Method for eliminating false positioning points in multi-station cooperative reconnaissance positioning

Abstract

The present application relates to a kind of multi-station cooperative reconnaissance positioning methods for eliminating false positioning points, belong to electronic countermeasure reconnaissance field.The present application is by first using parameter matching method to eliminate a large number of false positioning points, then carries out full pulse segment matching method to eliminate false positioning points of same type target or parameter difficult to distinguish, compared with directly using full pulse segment matching method, it is less in calculation amount, low in complexity, smaller in data transmission in practical application, more easily realized.The present application compared with prior art, introduce full pulse segment matching method, compared with existing data correlation and minimum distance method etc parameter matching method, the accuracy of eliminating false positioning points is higher.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of electronic countermeasure reconnaissance, and particularly relates to a method for eliminating false positioning points in multi-station cooperative reconnaissance positioning. BACKGROUND

[0002] Electronic countermeasure plays an increasingly important role in modern war, and radiation source positioning is an important research direction of electronic countermeasure. Active positioning technology has the advantages of all-weather and high precision, but it is easy to be detected by the enemy, so that it is attacked by the enemy's electronic interference soft kill or anti-radiation missile hard kill weapon, which greatly affects the positioning accuracy, and even threatens the safety of the positioning system. Passive reconnaissance positioning system is to measure the radiation signal emitted by the radiation source target to obtain the position of the radiation source without emitting electromagnetic wave by itself. Compared with active positioning technology, passive reconnaissance positioning technology has the characteristics of electromagnetic silence, strong battlefield survivability, and strong adaptability to complex electromagnetic environment. In modern battlefield environment, the use of passive reconnaissance positioning technology not only enhances the combat capability of our side, but also improves the survivability of our platform. At present, the battle method of modern war is developing from single platform and single sensor to multi-platform and multi-sensor, and passive reconnaissance positioning technology has a wide application in electronic warfare and plays an important role. The commonly used multi-platform positioning method is direction finding line intersection positioning method.

[0003] The direction finding line intersection positioning method is to measure the direction of the radiation source by high-precision direction finding equipment at two or more observation stations, and then the position of the radiation source can be determined through geometric triangular operation according to the data measured by each observation station and the distance between the observation stations. The direction finding intersection positioning system is composed of at least two observation stations, each observation station is equipped with a precise direction finding system, and the direction angle of each station measured by the radiation source forms a straight line connecting the radiation source and the observation station in the plane, and the intersection point of the two straight lines is the position of the radiation source.

[0004] When multiple observation stations detect multiple radiation sources, the direction finding lines of multiple observation stations intersect to produce many positioning points, which contain false target positioning points. The false target is caused by the insufficient matching degree of the target angle and other information in the direction finding line intersection positioning method. When the number of stations and the number of radiation sources increase, these false positioning points also increase. Assuming that there are two observation stations and n radiation source targets, each radiation source target will produce a direction finding line with the observation station, at this time there will be 2n direction finding lines, and n 2There are at most n real intersection points and at most n x (n-1) false intersection points. The false intersection points cause great disturbance and confusion to the observation of real target trajectory. How to effectively eliminate these false intersection points becomes the key problem of the direction finding line intersection positioning method. The existing methods mainly include two categories: data association method and clustering-based minimum distance algorithm.

[0005] The data association algorithm is to associate the data by the direction finding angle, so as to eliminate the false target points. The data association algorithm regards the measurement closest to the radiation source within the threshold range as the only and real measurement of the radiation source. The advantage of the algorithm is that the calculation amount is small, the implementation is simple, and it is suitable for the case that the number of radiation source targets is small. The disadvantage is that the anti-interference ability is poor, and it is easy to cause association error when the clutter density is large and the number of radiation sources is large. A series of algorithms are derived based on the idea of data association, such as joint probability data association algorithm, multiple hypothesis tracking data association algorithm, and multi-dimensional assignment algorithm.

[0006] The joint probability data association algorithm calculates the association probability of each measurement and the possible radiation source by using the concept of effective matrix, but it cannot provide the probability of the existence of the radiation source, and can only handle the case that the number of radiation sources does not change.

[0007] The multiple hypothesis tracking data association algorithm assumes that each newly received measurement may come from a new radiation source, an existing radiation source or clutter, calculates the posterior probability of each association hypothesis, and realizes data association by hypothesis evaluation, management, merging approximate hypotheses and deleting hypotheses with small posterior probability. The advantage of the algorithm is that it is suitable for multi-sensor multi-target data association, and can be effectively used for track initiation and track termination. However, the algorithm relies too much on the prior knowledge of the target and the clutter, and the calculation amount is large, which has the problem of combination explosion.

