-mounted millimeter-wave radar, as one of the core environmental perception sensors, is increasingly widely used, playing an irreplaceable role, especially in key tasks such as target detection, speed measurement, and angle estimation. Accurate and reliable angle estimation is the foundation for achieving functions such as target localization, lane keeping, and collision warning.

[0003] Currently, vehicle radar systems are mainly divided into single-radar architecture and distributed multi-radar architecture.

[0004] In a single radar system, its angular resolution is primarily determined by the array aperture. However, due to limitations in vehicle installation space, design constraints, and cost control, the antenna array size of a single radar cannot be significantly increased, resulting in an inherent bottleneck in its angular resolution. Under this finite aperture constraint, existing angle estimation methods all face challenges.

[0005] The angular resolution of a single radar system is limited. Traditional beamforming methods based on Fast Fourier Transform (FFT) have low angular resolution, making it difficult to distinguish multiple targets with similar spatial angles. While super-resolution algorithms, such as Multiple Signal Classification (MUSIC) and Rotation Invariant Subspace (ESPRIT), can improve resolution to some extent, their performance is still limited by the effective aperture of the array and they are quite sensitive to signal-to-noise ratio and model errors. Furthermore, in real-world traffic scenarios, these methods often encounter difficulties in distinguishing adjacent or weakly reflective targets (such as pedestrians and non-motorized vehicles), leading to decreased angle estimation accuracy or even failure.

[0006] To overcome the aperture limitations of single radar systems, existing technologies have proposed using distributed radar systems. This involves deploying multiple spatially diverse radar nodes at different locations on the vehicle (such as the front bumper, roof, and sides), and then fusing data to form a larger virtual array aperture, theoretically improving angular resolution. However, distributed radar systems suffer from synchronization difficulties, specifically as follows:

[0007] Each radar node is typically equipped with an independent clock and local oscillator, making it difficult to achieve precise time and phase synchronization between nodes. Unknown phase offsets exist between nodes, disrupting the array manifold structure. Direct coherent fusion of data from multiple nodes is also challenging.

[0008] In the absence of precise synchronization, the performance of traditional angle estimation methods degrades significantly, specifically as follows:

[0009] FFT-like methods rely on regular, phase-coherent array structures, making them difficult to apply directly to distributed, incoherent nodes.

[0010] Subspace methods such as MUSIC are highly sensitive to the accuracy of array manifolds. Unknown phase biases between nodes can directly lead to manifold mismatch, resulting in severe angle estimation errors.

[0011] The ESPRIT method relies on the shift-invariant structure of the array. Phase incoherence in distributed systems disrupts this structure, rendering the algorithm ineffective.

[0012] Some existing solutions attempt to achieve node coherence by adding high-precision synchronization hardware (such as shared local oscillator, fiber optic synchronization, high-precision clock distribution network), but this significantly increases the complexity, power consumption and manufacturing cost of the system.

[0013] Therefore, there is an urgent need for a distributed radar angle estimation method and device to improve the above problems. Summary of the Invention

[0014] The purpose of this invention is to provide a distributed radar angle estimation method and apparatus that can achieve coherent fusion of distributed data and high-resolution angle estimation.

[0015] In a first aspect, the present invention provides a distributed radar angle estimation method, comprising the steps of: acquiring two or more radar signals and processing them independently to obtain corresponding first data; acquiring all the first data and performing centralized correlation processing to obtain second data; estimating the angle of each radar based on the second data to obtain corresponding initial angle estimates; constructing a distributed radar observation model based on the second data and all the initial angle estimates, and performing iterative processing to obtain a final angle estimate.

[0016] Optionally, the independent processing includes one or more of the following: range-to-fast Fourier transform, Doppler-to-fast Fourier transform, and constant false alarm rate detection; and / or the first data is range-Doppler data.

