A minimum path two-dimensional pairing method based on distributed radar

Abstract

The application discloses a minimum path two-dimensional pairing method based on a distributed radar, and comprises the following steps: acquiring path information of a distributed radar platform signal, selecting effective path data from the signal path to form effective path information; the type of the signal path comprises a self-path and a mutual path; filtering N+1 pairing paths from the effective path information to form a pairing path data set, wherein N is the number of radars in the distributed radar platform; determining a distance dimension-Doppler dimension determination condition; traversing the pairing path data set, extracting pairing paths of the determination condition to form a pairing success path data set; traversing the pairing success path data set, extracting targets corresponding to the pairing success paths to generate a pairing result; and screening the pairing result to generate a minimum path two-dimensional pairing result set. According to the technical scheme, the problem of multi-path fusion during distributed radar detection can be solved, and the calculation complexity is reduced.

Description

Technical Field

[0001] This invention relates to the field of distributed radar detection enhancement technology, and more specifically, to a minimum path two-dimensional pairing method based on distributed radar. Background Technology

[0002] Distributed radar is a radar system that uses multiple radars to collaboratively detect targets. Each radar transmits orthogonal signals and receives signals from all other radars. The detected information is then fused to improve the overall detection performance of the system. A distributed radar system consists of multiple radar stations located at various points in space. Each radar transmits and receives signals simultaneously, and the received target information is fused. Because the radars are located in different azimuths, the transmitted signals can travel along different propagation paths to detect targets from different directions, and the target echoes can also travel along different propagation paths to reach each radar, thus achieving spatial diversity gain. This multi-transmit and multi-receive approach effectively combats multipath fading, reduces mutual interference between signals, and improves system performance.

[0003] For distributed radar, the echo signals from different paths can be enhanced by methods such as beamforming, matched filtering, and moving target detection (MTD). However, echo signals from different paths cannot be directly superimposed to obtain diversity gain. Therefore, it is necessary to perform target extraction and pairing processing on the RV spectrum after MTD, remove high-amplitude noise, and match the position and velocity information of multiple targets.

[0004] When the performance of different distributed radar platforms is inconsistent, the success of pairing is significantly affected by low-performance platforms. Therefore, a pairing scheme is needed that can screen for low-performance platforms, reduce their usage, and implement a minimum path selection criterion during pairing to reduce computational complexity and improve radar pairing efficiency. Summary of the Invention

[0005] To achieve the above objectives, this application provides a minimum path two-dimensional pairing method based on distributed radar, comprising the following steps:

[0006] The path information of the distributed radar platform signal is obtained, and valid path data is selected from the signal path to form valid path information; the types of the signal path include self-path and mutual path.

[0007] N+1 paired paths are selected from the valid path information to form a paired path dataset, where N is the number of radars in the distributed radar platform;

[0008] Determine the criteria for distance dimension-Doppler dimension determination;

[0009] Traverse the paired path dataset and extract the paired paths that meet the distance dimension-Doppler dimension determination criteria to form a successfully paired path dataset; the successfully paired path dataset includes path, amplitude, distance cell, and Doppler cell information.

[0010] Traverse the dataset of successfully paired paths, extract the targets corresponding to the successfully paired paths, delete invalid data without corresponding targets, and generate the pairing results;

[0011] The pairing results are filtered to generate a minimum path two-dimensional pairing result set.

[0012] In the process of obtaining path information of signals from a distributed radar platform, matched filtering is performed on the radar-generated signals, and MTD processing is performed on the pulse compression signals to obtain the RV spectrum.

[0013] The selection of valid path data from the signal path is achieved through a path selection matrix A; the path selection matrix A is represented as A = [x1 x2 ... x ... j ] T ,and: Where j is the path index, i is the platform index, and n <N, The distance index for path j relative to platform i is selected;

[0014] Based on the path selection matrix A, the path distance combination b is defined as follows: and Define the path Doppler combination as follows: and

[0015] Furthermore, the range selection index is implemented based on a matrix of range vector r, which consists of the one-way path distance from the target to the corresponding radar, expressed as: r = [r1 r2 r...] i r n ] T , where r i This indicates the distance from radar i to the target.

[0016] Furthermore, N+1 matching paths are selected from the valid path information, including:

[0017] Non-coherent pre-overlay processing is performed on the signal paths of the mutual paths;

[0018] High signal-to-noise self-path selection is performed on the self-path signal.