[0008] The multi-dimensional assignment algorithm is a generalized data association, which adds measurement and measurement, measurement and track, track and track data association, and adopts the exhaustive method to solve the optimal solution, and the calculation complexity increases exponentially with the increase of data dimension. The construction of multi-dimensional assignment in the algorithm is a very time-consuming process. Since the number and state of the radiation source are unknown, the association relationship between the measurement and the state of the radiation source is also uncertain. The construction of the multi-dimensional assignment problem needs to traverse all possible combinations of measurements and measurements, which will cause the combination explosion problem.

[0009] The main problem of the traditional data association algorithm is that it cannot meet the timeliness requirement when the dimension is high.

[0010] The baseline minimum distance method only uses the set distance of the intersection positioning points to exclude false positioning, the algorithm principle is simple, and the performance is good when the reference points and the targets are few, but is greatly affected by the sensor and the geometric position of the target, and the misjudgment rate is large in actual application. When the number of reference points and targets increases sharply, the performance sharply decreases, and the calculation amount is large, which is not conducive to real-time processing.

[0011] The secondary clustering algorithm based on minimum distance is based on the characteristics that the intersection set of the real target has a high clustering degree, and the clustering degree of the intersection set of the false intersection is not high. The intersection points on each direction finding line are analyzed by clustering through the minimum distance method, several intersection sets with high clustering degree are obtained, the intersection sets are secondarily clustered by taking the intersection method, a few intersection sets are obtained, and finally the intersection sets are optimized, so that the false intersection set is eliminated, and the real intersection is obtained. The algorithm can be regarded as further clustering on the basis of the baseline minimum distance method. With the increase of the noise level, the number of target points and reference points, the secondary clustering strategy may delete some correct intersection points, compared with the baseline minimum distance method, the false alarm rate may decrease, and the false alarm rate may obviously increase. SUMMARY

[0012] (1) Technical problems to be solved

[0013] The technical problem to be solved by the present application is how to provide a multi-station cooperative reconnaissance positioning method for eliminating false positioning points, so as to solve the problem of how to effectively eliminate these false intersection points.

[0014] (2) Technical solutions

[0015] In order to solve the above technical problems, the present application provides a multi-station cooperative reconnaissance positioning method for eliminating false positioning points, which comprises the following steps:

[0016] S1, a plurality of observation platforms detect the radiation source target signal;

[0017] S2, a plurality of observation platforms respectively perform parameter measurement, sorting and identification to obtain batch data with direction;

[0018] S3, when multiple targets appear, a direction finding line intersection positioning method is used for positioning to obtain intersection positioning points;

[0019] S4, whether the number M of intersection positioning points is greater than 1 is judged, if not, the intersection point position calculated by the direction finding line intersection positioning algorithm is the real target position; if yes, there is a false point intersection point, and S5 is executed;

[0020] S5. Use parameter matching to eliminate false intersections. Determine if the parameters are matched successfully. If they are, the intersection is a suspected real intersection and proceed to S6. Otherwise, the intersection is a false intersection and proceed to eliminate the false intersection.

[0021] S6. Determine the number of suspected real location points. n The number of maritime and air targets in the common field of view, M0, and the number of theoretically calculated actual location points, N, shall not exceed the total number of targets in the common field of view, M0. 2 The minimum value, i.e., C n ≤min(M0,N 2 If the suspected real location point is found, then the real target location is the actual target location; otherwise, execute S7.

[0022] S7. Generate a full pulse segment parameter set in the form of a structure from the pulse parameters. Use the full pulse segment matching method to continue to eliminate false positioning points. Determine whether the full pulse segment is successfully matched. If the match is successful, the suspected real positioning point is the real target position. Otherwise, the suspected real positioning point is a false positioning point and needs to be eliminated.

[0023] (III) Beneficial Effects

[0024] This invention proposes a method for eliminating false location points through multi-station collaborative reconnaissance and positioning. Compared to existing technologies, this invention introduces a full-pulse segment matching method, which achieves higher accuracy in eliminating false location points than existing parameter matching methods such as data association and minimum distance methods. Existing parameter matching methods struggle to identify targets of similar type or with similar parameters, resulting in some false location points not being completely eliminated. This invention first uses parameter matching to eliminate a large number of false location points, and then uses full-pulse segment matching to eliminate false location points of similar type or with indistinguishable parameters. This method has lower computational complexity and requires less data transmission in practical applications compared to directly using full-pulse segment matching, making it easier to implement.

