Multi-target positioning method based on burst signal association

Abstract

The application discloses a multi-target positioning method based on burst signal correlation, and comprises the following steps: acquiring time, frequency, bandwidth and azimuth information of signals received by each station; extracting all azimuth information in detection results of each station and carrying out denoising; classifying the denoised azimuth information, and establishing a new class if any azimuth information does not belong to an existing class; correlatively matching the detection results of each station after classification according to azimuth; and solving a positioning result of a target by using the correlatively matched detection results according to geometric relations between each station and the target position. The application can realize stable positioning of multiple targets and also can realize high-precision positioning of multiple targets.

Description

Technical Field

[0001] This invention belongs to the field of positioning and tracking technology, and particularly relates to a multi-target positioning method based on burst signal correlation. Background Technology

[0002] Unlike continuous signals, the starting point and location of burst signals are uncertain. If traditional correlation methods are used to analyze the detection results of burst signals, it will inevitably have a significant impact on the subsequent location results.

[0003] Because the detection results of sudden signals include the azimuth information of the sudden signals, directly using traditional methods for correlation matching will result in detection results containing the azimuth of the sudden signals. This will lead to poor stability and low positioning accuracy of the target positioning results, and in severe cases, even ineffective positioning. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and provide a multi-target positioning method based on burst signal association. By using the denoising of the azimuth angles measured at each station and the classified detection results for multi-condition association matching, the influence of noise and burst signals in the detection results on the positioning results can be effectively eliminated, and stable and high-precision positioning of multiple targets can be achieved.

[0005] The objective of this invention is achieved through the following technical solution:

[0006] A multi-target localization method based on burst signal correlation, the method comprising:

[0007] Obtain the time, frequency, bandwidth, and azimuth information of the signals received by each station;

[0008] Extract all azimuth information from the detection results of each station and perform noise reduction;

[0009] The denoised azimuth information is classified, and a new class is created if any azimuth information does not belong to an existing class.

[0010] The detection results of each station, classified according to azimuth, are correlated and matched.

[0011] Based on the geometric relationship between each station and the target location, the target's location is obtained by using the detection results after correlation matching.

[0012] Furthermore, the method for denoising azimuth information specifically includes:

[0013] Computing site S k The azimuth sample variance of the detection results in the sliding window:

[0014]

[0015] Set the length of the sliding window to P. Each time the sliding window slides one position, compare whether the variance of the azimuth angle samples in the sliding window meets the threshold condition within the window. If it does, it is determined to be the azimuth angle of the target signal; otherwise, it is the azimuth angle of noise.

[0016] in, This represents all azimuth angles, and k represents station S. k , where m represents the m-th azimuth angle.

[0017] Furthermore, the process of classifying the denoised azimuth information, and establishing a new class if any azimuth information does not belong to an existing class, specifically includes:

[0018] The absolute value of the difference between the denoised azimuth information and the mean of the existing azimuth types is compared with the classification threshold. If the condition is met, it is classified into the corresponding azimuth type. If it does not meet any preset azimuth type, it is treated as a new type. Initially, there is no azimuth type.

[0019] Furthermore, the association and matching of the detection results of each station after classification according to azimuth angle specifically includes:

[0020] The time difference, frequency difference, and bandwidth difference are obtained based on the time, frequency, and bandwidth information of the signals received by each station. The time difference, frequency difference, and bandwidth difference are then used as association conditions to match the detection results of each station after classification one by one according to the association conditions.

[0021] Furthermore, the step of obtaining time difference, frequency difference, and bandwidth difference based on the time, frequency, and bandwidth information of the signals received by each station, and using these time difference, frequency difference, and bandwidth difference as association conditions to match the detection results of each classified station one by one according to the association conditions specifically includes:

[0022] Using any class of the first site after site classification as the baseline class, extract the time, frequency, and bandwidth corresponding to the first azimuth angle in the baseline class and the time, frequency, and bandwidth corresponding to the first azimuth angle in any class of the second site;

[0023] The difference between the time, frequency, and bandwidth extracted from any category of the first site and any category of the second site is taken as the absolute value.