[0017] Optionally, acquiring all the first data and performing centralized correlation processing to obtain the second data includes: performing correlation processing on all radars based on all the first data, and filtering out targets that meet the set conditions as the second data; the targets that meet the set conditions include one or more of the following: targets with consistent or similar range and Doppler information in different radar nodes, targets that appear as isolated strong targets in their respective radars, and targets that exhibit single-peak characteristics in single radar angle estimation and whose angle single-peak positions corresponding to different radar nodes are similar.

[0018] Optionally, the angle of each radar is estimated based on the second data to obtain the corresponding initial angle estimate, including: performing spatial fast Fourier transform estimation, fine search optimization, and cross-node consistency processing on the angle of each radar based on the second data to obtain the corresponding initial angle estimate.

[0019] Optionally, constructing a distributed radar observation model based on the second data and all initial angle estimates, and performing iterative processing to obtain the final angle estimate includes: constructing a distributed radar observation model and a loss function based on the second data and all initial angle estimates; using the initial angle estimates as initial values ​​to iteratively optimize the loss function to obtain third data; performing phase calibration and data alignment based on the third data, and then performing joint angle estimation to obtain the final angle estimate; and / or the final angle estimate includes estimates of the target azimuth and elevation angles.

[0020] Optionally, the distributed radar observation model is:

[0021]

[0022] in, For the first The radar is for the first The observed signals of each target; For the first Unknown phase offset of a radar; For the first The array response of each radar at a corresponding angle; or, the loss function is:

[0023]

[0024] in, For each target angle; Phase offset of each node; or, the joint angle estimation can be performed using the MUSIC method or the ESPRIT method.

[0025] Secondly, the present invention provides a distributed radar angle estimation device, which includes modules / units for performing any of the possible design methods described in the first aspect above. These modules / units can be implemented in hardware or by hardware executing corresponding software.

[0026] Thirdly, the present invention provides an electronic device including a memory and a processor, wherein the memory stores a program executable on the processor, and when the program is executed by the processor, the electronic device implements a method for performing any of the possible designs described above.

[0027] Fourthly, the present invention provides a readable storage medium storing a program, which, when executed, implements a method of any possible design of any of the above aspects.

[0028] Fifthly, the present invention provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0029] The beneficial effects of the method of this invention are as follows: It acquires two or more radar signals and processes them independently to obtain corresponding first data; it acquires all the first data and performs centralized correlation processing to obtain second data; it estimates the angle of each radar based on the second data to obtain corresponding initial angle estimates; it constructs a distributed radar observation model based on the second data and all initial angle estimates, and performs iterative processing to obtain the final angle estimate. Based on the target-driven self-calibration mechanism, high-precision angle estimation of a distributed radar system is achieved without the need for precise time and phase synchronization. It eliminates the need for high-precision time and phase synchronization of multiple radar nodes, does not rely on additional synchronization hardware or complex calibration processes, and achieves phase alignment between radar nodes solely through the algorithm. This significantly reduces system design complexity and cost, and improves engineering feasibility. Attached Figure Description

[0030] Figure 1 A flowchart illustrating a distributed radar angle estimation method provided in an embodiment of the present invention;

[0031] Figure 2 This is a schematic diagram of the structure of a distributed radar angle estimation device provided in an embodiment of the present invention;

[0032] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0033] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions in the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without inventive effort are within the scope of protection of this invention. Unless otherwise defined, the technical or scientific terms used herein should have the ordinary meaning understood by those skilled in the art. The terms "comprising" and similar expressions used herein mean that the element or object preceding the word covers the element or object listed following the word and its equivalents, but do not exclude other elements or objects.

[0034] The technical solutions of the embodiments of the present invention will be described below with reference to the accompanying drawings. In the description of the embodiments of the present invention, the terminology used in the following embodiments is for the purpose of describing specific embodiments only and is not intended to limit the present invention. The singular expressions “a,” “the,” “the,” and “this” are intended to also include expressions such as “one or more,” unless the context clearly indicates otherwise. It should also be understood that in the following embodiments of the present invention, “at least one” and “one or more” refer to one or more (including two). The term “and / or” is used to describe the relationship between related objects, indicating that three relationships can exist; for example, A and / or B can represent: A alone, A and B simultaneously, or B alone, where A and B can be singular or plural. The character “ / ” generally indicates that the preceding and following related objects are in an “or” relationship.