[0019] Furthermore, when filtering N+1 paired paths from the valid path information, the following steps are included: limiting... That is, the path distance combination b is the actual distance combination extracted from the path selected by the first-level threshold corresponding to A; That is, the path Doppler combination f is the actual Doppler combination extracted from the path selected through the first-level threshold correspondence A.

[0020] The distance-Doppler dimension determination criterion refers to:

[0021] |Self-path distance vector - Full-rank mutual path distance matrix × coefficient vector| < Distance decision threshold

[0022] |Self-path Doppler vector - Full-rank mutual-path Doppler matrix × coefficient vector| < Doppler decision threshold;

[0023] The distance determination threshold is the maximum number of distance cells that deviate from the expected distance, and the Doppler determination threshold is the maximum number of Doppler cells that deviate from the expected Doppler.

[0024] When N=3, the distance dimension-Doppler dimension determination condition is expressed as:

[0025]

[0026] Where ε1 is the distance determination threshold and ε2 is the Doppler determination threshold.

[0027] When extracting the target corresponding to the successfully paired path, the self-path that did not participate in the pairing is judged when the signal of the self-path is selected with high signal-to-noise self-path. If the target exists in the path, the path, amplitude, distance unit, and Doppler unit information are added to the pairing result.

[0028] Furthermore, the pairing results are filtered by performing constant false alarm rate (CFAR) level two threshold filtering on the pairing results.

[0029] According to the present invention, the multipath fusion problem in distributed radar detection can be solved. On the one hand, it can replace the original two-dimensional pairing scheme without losing the detection probability under normal circumstances. On the other hand, since the number of paths involved in pairing is reduced, its computational complexity is reduced compared with the original two-dimensional pairing scheme. At the same time, for the case of inconsistent performance of distributed platforms, the present invention further improves the detection probability of the true target compared with the traditional two-dimensional pairing scheme. Attached Figure Description

[0030] Figure 1 This is a flowchart illustrating the steps of the minimum path two-dimensional pairing method provided in an embodiment of the present invention;

[0031] Figure 2 This is a flowchart of the minimum path two-dimensional pairing algorithm provided in an embodiment of the present invention;

[0032] Figure 3 This is a constant false alarm detection probability curve comparing the minimum path pairing method under the same platform conditions provided by the present invention with other methods;

[0033] Figure 4 This is a constant false alarm rate (CFAR) detection probability curve comparing the minimum path pairing method under low performance conditions provided by the third platform according to an embodiment of the present invention with other methods. Detailed Implementation

[0034] Distributed radar is a network composed of multiple radar stations located in different positions but working collaboratively. Through network connectivity and collaborative operation, it overcomes the limitations of a single radar operating from a single angle, achieving high-precision, omnidirectional monitoring and tracking of targets. Generally, the signal waveforms transmitted by such radars need to have low cross-correlation so that the radar can separate the signals through matched filtering during reception. When a distributed radar platform detects one or more targets, the multipath information includes real target information and false target information, which cannot be separated due to mutual interference. Furthermore, the range or platform performance of some distributed radar platforms leads to a low signal-to-noise ratio. To address this situation, this invention utilizes the minimum path pairing criterion to minimize the use of low signal-to-noise ratio information. It uses two-dimensional range-Doppler information to complete target information pairing and false target elimination, and finds the corresponding data for the same target on each path based on whether the target's range and Doppler information satisfy the relationship between range and Doppler on different paths.

[0035] The specific implementation of the present invention will now be described in detail with reference to the accompanying drawings.

[0036] The minimum path two-dimensional pairing method provided by this invention is as follows: Figure 1 As shown, it includes the following steps:

[0037] Step S100: Obtain the path information of the distributed radar platform signal, and select valid path data from the signal path to form valid path information;

[0038] In this step, the distributed radar platform performs matched filtering on the signals generated by N radars and obtains the RV spectrum by MTD processing of the pulse compression signal. In practical applications, when distributed radars operate at the same carrier frequency, one radar will receive echo signals from multiple radars. Therefore, path separation processing is also required in this step. By performing matched filtering on the transmitted signals of different radars, the echo signals of the corresponding paths can be obtained, thereby achieving path separation.

[0039] The signal path types for distributed radar to transmit signals to a target include self-path and mutual path. A signal path where the transmitting and receiving radars are the same is a self-path, such as radar 1-target-radar 1; a signal path where the transmitting and receiving radars are different is a mutual path, such as radar 1-target-radar 2 and radar 2-target-radar 1. In the examples, these two paths are represented as 1 transmit 2 receive and 2 transmit 1 receive, respectively.