[0024] The time difference, frequency difference, and bandwidth difference are compared with the matching conditions. If all of them are less than the matching conditions, the corresponding classes in the first and second stations are associated. If the match fails, the matching continues with another class in the same station until all classes fail. If the match fails, the two stations are determined to be unrelated and a new station is used for matching. If a class is successfully matched, the matching continues with the next station until all stations are matched.

[0025] Furthermore, the association matching of the detection results of each station after classification according to azimuth angle includes association matching of all stations or association matching of some stations.

[0026] Furthermore, the step of obtaining the target's location result based on the geometric relationship between each station and the target location, using the detection results after association matching, specifically includes:

[0027] Using the detection results obtained after correlation matching, a geometric relationship equation between the observation station and the target location is established;

[0028] The target location result is obtained by solving the relational equation.

[0029] Furthermore, the method also includes storing the denoised azimuth and other corresponding measurement information after extracting all azimuth information from the detection results of each station and performing a denoising step.

[0030] Furthermore, the method also includes storing the classified azimuth and its corresponding other measurement information after classifying the denoised azimuth information.

[0031] The beneficial effects of this invention are as follows:

[0032] (1) The present invention can effectively eliminate the influence of noise and sudden signals on the positioning results in the detection results, and can effectively and stably locate multiple targets, and can also achieve high-precision positioning of multiple targets.

[0033] (2) This invention can be applied to a dual-station rendezvous positioning system to improve its ability to adapt to complex electromagnetic environments and high-precision positioning. Attached Figure Description

[0034] Figure 1 This is a schematic flowchart of a multi-target localization method based on burst signal correlation according to an embodiment of the present invention;

[0035] Figure 2 This is a schematic diagram of a multi-target correlation scenario for a burst signal according to an embodiment of the present invention. Detailed Implementation

[0036] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that, unless otherwise specified, the following embodiments and features described therein can be combined with each other.

[0037] Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0038] Because the detection results of sudden signals include the azimuth information of the sudden signals, directly using traditional methods for correlation matching will result in detection results containing the azimuth of the sudden signals. This will lead to poor stability and low positioning accuracy of the target positioning results, and in severe cases, even ineffective positioning.

[0039] To address the aforementioned technical problems, the following embodiments of the multi-target localization method based on burst signal correlation of the present invention are proposed.

[0040] Example 1

[0041] Reference Figure 1 ,like Figure 1 The diagram shown is a flowchart of the multi-target localization method based on burst signal association provided in this embodiment. The method specifically includes the following steps:

[0042] Step 1: At each site {S k}, k=1,2,…,K Deploy reconnaissance equipment, and obtain the received signal time t by measurement. km Frequency f km Bandwidth B km and azimuth θ km Information such as m = 1, 2, ..., M represents the m-th measurement information (time, frequency, bandwidth, and azimuth, etc.), and k = 1, 2, ..., K represents the station S. k .

[0043] As one implementation method, this embodiment addresses each site {S} k The detection results obtained by measuring k = 1, 2, ..., K are composed of a specific data structure with a typical value of 100 per second.

[0044] Reference Figure 2 ,like Figure 2 The diagram shown is a multi-target association scenario of a sudden signal in this embodiment. This embodiment addresses the scenario where multiple stations associate multiple targets that have sudden signals.

[0045] Step 2: Extract {S} from each site k The total azimuth angles {θ} in the detection result data structure, k = 1, 2, ..., K km},m=1,2,...,M,k=1,2,...,K.

[0046] Using a sliding window method to view each site {S kNoise removal is performed on the azimuth angles in the sliding window, k = 1, 2, ..., K. The sample variance of the azimuth angles in the sliding window is calculated as follows:

[0047]

[0048] in,

[0049] Specifically, let the length of the sliding window be P, and first calculate the sample variance within the first segment of the sliding window. Determine if the sample variance is less than the sample variance threshold. If the conditions are met, all azimuth angles within the sliding window and their corresponding other measurement information will be stored as the detection results after noise reduction.