[0025] The technical solution of this invention can be applied to collaborative reconnaissance and positioning of multiple ground reconnaissance stations, as well as to collaborative reconnaissance and positioning of ground reconnaissance stations and airborne electronic countermeasures equipment. Through ground-to-ground and air-to-ground collaborative reconnaissance and direction finding, this invention enables rapid location of radiation source targets, understanding of the current electromagnetic threat situation, and enhances reconnaissance and surveillance capabilities over the air and sea, achieving surveillance of key air and sea targets. Attached Figure Description

[0026] Figure 1 This is a flowchart illustrating the technical solution of the present invention.

[0027] Figure 2 Schematic diagram of cross-location of direction finding lines when targeting a single target;

[0028] Figure 3 A schematic diagram illustrating the parameter matching method for eliminating false localization points in multi-target scenarios;

[0029] Figure 4 Schematic diagram for eliminating false positioning points for multi-target time parameter matching method;

[0030] Figure 5 Example diagram for full pulse segment parameter set structure. DETAILED DESCRIPTION

[0031] In order to make the objects, contents and advantages of the present application clearer, the specific embodiments of the present application are described in further detail below in combination with the drawings and examples.

[0032] In multi-station reconnaissance positioning, the direction-finding line intersection positioning method is usually used. When multiple targets appear, the direction-finding line intersection positioning method will generate multiple false target positioning points. The present application can eliminate the false positioning points and obtain the real target position.

[0033] In the technical scheme of the present application, when multiple observation platforms jointly perform reconnaissance positioning, first, the radar target parameters of the radiation source are measured, sorted and identified to obtain batched data with directions, and then the direction-finding line intersection positioning is performed. The flow chart is shown in Figure 1 .

[0034] As shown in Figure 1 , the present application proposes a method for eliminating false positioning points in multi-station cooperative reconnaissance positioning, which comprises the following steps:

[0035] S1, multiple observation platforms detect and receive radiation source target signals;

[0036] S2, multiple observation platforms respectively perform parameter measurement, sorting and identification to obtain batched data with directions;

[0037] S3, when multiple targets appear, the direction-finding line intersection positioning method is used for positioning to obtain intersection positioning points;

[0038] S4, it is judged whether the number M of intersection positioning points is greater than 1. If not, the intersection point position calculated by the direction-finding line intersection positioning algorithm is the real target position. If yes, there are false point intersection points, and S5 is executed;

[0039] S5, the parameter matching method is used to eliminate false intersection points. It is judged whether the parameter matching is successful. If yes, the intersection positioning point is a suspected real positioning point, and S6 is executed. If not, the intersection positioning point is a false positioning point, and false positioning point elimination is executed;

[0040] S6, it is judged whether the number C of suspected real positioning points is not greater than the minimum value of the number M0 of sea-air target in the common view area and the number N of theoretically calculated real positioning points, i.e., C n ≤min(M0,N 2 . n 2 ​If yes, the suspected real positioning point is the real target position; otherwise, S7 is executed;

[0041] S7, the pulse parameters are generated in a structure form to generate a full pulse segment parameter set, and a full pulse segment matching method is used to continue to eliminate the false positioning points.

[0042] The technical scheme of the present application is specifically introduced as follows.

[0043] Suppose that the observation platforms have S observation platforms and the radiation source targets are in the same plane, such as the X0Y plane, wherein S≥2; each observation platform can normally receive signals of all radiation sources, and the signals have no overlapping conditions, for example, there are n targets, and n groups of data can be observed; the data received by each observation platform has the same measurement accuracy, and has been time and space registered.

[0044] In order to facilitate drawing and calculation, two observation platforms are taken as examples to elaborate the present application in detail.

[0045] Suppose that there are N radiation source targets in the sea-land-air common view area of the two observation platforms, N≥1, and the sea-land-air situation information shows that there are M0 radiation source targets in the common view area of the two observation platforms, after direction finding line intersection positioning, M intersection positioning points are obtained; theoretically, the direction finding intersection positioning of the two observation platforms has at most N intersection positioning points, wherein M≤N 2 . 2 .