[0035] References to "one embodiment" or "some embodiments" in this specification mean that a particular feature, structure, or characteristic described in connection with that embodiment is included in one or more embodiments of the invention. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," and "in still other embodiments" appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including, but not limited to," unless otherwise specifically emphasized. The term "connection" includes both direct and indirect connections, unless otherwise stated. "First" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated.

[0036] In embodiments of the present invention, "exemplarily" or "for example" are used to indicate that they are examples, illustrations, or descriptions. Any embodiment or design described as "exemplarily" or "for example" in embodiments of the present invention should not be construed as being more preferred or advantageous than other embodiments or design solutions. Rather, the use of "exemplarily" or "for example" is intended to present the relevant concepts in a specific manner.

[0037] like Figure 1 As shown, this invention provides a distributed radar angle estimation method, including the following steps:

[0038] S101: Acquire two or more radar signals and process them independently to obtain the corresponding first data.

[0039] In some embodiments, the independent processing includes one or more of range-to-fast Fourier transform, Doppler-to-fast Fourier transform, and constant false alarm rate detection.

[0040] In other embodiments, the first data is distance-Doppler data.

[0041] S102, obtain all the first data, and perform centralized correlation processing to obtain the second data.

[0042] In some embodiments, acquiring all the first data and performing centralized correlation processing to obtain the second data includes: performing correlation processing on all radars based on all the first data, and filtering out targets that meet the set conditions as the second data; the targets that meet the set conditions include one or more of the following: targets with consistent or similar range and Doppler information in different radar nodes, targets that appear as isolated strong targets in their respective radars, and targets that exhibit single-peak characteristics in single radar angle estimation and whose angle single-peak positions are similar across different radar nodes. The "single-peak position similar" condition is used to ensure that the selected target corresponds to the same physical target in different radar observations, thereby avoiding mismatches caused by multi-target or multipath interference.

[0043] S103, based on the second data, the angle of each radar is estimated to obtain the corresponding initial angle estimate.

[0044] In some embodiments, estimating the angle of each radar based on the second data to obtain the corresponding initial angle estimate includes: performing spatial fast Fourier transform estimation, fine search optimization, and cross-node consistency processing on the angle of each radar based on the second data to obtain the corresponding initial angle estimate.

[0045] S104. Based on the second data and all the initial angle estimates, a distributed radar observation model is constructed and iteratively processed to obtain the final angle estimate.

[0046] In some embodiments, constructing a distributed radar observation model based on the second data and all initial angle estimates, and performing iterative processing to obtain the final angle estimate includes: constructing a distributed radar observation model and a loss function based on the second data and all initial angle estimates; iteratively optimizing the loss function using the initial angle estimates as initial values ​​to obtain third data; and performing joint angle estimation based on the third data after phase calibration and data alignment to obtain the final angle estimate.

[0047] In other embodiments, the final angle estimate includes estimates of the target azimuth and pitch angles.

[0048] In some embodiments, the distributed radar observation model is:

[0049]

[0050] in, For the first The radar is for the first The observed signals of each target; For the first Unknown phase offset of a radar; For the first The array response of each radar at a corresponding angle.

[0051] In other embodiments, the loss function is:

[0052]

[0053] in, For each target angle; Phase offset at each node.

[0054] In some other embodiments, the joint angle estimation may employ the MUSIC method or the ESPRIT method.

[0055] The advantages of this invention are that, based on the target-driven self-calibration mechanism, high-precision angle estimation of a distributed radar system is achieved without the need for precise time and phase synchronization. It does not require high-precision time and phase synchronization of multiple radar nodes, does not rely on additional synchronization hardware or complex calibration processes, and can achieve phase alignment between radar nodes solely through algorithms. This significantly reduces system design complexity and cost, and improves engineering feasibility.