[0040] In this step, the separated path information is used to construct valid path data.

[0041] In the effective path data, the one-way path distance from the target to the corresponding radar forms a matrix of range vectors r, represented as r = [r1 r2 r...]. i r n ] T , where r i This represents the distance from radar i to the target, where n is the number of radar platforms, n <= N, and r is the distance before pairing. i It is an objectively existing unknown quantity;

[0042] Based on the distance vector r, define the path selection matrix A, denoted as A = [x1 x2 ... x...]. j ] T And: vector Where j is the path index and J is the number of paths; in the path selection matrix, Let j be the distance selection index for platform i. For example, in a radar platform with N=3, if radar 1 is a self-transmitting and self-receiving (1 transmits and 1 receives) path, the path distance of radar 1 is 2r1, and the corresponding row vector is

[200] . If it is a mutual transmitting and receiving path of radar 1 and radar 2, the distance relationship is r1+r2, and the corresponding row vector is

[110] .

[0043] Based on the path selection matrix A, the path distance combination b is defined as follows: Define the path Doppler combination as follows:

[0044] Step S110: According to the minimum pairing path rule, in this step, N+1 pairing paths are selected from the valid path information to form a pairing path dataset, where N is the number of radars in the distributed radar platform.

[0045] Paired path dataset, from path selection matrix Obtained from.

[0046] The specific implementation process is as follows: Figure 2 As shown:

[0047] This invention provides a detailed description of a distributed radar platform with N=3, as shown in step S200 of the figure. In this three-base-station (i.e., three radar) configuration, nine path echo signal splitting is generated, meaning the paths include nine combinations, which can be represented as:

[0048]

[0049]

[0050] The above paths include self-paths and mutual paths.

[0051] For the self-path, proceed to step S221 to select a self-path with higher signal-to-noise ratio;

[0052] For mutual paths, since the signals of mutual paths have the characteristic that the distance is the same and the Doppler is approximately the same, it is necessary to perform non-coherent superposition of their RV spectra, that is, to perform step S222, non-coherent pre-superposition of mutual paths in the same group, that is: 1-in-2-out and 2-in-1-out, 2-in-3-out and 3-in-2-out, 1-in-3-out and 3-in-1-out are fused.

[0053] Subsequently, step S231, the first-level threshold screening of the self-path signal, and step S232, the first-level threshold screening of the mutual path are performed on the self-path and the mutual path respectively. Based on the principle of closest distance or best performance, N+1 paired path data are selected to form the path selection matrix corresponding to the paired path dataset.

[0054] In this step, the valid path data is further restricted. This refers to the actual distance combination extracted from the path selected by the first-level threshold A. For the actual Doppler combination extracted through the path selected by A corresponding to the first-level threshold, specifically, the amplitude of the first-level threshold for the data superimposed on the mutual paths is higher than the amplitude of the first-level threshold corresponding to the self-path.

[0055] In the non-coherent pre-stacking process, for far-field targets, the echo range paths of radar 1 transmitting and radar 2 receiving are the same as those of radar 2 transmitting and radar 1 receiving. Given the same carrier frequency and narrowband frequency division, the Doppler frequencies reflecting the target velocity can also be considered the same. Directly performing amplitude superposition and fusion on a pair of mutual paths in the RV spectrum stage can enhance the RV spectrum peak at the target. Especially when one path is the dominant path, the pre-stacking operation can enhance the amplitude through its corresponding mutual path, allowing target signals that previously could not pass the first threshold to pass through.

[0056] In the design of the first-level threshold, for situations limited by computational power, the first-level threshold can be designed as the peak value of m in the filtering path (where m is the number of data points left in each path after filtering by the first-level threshold). For this case with the N radar platform, the final computational complexity of the algorithm is O(m). N However, it is worth noting that due to the existence of mutual path pre-fusion, if there are no mutual paths with significantly different signal-to-noise ratios, the signal-to-noise ratio of the fused mutual paths will be higher than that of the self-paths. Therefore, the threshold for mutual paths can be set relatively high to reduce computational complexity.

[0057] The path selection matrix corresponding to the final paired path dataset constructed in this step is represented as follows:

[0058] The corresponding paths are: fused 1-in-2-out (i.e., mutual path 12), 2-in-2-out (i.e., mutual path 23), and 1-in-3-out (i.e., mutual path 13); for the self-paths, the 1-in-1-out with higher signal-to-noise ratio (i.e., self-path 1) is selected using prior information, while the self-paths 2 and 3 with lower signal-to-noise ratios are processed in subsequent steps.