[0046] If the number of intersection positioning points M is not greater than 1, that is, M=1, the intersection point position calculated by the direction finding line intersection positioning algorithm is the real target position. See Figure 2 .

[0047] If the number of intersection positioning points M is greater than 1, that is, M>1, at most N intersection points are real positioning points, and there are N×(N-1) false point intersection points. At this time, the parameter matching method is used to eliminate the false intersection points. See Figure 3 .

[0048] Suppose that the observation platform i respectively measures the observation data of the jth target, which can be represented as W i,j =[RF ij ,PW ij ,DOA ij ,TOA ij ,PA ij, where RF is the pulse carrier frequency, PW is the pulse width, DOA is the pulse direction of arrival, TOA is the pulse time of arrival, PA is the pulse amplitude, i = 1, 2; j = 1, 2,..., N. (Basic pulse parameters such as pulse repetition period, intra-pulse modulation characteristics, etc. can also be added). Each observation platform takes one direction finding line, and two direction finding lines intersect to form a cross point C m . The cross point C m The measurement data set of the cross point C

[0049]

[0050] where m = 1, 2,..., N 2 , is the observation data of the j1thradiation source target detected by observation platform 1, is the observation data of the j2thradiation source target detected by observation platform 2, j1, j2 = 1, 2,..., N.

[0051] If the cross point C m is a real cross point, then the observation quantity

[0052] is very close data. The above observation quantity can be used to adopt the minimum distance method, the least square method, the threshold method, the residual error, etc. to calculate the correlation degree of the observation data, and to determine whether the observation data of the cross point C m comes from the same target. If the observation data does not come from the same target, then the cross point is a false positioning point; if the observation data comes from the same target, then the cross point is a possible real positioning point.

[0053] If the number of suspected real positioning points C n after removing the false cross points by the parameter matching method is not greater than the minimum value of the number of sea-air target in the common view area M0and the number of theoretically calculated real positioning points N 2 , that is, C n ≤ min(M0, N 2 ), it is indicated that the false positioning points have been removed by the above parameter matching method, and the suspected real positioning points are real target positions. If the number of suspected real positioning points C n is greater than the minimum value of the number of sea-air target in the common view area and the number of theoretically calculated real positioning points, that is, C n > min(M0, N 2 ), it is indicated that the false positioning points have not been completely removed by the above parameter matching method. The same type target or multiple targets with similar parameters cannot be distinguished by the parameter matching method, and the false positioning points cannot be eliminated. At this time, the full pulse segment matching is used to continue to eliminate the false positioning points. See Figure 4 .

[0054] The pulse parameters are generated in a structure to form a full pulse segment parameter set. The full pulse segment parameter set is composed of a multi-dimensional parameter sequence, which can more comprehensively, more specifically and more accurately describe the characteristic parameters of the radiation source target changing over time. The full pulse segment parameter set structure can be expressed as Figure 5

[0055] Figure 5 The structure can also be written in the following parameterized form:

[0056]

[0057] wherein W ID is the full pulse segment parameter set, ID is the full pulse segment sequence number; PP ID is the pulse sample graph; W TOA is the full pulse segment pulse arrival time, W TW is the full pulse segment pulse width, N is the number of pulse groups, W Type is the full pulse segment pulse type, including: single pulse, pulse train, pulse train group, etc.; n1~n N are the number of pulses of the 1st-Nth group of pulses; RF is the carrier frequency; PRI1~PRI N are the pulse repetition periods; PW1~PW N are the pulse widths; MOP1~MOP N are the intra-pulse modulations; X1~X N are the intra-pulse characteristic sequence numbers.

[0058] As can be seen from formula (1), the parameters can have three forms. Taking the carrier frequency parameter as an example, it can be a numerical value RF, a vector or [RF min , RF max ], which respectively correspond to the cases where RF is a fixed value, periodically changes, and randomly changes. When the carrier frequency parameter is a fixed value, a numerical value can be used to represent the carrier frequency parameter. When the carrier frequency parameter periodically changes, the periodic change needs to be clearly described. For example, if the periodic change is in a step form, the parameter fixed carrier frequency description method can be referred to, and it can be written as RF = [f1, Δf], wherein

[0059] Δf = f2-f1 = f3-f2 =...; if the period is short, the parameter can also be listed in an exhaustive form, and it can be written as RF = [f1, f2, f3,...]. When the carrier frequency parameter randomly changes, only the boundary values of the parameter change range can be determined, and it can be expressed as RF = [f min , f max ], wherein f min and f max are the upper and lower boundaries of the frequency change range, respectively.