[0056] To facilitate understanding, this embodiment further elaborates on the specific implementation process of the above method in conjunction with a specific application scenario. Taking vehicle-mounted distributed radar as an example, the specific steps include:

[0057] (1) System structure and data acquisition

[0058] Multiple radar nodes are deployed on the vehicle platform. Each radar node operates independently, transmitting and receiving signals separately, and processing them independently through its own signal processing unit (such as DSP or GPU), including:

[0059] Distance-oriented FFT;

[0060] Doppler to FFT;

[0061] Constant False Alarm Rate (CFAR) detection;

[0062] Obtain the range-Doppler data (i.e., the first data) corresponding to each radar node.

[0063] The processing results of each radar node are transmitted to the central processing unit via the vehicle communication bus (such as CAN, Ethernet, etc.) for subsequent joint processing (i.e., correlation processing).

[0064] (2) Target selection and cross-node association

[0065] In the central processing unit, the detection results of each radar node are correlated, and targets that meet the following conditions are selected as the second data:

[0066] The range and Doppler information are consistent or similar across different radar nodes;

[0067] They appear as isolated, strong targets in their respective radars;

[0068] It exhibits a single-peak characteristic in single radar angle estimation, and the single-peak positions of the angles corresponding to different radar nodes are close;

[0069] Among them, "single-peak position proximity" is used to ensure that the selected target corresponds to the same physical target in different radar observations, thereby avoiding mismatch caused by multi-target or multipath interference.

[0070] Through the above screening, a set of high-confidence targets is obtained, which can be used for subsequent phase offset estimation and system self-calibration.

[0071] (3) Coarse estimation of single radar angle

[0072] For the selected targets, the angle is roughly estimated within each radar node based on its virtual array to obtain the corresponding initial angle estimate.

[0073] Let the first The array observation vectors of the radar nodes are:

[0074]

[0075] in, This represents the number of virtual array channels for this radar node.

[0076] (3.1) Coarse-grid spatial FFT estimation

[0077] First, in the preset angle grid Construct the array response vector above:

[0078]

[0079] in, For the first The first radar node The position of each array element.

[0080] Computing the spatial spectrum:

[0081]

[0082] Search for the location of the maximum value on the coarse grid:

[0083]

[0084] (3.2) Fine-grained search optimization

[0085] Based on rough estimate results Centered on the grid, construct a finer angular mesh within its neighborhood. And perform a local search:

[0086]

[0087] Get the first Coarse angle estimation results for each radar node.

[0088] (3.3) Cross-node consistency processing

[0089] Because the observation angles of different radar nodes differ, a weighted average is taken from the estimation results of each node to obtain a unified initial angle estimate:

[0090]

[0091] Among them, weight It can be determined based on the signal strength or signal-to-noise ratio of the corresponding target, for example:

[0092]

[0093] Through the above processing, the initial angle estimate of the target is obtained for subsequent joint optimization.

[0094] (4) Joint estimation of phase offset based on target

[0095] Based on the second data and all the initial angle estimates, a distributed radar observation model is constructed:

[0096]

[0097] in: For the first The radar is for the first The observed signals of each target;

[0098] For the first Unknown phase offset of each radar node;

[0099] For the first The array response of each radar at a corresponding angle;

[0100] Considering that the rough estimate of the target angle may be inaccurate, the following loss function is constructed to estimate the node phase offset:

[0101]

[0102] Using the coarse angle estimate obtained in step (3) as the initial value, the above loss function is iteratively optimized and solved jointly:

[0103] Target angles ;

[0104] Phase offset of each node ;

[0105] Specifically, an alternating optimization approach can be adopted:

[0106] 1. Fixed phase offset Optimize target angle

[0107] 2. Fix the target angle Update phase bias

[0108] Through an iterative process, the loss function gradually converges, thereby obtaining consistent phase calibration results and more accurate target angle estimation.

