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

Abstract

The invention relates to a method for eliminating false positioning points through multi-station cooperative reconnaissance positioning, and belongs to the field of electronic countermeasure reconnaissance. According to the method, a large number of false positioning points are eliminated by adopting the parameter matching method, and then the false positioning points of which the same-type targets or parameters are difficult to distinguish are eliminated by adopting the full-pulse fragment matching method, so that compared with the method of directly adopting the full-pulse fragment matching method, the calculation amount is small, the complexity is low, and the data transmission amount is smaller and easier to implement in practical application. Compared with the prior art, the full-pulse fragment matching method is introduced, and compared with existing parameter matching methods such as data association and a minimum distance method, the accuracy of eliminating false positioning points is higher.

Description

Technical Field

[0001] The invention belongs to the field of electronic countermeasure reconnaissance, and in particular relates to a method for eliminating false positioning points by multi-station collaborative reconnaissance positioning. Background Art

[0002] The status and role of electronic countermeasures in modern warfare are constantly increasing, and radiation source positioning is an important research direction of electronic countermeasures. Active positioning technology has the advantages of all-weather and high precision, but it is easily detected by the enemy, and thus suffers from soft kill of enemy electronic interference or hard kill weapons such as anti-radiation missiles, which has a great impact on positioning accuracy and even threatens the safety of the positioning system itself. The passive reconnaissance and positioning system uses the radiation signal emitted by the radiation source target to measure without emitting electromagnetic waves itself to obtain the location of the radiation source. Compared with active positioning technology, passive reconnaissance and positioning technology has the characteristics of electromagnetic silence, strong battlefield survivability, and strong adaptability to complex electromagnetic environments. In the modern battlefield environment, the use of passive reconnaissance and positioning technology not only enhances our combat capability, but also improves the survivability of our platform. At present, the tactics of modern warfare are developing from a single platform and a single sensor to a multi-platform and multi-sensor direction. Passive reconnaissance and positioning technology has a wide range of applications in electronic warfare and plays an important role. The most commonly used multi-platform positioning methods are the cross-line positioning method.

[0003] The direction finding line cross positioning method uses high-precision direction finding equipment to find the direction of the radiation source at two or more observation stations, and then determines the location of the radiation source through geometric trigonometric operations based on the data measured by each observation station and the distance between the observation stations. The direction finding cross positioning system consists of at least two observation stations, each of which is equipped with a precise direction finding system. The azimuth of the radiation source measured by each station forms a straight line connecting the radiation source and the observation station on the plane. The intersection of the two straight lines is the location of the radiation source.

[0004] When an observation station detects multiple radiation sources, the intersection of the direction finding lines of multiple observation stations generates many positioning points, including false target positioning points. False targets are caused by the lack of matching between the target arrival angle and other information in the direction finding line intersection positioning method. When the number of stations and radiation sources increases, these false positioning points will also increase. Suppose there are two observation stations and n radiation source targets. Each radiation source target will generate a direction finding line with the observation station. At this time, there will be 2n direction finding lines, generating n 2There are at most n real positioning points and at most n×(n-1) false positioning points. False positioning points cause great disturbance and confusion to the observation of the real target trajectory. How to effectively eliminate these false intersections has become the key issue of the direction finding line intersection positioning method. There are two main types of existing methods: data association method and clustering-based minimum distance algorithm.

[0005] The data association algorithm uses the direction finding angle to perform data association, thereby eliminating false target points. The data association algorithm treats the measurement closest to the radiation source within the threshold range as the only and true measurement of the radiation source. The advantages of this algorithm are small amount of calculation, simple implementation, and suitable for situations where the number of radiation source targets is small. Its disadvantage is poor anti-interference ability, which can easily lead to association errors when the clutter density is large and the number of radiation sources is large. A series of algorithms have been derived based on the idea of ​​data association, such as the joint probability data interconnection algorithm, the multi-hypothesis tracking data association algorithm, the multi-dimensional allocation algorithm, etc.

[0006] The joint probabilistic data association algorithm uses the concept of effective matrix to calculate the association probability of each measurement with possible radiation sources, but it cannot provide the probability of the existence of radiation sources and can only handle the situation where the number of radiation sources does not change.

[0007] The multi-hypothesis tracking data association algorithm assumes that each newly received measurement may come from a newly established radiation source, an existing radiation source, or clutter, calculates the posterior probability of each association hypothesis, and achieves data association through hypothesis evaluation and management, merging approximate hypotheses, and deleting hypotheses with small posterior probabilities. The advantage of this 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 prior knowledge of targets and clutter, and has a large amount of calculation, and there is a problem of combinatorial explosion.

[0008] The multidimensional allocation algorithm is a generalized data association. It adds data associations such as measurement and measurement, measurement and track, track and track, and uses an exhaustive method to solve the optimal solution. Its computational complexity increases exponentially with the increase of data dimension. Constructing multidimensional allocation in this algorithm is a very time-consuming process. Since the number and state of radiation sources are unknown, the correlation between the measurement and the state of the radiation source is also uncertain. Constructing a multidimensional allocation problem requires traversing all possible combinations of measurements, which will lead to a combinatorial explosion problem.

[0009] The main problem of traditional data association algorithms is that they cannot meet the timeliness requirements when the dimensions are high.

[0010] The baseline minimum distance method only uses the collective distance of the cross positioning points to eliminate false positioning. The algorithm principle is simple and has good performance when the number of reference points and targets is small. However, it is affected by the geometric position of the sensor and the target, and the misjudgment rate is large in practical applications. When the number of reference points and targets increases sharply, the performance drops sharply, and the amount of calculation is large, which is not conducive to real-time processing.