[0060] ​Successively to the suspected real positioning point C n The full-pulse segments of the corresponding two direction-finding lines are matched. Two sets of full-pulse segment parameter set structures are input into a target recognition model for multi-dimensional parameter matching, and the target recognition model gives two sets of full-pulse segment calibration full-pulse segment serial numbers. At this time, whether the two sets of full-pulse segment serial numbers are the same can be used to determine whether the two direction-finding lines are derived from the same radiation source target. If the two sets of full-pulse segment serial numbers are the same, the two direction-finding lines are derived from the same radiation source target, and the suspected real positioning point is the real target position. If the two sets of full-pulse segment serial numbers are different, the two direction-finding lines are derived from different radiation source targets, and the suspected real positioning point is a false positioning point, and the positioning point needs to be eliminated.

[0061] After the false positioning points are eliminated through the two rounds of parameter matching method and full-pulse segment matching method, the remaining intersection positioning points are the real target positions.

[0062] When multiple observation stations detect multiple radiation sources, the direction-finding lines of multiple observation stations intersect to generate many positioning points, which contain false target positioning points. The false target positioning points greatly disturb and confuse the observation of the real target trajectory. Compared with the prior art, the full-pulse segment matching method introduced in the present application has higher accuracy in eliminating false positioning points than the existing data association and minimum distance method and other parameter matching methods. The existing parameter matching methods are difficult to identify the same type of targets or targets with similar parameters, and the false positioning points may not be completely eliminated. The present application eliminates a large number of false positioning points by using the parameter matching method first, and then eliminates the false positioning points of the same type of targets or targets with difficult-to-distinguish parameters by using the full-pulse segment matching method. Compared with directly using the full-pulse segment matching method, the present application has smaller calculation amount, lower complexity, smaller data transmission amount in actual application, and is easier to implement.

[0063] The technical scheme of the present application can be applied to cooperative reconnaissance and positioning of multiple ground reconnaissance stations, and can also be applied to cooperative reconnaissance and positioning of ground reconnaissance stations and airborne electronic countermeasure equipment. The technical scheme of the present application realizes rapid positioning of radiation source targets, masters the electromagnetic threat situation, enhances the reconnaissance and surveillance capability of the air and the sea, and realizes surveillance of key targets in the air and the sea, through cooperative reconnaissance and direction-finding between ground-to-ground and air-to-ground.

[0064] The above only describes the preferred embodiments of the present application. It should be noted that those skilled in the art can make several improvements and modifications without departing from the technical principles of the present application, and these improvements and modifications should also be considered as the protection scope of the present application.

Claims

1. A method for eliminating false positioning points in multi-station cooperative reconnaissance positioning, characterized in that, The method comprises the following steps: S1, a plurality of observation platforms detect and receive a radiation source target signal; S2, the plurality of observation platforms respectively perform parameter measurement, sorting and identification to obtain batch data with a direction; S3, when multiple targets appear, a direction finding line intersection positioning method is used for positioning to obtain an intersection positioning point; S4, judging the number of intersection positioning points whether greater than 1, if not, the intersection point position calculated by the direction finding line intersection positioning algorithm is the real target position; if yes, there is a false intersection point, and S5 is executed; S5, a parameter matching method is used to eliminate false intersection points, and it is judged whether the parameters are matched successfully, if yes, the intersection positioning point is a suspected real positioning point, S6 is executed, otherwise, the intersection positioning point is a false positioning point, false positioning point elimination is executed; S6、judge the number of suspected real positioning points No more than the number of sea-air target in the common view area And the minimum value of the number of theoretically calculated real positioning points If the suspected real positioning point is the real target position;​ Otherwise, S7 is executed; S7, pulse parameters are generated in a structure body form to generate a full pulse segment parameter set, a full pulse segment matching method is used to continue to eliminate false positioning points, and it is judged whether the full pulse segment is matched successfully, if yes, the suspected real positioning point is a real target position, otherwise, the suspected real positioning point is a false positioning point, and the positioning point needs to be eliminated.

2. The method of claim 1, wherein the false position points are eliminated by, Assume that there are S observation platforms and the radiation source target is in the same plane, wherein, ; each observation platform can normally receive signals of all radiation sources, and there is no overlapping condition; the data received by each observation platform has the same measurement accuracy, and has been time and space registered.