[0050] The sliding window continues to calculate the sample variance within the window by sliding one step. If the threshold condition is met, it is determined to be the target azimuth and stored; if the threshold condition is not met, it is determined to be noise and removed.

[0051] The process involves continuously sliding the window one by one to determine whether the threshold condition is met, until all azimuth angles have been processed.

[0052] Step 3: Classify the azimuth angles after noise reduction.

[0053] Specifically, set the azimuth classification threshold θ′ k The first azimuth angle θ′ after noise reduction k1 As the first type, the second azimuth angle θ′ is extracted. k1 , will |θ′ k1 -θ′ k2 The value of | and the classification threshold θ′ k For comparison, if it is less than θ′ k If the second azimuth is positive, then the second azimuth is classified as category 1; otherwise, it is classified as category 2.

[0054] Extract the i-th azimuth angle θ′ ki Find the mean μ of the azimuth angles for each class after classifying the first i-1 azimuth angles. j j = 1, 2, ... J, let |μ j -θ′ ki The value of | and the classification threshold θ′ k For comparison, if it is less than θ′ k If the azimuth is true, then the i-th azimuth angle is classified as class J; otherwise, it is classified as class J+1. L j The sum of the number of azimuth angles in the j-th class For site S k The l-th azimuth angle in the j-th category after azimuth classification.

[0055] The azimuth angles are extracted one by one, and threshold comparisons are continuously performed until all the azimuth angles obtained after noise reduction are processed.

[0056] Step 4: Extract the detection results after denoising and classification, and set the time difference, frequency difference, and bandwidth difference as matching conditions.

[0057] This example demonstrates a full match across K sites:

[0058] Using the first category after classifying station S1 as the benchmark, extract the time corresponding to the first azimuth angle in the first category of station S1. frequency and bandwidth Where (k=1,j=1,l=1); the time corresponding to the first azimuth information in the first category of station S2 is extracted. frequency and bandwidth in,

[0059] (k = 2, j = 1, l = 1).

[0060] The corresponding measurement information extracted from the first category of stations S1 and S2 is subtracted and the absolute value is taken.

[0061]

[0062] as well as Compare the time difference, frequency difference, and bandwidth difference with the matching conditions. If all of them are less than the matching conditions, then associate station S1 with the class corresponding to station S1 and set k = 3, j = 1, q = 1. Otherwise, set k = 2, j = 2, q = 1.

[0063] Extracting site S k The time corresponding to the first azimuth angle in the j-th class of (k≠1) frequency and bandwidth The qth class of site S1 and site S k The time, frequency, and bandwidth corresponding to the first azimuth angle in the j-th class are subtracted and their absolute values ​​are taken. Then the result is compared with the matching conditions.

[0064] When the matching condition is met, the q-th class of site S1 is matched with that of site S. k The j-th category is associated, and then k = k + 1, j = 1. Otherwise, j = j + 1, until the association matching condition is met when k = K and j = J′. Then the detection results of each station are matched and a specific frame structure is formed.

[0065] Based on the geometric relationship between each station and the target location, the target's location is obtained by using the detection results after correlation matching.

[0066] This embodiment takes two observation stations as an example. First, it uses the detection results after correlation matching to establish the geometric relationship between the observation stations and the target location:

[0067]

[0068] Wherein, the target position s = [x1 y1], the position of station S1 s1 = [x1′y1′], the position of station S2 s2 = [x2y2], θ1 is the azimuth angle obtained by station S1 to detect the target s, and θ2 is the azimuth angle obtained by station S2 to detect the target s.