[0059] Before extracting the pairing paths that meet the distance-Doppler dimension determination criteria, it is necessary to determine the distance-Doppler dimension determination criteria.

[0060] Step S101: Determine the distance dimension-Doppler dimension determination criteria;

[0061] The criteria for determining the distance dimension-Doppler dimension are:

[0062] |Self-path distance vector - Full-rank mutual path distance matrix × coefficient vector| < Distance decision threshold

[0063] |Self-path Doppler vector - Full-rank mutual-path Doppler matrix × coefficient vector| < Doppler decision threshold.

[0064] For example, when N=3, the distance dimension-Doppler dimension determination condition is expressed as:

[0065]

[0066] Where ε1 is the distance determination threshold and ε2 is the Doppler determination threshold;

[0067] Specifically, the distance judgment threshold ε1 is the maximum number of distance cells that deviate from the expected distance; the Doppler judgment threshold ε2 is the maximum number of Doppler cells that deviate from the expected Doppler.

[0068] Step S120: Traverse the pairing path dataset and extract the pairing paths that meet the distance dimension-Doppler dimension determination criteria to form a successfully paired path dataset;

[0069] In this step, all N+1 path combinations are extracted from the path selection matrix obtained in step S110. Path, amplitude, distance cell, and Doppler cell information are extracted from the path information and combined with the distance-Doppler dimension decision criteria defined in step S101 to obtain all outputs that satisfy the reduced path pairing relationship. The outputs that meet the conditions constitute the successfully paired path dataset (e.g., ...). Figure 2 Step S240).

[0070] Step S130: Traverse the dataset of successfully paired paths, extract the targets corresponding to the successfully paired paths, delete invalid data without corresponding targets, and generate pairing results;

[0071] In this step, the presence of the target is verified in all paths of the successfully paired path dataset. If the target exists, it is added to the pairing results according to path, amplitude, distance cell, and Doppler cell information (e.g., ...). Figure 2 Step S250);

[0072] On the other hand, for self-paths that have not participated in pairing, supplementation is provided. For example, in the N=3 example proposed in this invention, when performing high signal-to-noise self-path selection on the signals of the self-paths, self-paths that have not participated in pairing, such as self-path 2 and self-path 3, are determined by the following method:

[0073] The path that has been judged is also judged to see if there is a target. If there is a target in the path, the path, amplitude, distance cell and Doppler cell information are added to the pairing result. This information is used to restore the coordinate position by combining angle measurement later.

[0074] In this step, the results from step 120 are used for pairing and supplementation. For combinations that meet the pairing conditions, the possible RV spectrum positions of all other paths can be calculated based on the range-Doppler relationship. Data that meets the range-Doppler two-dimensional determination threshold is searched in the data of the corresponding path passage threshold. If it exists, it is added to the pairing combination; if it does not exist, the corresponding amplitude is set to zero to mark that the path data is unavailable.

[0075] Step S140: Generate the minimum path two-dimensional pairing result set.

[0076] In this step, the pairing results generated in step S130 are subjected to constant false alarm rate (CFAR) level two threshold filtering (e.g., ...). Figure 2 Step S260) obtains the final minimum path two-dimensional pairing result set.

[0077] This invention provides a simulation scenario of three radars detecting a single target: each radar can receive and process the transmitted signals of the other radars, thus generating a total of nine paths. Using the three-platform two-dimensional pairing algorithm provided by this invention, four successfully paired paths are generated. The simulation inputs the RV spectrum data of the processed echo signals from each path into these four successfully paired path data, outputting the corresponding information for successful pairing. The simulation uses a Monte Carlo method, setting a constant false alarm probability of 0.01% and a first-level threshold data screening rate of 1%, and conducts 500 repeated experiments within a signal-to-noise ratio range of 4-18 dB, with 1 dB intervals. The number of successfully paired target points is recorded in each experiment, and a detection probability curve is plotted to verify the detection performance of this spatial registration system. Figure 3 As shown, under the same signal-to-noise ratio conditions, the detection performance of the minimum path pairing scheme is higher than that of the traditional two-dimensional pairing scheme.