[0109] (5) Phase calibration and data alignment

[0110] The estimated node phase offset is used to calibrate the observation data of each radar node:

[0111]

[0112] This operation maps the distributed observation data, which originally had phase mismatch, to a unified reference coordinate system, thereby restoring the equivalent array structure.

[0113] (6) Joint angle estimation

[0114] After phase calibration, data from multiple radar nodes are fused to construct an equivalent large-aperture array. At this point, all targets in the range-Doppler cells are mapped to a unified reference coordinate system. We can then process each node using a high-resolution angle estimation algorithm, including but not limited to:

[0115] MUSIC method;

[0116] ESPRIT method;

[0117] This allows for the acquisition of highly accurate target azimuth and elevation angle estimates.

[0118] This invention addresses the problem of high-precision angle estimation by vehicle-mounted distributed radar in the absence of accurate time and phase synchronization. The key lies in:

[0119] (1) Distributed radar processing framework without precise time synchronization. Under the condition that multiple radar nodes are not precisely synchronized in time and phase, the unknown phase offset between nodes is introduced into a unified model by performing structured modeling of the observation data, thereby avoiding reliance on high-precision synchronization hardware and realizing low-cost deployment of distributed radar systems.

[0120] (2) Single-target screening mechanism based on multiple constraints. By using multiple constraints such as distance, Doppler consistency, unimodality of angle spectrum and proximity of angle peak positions of different nodes, a set of targets that meet the characteristics of a single target is screened for use in the subsequent self-calibration process, thereby reducing the impact of multi-target and multipath interference on the system from the source.

[0121] (3) Target-driven node phase offset self-calibration method. Using the selected target set, the relationship between observation data and array response is constructed. By combining information from multiple targets, the phase offset of each radar node is estimated, thus realizing a self-calibration process without the need for external reference signals.

[0122] (4) Joint optimization mechanism of angle and phase based on loss function. A joint loss function containing target angle and node phase offset is constructed, and the coarse angle estimate is used as the initial value. The solution is iteratively solved by alternating optimization method to achieve coordinated convergence of angle and phase parameters and improve estimation accuracy and stability.

[0123] (5) Unified processing mechanism for extending calibration results to the entire data. After completing the system phase calibration using strong targets, the calibration results are applied to all range-Doppler units to achieve unified high-precision angle estimation for weak targets and targets in complex scenes, thereby improving the overall detection performance of the system.

[0124] The advantage of this invention is that by introducing a target-driven self-calibration mechanism, high-precision angle estimation of a distributed radar system is achieved without the need for precise time and phase synchronization, resulting in the following effects and advantages:

[0125] (1) No need for precise time synchronization, reducing system complexity. This invention does not require high-precision time and phase synchronization of multiple radar nodes, does not rely on additional synchronization hardware or complex calibration processes, and achieves phase alignment between nodes only through algorithms, thereby significantly reducing system design complexity and cost, and improving engineering feasibility.

[0126] (2) Improve the equivalent array aperture and enhance angular resolution. By self-calibrating and fusing data from multiple radar nodes, an equivalent large-aperture array is constructed, which significantly improves angular resolution compared to a single radar system, enabling adjacent targets to be effectively distinguished.

[0127] (3) Achieving coherent fusion of distributed radar data. Under asynchronous conditions, this invention maps the observation data of each node to a unified reference coordinate system through phase self-calibration, thereby achieving coherent processing of distributed radar data and making full use of multi-node information.

[0128] (4) Improve the accuracy and stability of angle estimation. By constructing a joint optimization model of angle and phase and combining it with multi-objective information for estimation, the accuracy and robustness of parameter estimation are improved, and the impact of noise and initial error on the results is reduced.

[0129] (5) Achieving enhanced detection of weak targets driven by strong targets. This invention utilizes strong targets to complete system self-calibration and applies the calibration results to all range-Doppler units, thereby enhancing weak targets under a unified array structure and improving the detection capability and angle estimation accuracy of weak targets.