3. The method of claim 2, wherein the false position points are eliminated by, Assuming that there are radiation source targets in the sea-land-air common view area of two observation platforms, 1, and the sea-land-air situation information shows that there are radiation source targets in the common view area of the two observation platforms, after cross positioning by the direction finding lines, there are cross positioning points; theoretically, there are cross positioning points at most in the cross positioning of the two observation platforms by direction finding, wherein ; If the number of intersection positioning points is not greater than 1, i.e. = 1, the intersection point position calculated by the direction finding line intersection positioning algorithm is the real target position; If the number of intersection positioning points Greater than 1, that is When >1, there are at most The intersection points are the actual positioning points. A false intersection.

4. The method of claim 3, wherein the false position points are eliminated by, Assume that the observation platform i respectively measures the observation data of the jth target, denoted as , wherein RF is a pulse carrier frequency, PW is a pulse width, DOA is a pulse arrival angle, TOA is a pulse arrival time, and PA is a pulse amplitude, ; Each observation platform takes a direction finding line, two direction finding lines intersect to form an intersection point ; the intersection point The measurement data set of the intersection point is: in , The first one detected by observation platform 1 Observational data of individual radiation source targets, The first one detected by observation platform 2 Observational data of individual radiation source targets, .

5. The method of claim 4, wherein the false position points are eliminated by, In the S5, the parameter matching method for eliminating false intersection points comprises: If the intersection point is a real intersection point, then the observation , , , , is very close data; using the data association method on the above observation, the association degree of the observation data is calculated to determine whether the observation data of the intersection point comes from the same target. If the observation data does not come from the same target, the intersection point is a false positioning point. If the observation data comes from the same target, the intersection point may be a real positioning point.

6. The method of claim 5, wherein the false position points are eliminated by, The data association method comprises a minimum distance method, a least square method, a threshold method and residual error calculation.

7. The method of claim 5, wherein the false position points are eliminated by, In the S7, the pulse parameters are generated in a structure body form to generate a full pulse segment parameter set, the full pulse segment parameter set comprises a plurality of multi-dimensional parameter sequences, and the full pulse segment parameter set structure body is as follows: （1） Wherein, is a full pulse segment parameter set, and ID is a full pulse segment serial number; is a pulse sample map; is a full pulse segment pulse arrival time, is a full pulse segment pulse width, and N is a pulse group number, is a full pulse segment pulse type, including: single pulse, pulse train, pulse train group; is the number of pulses of the 1st-Nth group of pulses, respectively; and RF is a carrier frequency; is a pulse repetition period, is a pulse width, is an intra-pulse modulation, is an intra-pulse feature serial number.

8. The method of claim 7, wherein the false position points are eliminated by, The carrier frequency parameter is a numerical value RF, a vector Or Corresponding to the cases of fixed value, periodic change, and random change of RF, respectively; when the carrier frequency parameter is a fixed value, the carrier frequency parameter is represented by a numerical value. When the carrier frequency parameter is periodically changed, the periodic change needs to be clearly described; when the carrier frequency parameter is randomly changed, only the boundary values of the parameter change range can be determined, which is expressed as wherein and are the upper and lower boundaries of the frequency change range, respectively.

9. The method of claim 8, wherein the false position points are eliminated by, When the carrier frequency parameter is periodically changed, if the periodic change is in step form, it is written as where ; if the period is short, the parameter is listed in the form of exhaustive, written as .

10. The method of claim 7 wherein the false position points are eliminated by, In the S7, the full pulse segment matching method is used to continue to eliminate false positioning points, and the full pulse segment matching method comprises: successively to the suspected real positioning point The full-pulse segment matching is performed on the corresponding two sets of full-pulse segments; the two sets of full-pulse segment parameter set structures are input into a target recognition model for multi-dimensional parameter matching, and the target recognition model gives two sets of full-pulse segment calibration full-pulse segment serial numbers; at this time, whether the two sets of full-pulse segment serial numbers are the same can be used to determine whether the two sets of direction-finding lines are derived from the same radiation source target; if the two sets of full-pulse segment serial numbers are the same, the two sets of direction-finding lines are derived from the same radiation source target, and the suspected real positioning point is the real target position; if the two sets of full-pulse segment serial numbers are different, the two sets of direction-finding lines are derived from different radiation source targets, and the suspected real positioning point is a false positioning point, and the positioning point needs to be eliminated.

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