[0078] The simulation experiments also compared the single-target detection performance under two conditions: one where the third platform was a low-performance platform (with a receiver gain 5dB less than other platforms) and the other where all platforms had uniform performance. A single-platform control group was also included to ensure the rationality of the experimental design. Figure 4 As shown, under the condition of a 5dB decrease in the signal-to-noise ratio (SNR) of a single missile receiver, its detection performance can be improved by 2.5dB in 90% detection probability and by 3dB in 99% detection probability compared to the traditional scheme. The reason for not reaching the 5dB attenuation of a single missile is that the SNR of the third platform receiver decreases, resulting in a decrease in the SNR of the 1-3 and 2-3 mutual path pre-fusion.

[0079] This invention utilizes the minimum pairing criterion to filter pairing paths based on the known low signal-to-noise ratio of some expected paths. Finally, it employs a path reduction pairing algorithm to filter the data processed by the distributed radar, thereby eliminating false targets and selecting the best-performing true targets, thus enabling the distributed radar platform to perform optimal path pairing.

[0080] The above-disclosed embodiments are merely a few specific examples of the present invention. However, the present invention is not limited thereto, and any variations that can be conceived by those skilled in the art should fall within the protection scope of the present invention.

Claims

1. A minimum path two-dimensional pairing method based on distributed radar, characterized in that, Includes the following steps: The path information of the distributed radar platform signal is obtained, and valid path data is selected from the signal path to form valid path information; the types of signal paths include self-path and mutual path. N+1 paired paths are selected from the valid path information to form a paired path dataset, where N is the number of radars in the distributed radar platform; Determine the criteria for distance dimension-Doppler dimension determination; Traverse the pairing path dataset and extract the pairing paths that meet the distance dimension-Doppler dimension determination criteria to form a successfully paired path dataset. Traverse the dataset of successfully paired paths, extract the targets corresponding to the successfully paired paths, delete invalid data without corresponding targets, and generate the pairing results; The pairing results are filtered to generate a minimum path two-dimensional pairing result set; The distance-Doppler dimension determination condition refers to: |Self-path distance vector - Full-rank mutual path distance matrix × coefficient vector| < Distance decision threshold |Self-path Doppler vector - Full-rank cross-path Doppler matrix × coefficient vector| < Doppler decision threshold; The distance determination threshold is the maximum number of distance cells that deviate from the expected distance, and the Doppler determination threshold is the maximum number of Doppler cells that deviate from the expected Doppler.

2. The minimum path two-dimensional pairing method based on distributed radar according to claim 1, characterized in that, When acquiring the path information of the distributed radar platform signal, the radar-generated signal is subjected to matched filtering, and the pulse compression signal is processed by MTD to obtain the RV spectrum. The selection of valid path data from the signal path is achieved through a path selection matrix A; the path selection matrix A is represented as follows: ,and: Where j is the path index, i is the platform index, and n <N, Let j be the distance selection index for platform i, n be the number of radar platforms, and N be the number of radars in the distributed radar platform. Based on the path selection matrix A, the path distance combination b is defined as follows: Define the path Doppler combination f as: .

3. The minimum path two-dimensional pairing method based on distributed radar according to claim 2, characterized in that, The distance selection index is based on the distance vector. The matrix implementation, the distance vector The matrix is ​​composed of the one-way path distance from the target to the corresponding radar, and is expressed as: ,in, This indicates the distance from radar i to the target.

4. The minimum path two-dimensional pairing method based on distributed radar according to claim 2, characterized in that, The step of filtering N+1 paired paths from the valid path information includes: Non-coherent pre-overlay processing is performed on the signal paths of the mutual paths; High signal-to-noise self-path selection is performed on the self-path signal.

5. The minimum path two-dimensional pairing method based on distributed radar according to claim 1, characterized in that, The successfully paired path dataset includes path, amplitude, distance cell, and Doppler cell information.

6. The minimum path two-dimensional pairing method based on distributed radar according to claim 4, characterized in that, When extracting the target corresponding to the successfully paired path, when performing high signal-to-noise self-path selection on the signal of the self-path, the self-path that did not participate in the pairing is judged as a target. If it exists in the path, the path, amplitude, distance unit, and Doppler unit information are added to the pairing result.

7. The minimum path two-dimensional pairing method based on distributed radar according to claim 1, characterized in that, The filtering of the pairing results refers to the filtering of the pairing results using a constant false alarm rate (CFAR) level two threshold.

8. The minimum path two-dimensional pairing method based on distributed radar according to claim 1, characterized in that, When N=3, the distance dimension-Doppler dimension determination condition is expressed as: ， in, For distance determination threshold, Determine the threshold for Doppler.

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