[0130] (6) Adapt to complex vehicle environments and improve system robustness. Under conditions of multiple targets, multiple paths and non-ideal synchronization, the present invention effectively suppresses interference and mismatch through target selection and joint optimization mechanisms, thereby improving the stability and reliability of the system in complex scenarios.

[0131] (7) The computational complexity is controllable and suitable for real-time implementation in vehicles. The main computations of this invention are focused on target selection, phase estimation and small-scale parameter optimization processes, which can be implemented in real time on vehicle-mounted DSP or GPU platforms to meet the needs of practical applications.

[0132] like Figure 2 As shown, based on the above method, the present invention provides a distributed radar angle estimation device, comprising: a sub-processing unit 201, used to acquire two or more radar signals and process them independently to obtain corresponding first data; a central processing unit 202, used to acquire all the first data and perform centralized correlation processing to obtain second data; an estimation unit 203, used to estimate the angle of each radar based on the second data to obtain corresponding initial angle estimates; and a construction unit 204, used to construct a distributed radar observation model based on the second data and all initial angle estimates, and perform iterative processing to obtain a final angle estimate.

[0133] It should be understood that all relevant content of each step involved in the above method embodiments can be referenced to the functional description of the corresponding functional module, and will not be repeated here. Furthermore, the use of suffixes such as "module," "component," or "unit" to represent elements is merely for the purpose of illustrative purposes and has no specific meaning in itself. Therefore, "module," "component," or "unit" can be used interchangeably. Terminals can be implemented in various forms. For example, the terminals described in this invention may include mobile terminals such as mobile phones, tablets, laptops, handheld computers, personal digital assistants (PDAs), portable media players (PMPs), navigation devices, wearable devices, smart bracelets, pedometers, etc., as well as fixed terminals such as digital TVs and desktop computers. The following description will use mobile terminals as examples; those skilled in the art will understand that, in addition to elements specifically designed for mobile purposes, the construction according to embodiments of the present invention can also be applied to fixed-type terminals.

[0134] In other embodiments of the present invention, an electronic device 300 is disclosed, such as... Figure 3 As shown, the device may include: one or more processors 301; memory 302; display 303; one or more application programs (not shown); and one or more computer programs 304. These devices can be connected via one or more communication buses 305. The one or more computer programs 304 are stored in the memory 302 and configured to be executed by the one or more processors 301. The one or more computer programs 304 include instructions that can be used to perform actions such as... Figure 1 Each step in the corresponding embodiment.

[0135] Processor 301 can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0136] The memory 302 can be an internal storage unit of the electronic device 300, such as a hard disk or RAM of the electronic device 300. The memory 302 can also be an external storage device of the electronic device 300, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, or FlashCard equipped on the electronic device 300. Furthermore, the memory 302 can include both internal and external storage units of the electronic device 300. The memory 302 is used to store computer programs and other programs and data required by the electronic device. The memory 302 can also be used to temporarily store data that has been output or will be output.

[0137] The computer program 304 can be divided into one or more modules / units. The one or more modules / units can be a series of computer program instruction segments that can perform a specific function. The instruction segments are used to describe the execution process of the computer program 304 in the electronic device 300.

[0138] In addition to the above-described structure, those skilled in the art will understand that Figure 3 This is merely an example of electronic device 300 and does not constitute a limitation on electronic device 300. Electronic device 300 may include more or fewer components than shown, or combine certain components, or different components. For example, electronic device may also include input / output devices, network access devices, buses, etc.

[0139] Those skilled in the art will understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the functions described above can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this invention. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0140] Based on the above embodiments, the present invention also discloses a computer-readable storage medium having at least one computer program stored thereon, wherein the computer program, when executed by a processor, implements the methods described in the foregoing embodiments.

[0141] Those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing a processor. The program can be stored in a computer-readable storage medium, which is a non-transitory medium, such as random access memory, read-only memory, flash memory, hard disk, solid-state drive, magnetic tape, floppy disk, optical disk, and any combination thereof. The storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. This available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., digital video disc (DVD)), or a semiconductor medium (e.g., solid-state drive (SSD)).