[0069] Solving the aforementioned relational equation yields the target's location in the two-dimensional plane:

[0070] s = A -1 B;

[0071] in,

[0072] This embodiment effectively eliminates the influence of noise and sudden signals in the detection results on the positioning results, enabling stable positioning of multiple targets and achieving high-precision positioning of multiple targets. It can be applied to bi-station rendezvous positioning systems to improve their ability to adapt to complex electromagnetic environments and achieve high-precision positioning.

[0073] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A multi-target localization method based on burst signal correlation, characterized in that, The method includes: Obtain the time, frequency, bandwidth, and azimuth information of the signals received by each station; Extract all azimuth information from the detection results of each station and perform noise reduction; The denoised azimuth information is classified, and a new class is created if any azimuth information does not belong to an existing class. The detection results of each station, classified according to azimuth, are correlated and matched. Based on the geometric relationship between each station and the target location, the target's location result is obtained by using the detection results after correlation matching; The method for denoising azimuth information specifically includes: Computing site The azimuth sample variance of the detection results in the sliding window: ； Set the length of the sliding window to Each time the sliding window slides one position, the variance of the azimuth angle samples in the sliding window is compared to see if it meets the threshold condition within the window. If it does, it is determined to be the azimuth angle of the target signal; otherwise, it is the azimuth angle of noise. in, , Indicates all azimuth angles. Indicates site , Indicates the first One azimuth angle; The process of classifying the denoised azimuth information, and creating a new class if any azimuth information does not belong to an existing class, specifically includes: The absolute value of the difference between the denoised azimuth information and the mean of the existing azimuth types is compared with the classification threshold. If the condition is met, it is classified into the corresponding azimuth type. If it does not meet any preset azimuth type, it is treated as a new type. Initially, there is no azimuth type. The process of associating and matching the detection results of each station after classification by azimuth angle specifically includes: The time difference, frequency difference, and bandwidth difference are obtained based on the time, frequency, and bandwidth information of the signals received by each station. The time difference, frequency difference, and bandwidth difference are then used as association conditions to match the detection results of each station after classification one by one according to the association conditions.

2. The multi-target localization method based on burst signal correlation as described in claim 1, characterized in that, The step of obtaining time difference, frequency difference, and bandwidth difference based on the time, frequency, and bandwidth information of the signals received by each station, and using these differences as association conditions to match the detection results of each classified station one by one according to the association conditions, specifically includes: Using any class of the first site after site classification as the baseline class, extract the time, frequency, and bandwidth corresponding to the first azimuth angle in the baseline class and the time, frequency, and bandwidth corresponding to the first azimuth angle in any class of the second site; The difference between the time, frequency, and bandwidth extracted from any category of the first site and any category of the second site is taken as the absolute value. The time difference, frequency difference, and bandwidth difference are compared with the matching conditions. If all of them are less than the matching conditions, the corresponding classes in the first and second stations are associated. If the match fails, the matching continues with another class in the same station until all classes fail. If the match fails, the two stations are determined to be unrelated and a new station is used for matching. If a class is successfully matched, the matching continues with the next station until all stations are matched.

3. The multi-target localization method based on burst signal correlation as described in claim 1, characterized in that, The process of associating and matching the detection results of each station after classification by azimuth angle includes associating and matching all stations or associating and matching a subset of stations.

4. The multi-target localization method based on burst signal correlation as described in claim 1, characterized in that, The process of obtaining the target's location result based on the geometric relationship between each station and the target location, and using the detection results after correlation matching, specifically includes: Using the detection results obtained after correlation matching, a geometric relationship equation between the observation station and the target location is established; The target location result is obtained by solving the relational equation.

5. The multi-target localization method based on burst signal correlation as described in claim 1, characterized in that, The method also includes storing the denoised azimuth and other corresponding measurement information after extracting all azimuth information from the detection results of each station and performing a denoising step.

6. The multi-target localization method based on burst signal correlation as described in claim 1, characterized in that, The method further includes storing the classified azimuth angle and its corresponding other measurement information after classifying the denoised azimuth information.

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