[0142] The descriptions of the processes or structures corresponding to the above figures each have their own emphasis. For parts of a process or structure that are not described in detail, please refer to the relevant descriptions of other processes or structures.

[0143] Although the embodiments of the present invention have been described in detail above, it will be apparent to those skilled in the art that various modifications and variations can be made to these embodiments. The above descriptions are merely embodiments of the present invention and do not limit the patent scope of the present invention. However, it should be understood that such modifications and variations fall within the scope and spirit of the present invention. Moreover, the present invention described herein may have other embodiments and can be implemented or realized in various ways. All equivalent transformations made based on the description and drawings of the present invention, or direct or indirect applications in related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A distributed radar angle estimation method, characterized in that, Including the following steps: Acquire two or more radar signals and process them independently to obtain the corresponding first data; Obtain all the first data and perform centralized correlation processing to obtain the second data; Based on the second data, the angle of each radar is estimated to obtain the corresponding initial angle estimate; Based on the second data and all the initial angle estimates, a distributed radar observation model is constructed and iteratively processed to obtain the final angle estimate. Based on the second data and all the initial angle estimates, a distributed radar observation model is constructed and iteratively processed to obtain the final angle estimates, including: Based on the second data and all the initial angle estimates, a distributed radar observation model and loss function are constructed. The loss function is iteratively optimized using the initial angle estimate as the initial value to obtain the third data. After performing phase calibration and data alignment based on the third data, joint angle estimation is performed to obtain the final angle estimate. The distributed radar observation model is as follows: in, For the first The radar is for the first The observed signals of each target; For the first Unknown phase offset of a radar; For the first The array response of each radar at a corresponding angle; The loss function is: in, For each target angle; Phase offset at each node.

2. The method according to claim 1, characterized in that, The independent processing includes one or more of the following: range-to-fast Fourier transform, Doppler-to-fast Fourier transform, and constant false alarm rate detection. And / or the first data is distance-Doppler data.

3. The method according to claim 1, characterized in that, All the first data is acquired and centrally correlated to obtain the second data, which includes: Based on all the first data, all radars are correlated and filtered to select targets that meet the set conditions as the second data. The targets defined by the conditions include one or more of the following: targets with consistent or similar range and Doppler information in different radar nodes; targets that appear as isolated strong targets in their respective radars; and targets that exhibit single-peak characteristics in single-radar angle estimation and whose angular single-peak positions are similar across different radar nodes.

4. The method according to claim 1, characterized in that, Based on the second data, the angle of each radar is estimated to obtain the corresponding initial angle estimates, including: Based on the second data, spatial fast Fourier transform estimation, fine search optimization, and cross-node consistency processing are performed on the angle of each radar to obtain the corresponding initial angle estimate.

5. The method according to any one of claims 1-4, characterized in that, The final angle estimate includes estimates of the target azimuth and elevation angles.

6. The method according to claim 1, characterized in that, The joint angle estimation can be performed using the MUSIC method or the ESPRIT method.

7. A distributed radar angle estimation device, used in the method according to any one of claims 1-6, characterized in that, include: The sub-processing unit is used to acquire two or more radar signals and process them independently to obtain the corresponding first data. The central processing unit is used to acquire all the first data and perform centralized correlation processing to obtain the second data; An estimation unit is used to estimate the angle of each radar based on the second data to obtain the corresponding initial angle estimate. The construction unit is used to construct a distributed radar observation model based on the second data and all the initial angle estimates, and to perform iterative processing to obtain the final angle estimate.

8. An electronic device, characterized in that, The device includes a memory and a processor, wherein the memory stores a program that can run on the processor, and when the program is executed by the processor, causes the electronic device to perform the method of any one of claims 1-6.

9. A readable storage medium storing a program, characterized in that, When the program is executed, it implements the method of any one of claims 1-6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1-6.

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