[0011] The secondary clustering algorithm based on minimum distance is based on the fact that the intersection set of the real target has a high degree of clustering, while the clustering degree of the set of false intersections is not high. The minimum distance method is used to cluster the intersections on each direction finding line to obtain several intersection sets with a high degree of clustering. These intersection sets are then clustered again by taking the intersection method to obtain a few intersection sets. Finally, these few intersection sets are selected to eliminate the false intersection sets and obtain the real intersections. This algorithm can be regarded as further clustering based on the baseline minimum distance method. With the increase of noise level, target points and reference points, the secondary clustering strategy may delete the correctly associated intersections, which may decrease compared with the baseline minimum distance method, and the false alarm rate will increase significantly. Summary of the invention

[0012] 1. Technical issues to be resolved

[0013] The technical problem to be solved by the present invention is how to provide a method for eliminating false positioning points by collaborative reconnaissance and positioning of multiple stations, so as to solve the problem of how to effectively eliminate these false intersections.

[0014] (II) Technical solution

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

[0016] S1. Multiple observation platforms detect and receive radiation source target signals;

[0017] S2, multiple observation platforms respectively measure, sort and identify parameters to obtain batch data with direction;

[0018] S3. When multiple targets appear, use the direction finding line cross positioning method to obtain the cross positioning point;

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

[0020] S5, using the parameter matching method to eliminate false intersection points, and judging whether the parameters are matched successfully. If so, the intersection positioning point is a suspected real positioning point, and S6 is executed; otherwise, the intersection positioning point is a false positioning point, and false positioning point elimination is executed;

[0021] S6. Determine the number of suspected real positioning points C n Not more than the number of sea and air targets in the common view area M0 and the number of theoretically calculated real positioning points N 2 The minimum value of C n ≤min(M0,N 2 ), then the suspected real positioning point is the real target position; otherwise, execute S7;

[0022] S7. Generate a full pulse segment parameter set in the form of a structure using the pulse parameters, and continue to eliminate false positioning points using the full pulse segment matching method to determine whether the full pulse segment is matched successfully. 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] The present invention proposes a method for eliminating false positioning points by collaborative reconnaissance and positioning of multiple stations. Compared with the prior art, the present invention introduces a full pulse segment matching method, which has higher accuracy in eliminating false positioning points than existing parameter matching methods such as data association and minimum distance method. It is difficult for existing parameter matching methods to identify targets of the same type or targets with similar parameters, and there will be false positioning points that are not completely eliminated. The present invention first uses the parameter matching method to eliminate a large number of false positioning points, and then uses the full pulse segment matching method to eliminate the same type of targets or false positioning points whose parameters are difficult to distinguish. Compared with directly using the full pulse segment matching method, the present invention has a smaller amount of calculation and lower complexity. In practical applications, the amount of data transmission is smaller, and it is easier to implement.

[0025] The technical solution of the present invention can be applied to the coordinated reconnaissance and positioning of multiple ground reconnaissance stations, and can also be applied to the coordinated reconnaissance and positioning of ground reconnaissance stations and avionic electronic countermeasure equipment. The technical solution of the present invention can realize the rapid positioning of radiation source targets, grasp the electromagnetic threat situation in front of it, enhance the reconnaissance and surveillance capabilities of air and sea, and realize the surveillance of key targets in the air and sea through the coordinated reconnaissance and direction finding between ground-ground and air-ground. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a working flow diagram of the technical solution of the present invention;

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

[0028] Figure 3 Schematic diagram of eliminating false positioning points using parameter matching method for multiple targets;

[0029] Figure 4 Schematic diagram of eliminating false positioning points using parameter matching method for multiple targets;

[0030] Figure 5 This is an example diagram of the full pulse segment parameter set structure. DETAILED DESCRIPTION

[0031] In order to make the purpose, content and advantages of the present invention more clear, the specific implementation methods of the present invention are further described in detail below in conjunction with the drawings and examples.

[0032] The direction finding line cross positioning method is often used in multi-station reconnaissance positioning. When multiple targets appear, multiple false target positioning points will appear in the direction finding line cross positioning. The present invention can eliminate the false positioning points and obtain the real target position.

[0033] In the technical solution of the present invention, when multiple observation platforms jointly conduct reconnaissance and positioning, the radar target parameters of the radiation source are first measured, sorted, and identified to obtain batch data with directions, and then the direction finding line cross positioning is performed. See the flowchart for details. Figure 1 .

[0034] like Figure 1 As shown, the present invention proposes a method for eliminating false positioning points by multi-station collaborative reconnaissance positioning, comprising the following steps:

[0035] S1. Multiple observation platforms detect and receive radiation source target signals;

[0036] S2, multiple observation platforms respectively measure, sort and identify parameters to obtain batch data with direction;

[0037] S3. When multiple targets appear, use the direction finding line cross positioning method to obtain the cross positioning point;

[0038] S4, determine whether the number of intersection positioning points M is greater than 1, if not, the intersection position calculated by the direction finding line intersection positioning algorithm is the real target position; if yes, there is a false intersection point, and execute S5;

[0039] S5, using the parameter matching method to eliminate false intersection points, and judging whether the parameters are matched successfully. If so, the intersection positioning point is a suspected real positioning point, and S6 is executed; otherwise, the intersection positioning point is a false positioning point, and false positioning point elimination is executed;

[0040] S6. Determine the number of suspected real positioning points C n Not more than the number of sea and air targets in the common view area M0 and the number of theoretically calculated real positioning points N 2 The minimum value of C n ≤min(M0,N 2), then the suspected real positioning point is the real target position; otherwise, execute S7;

[0041] S7. Generate a full pulse segment parameter set in the form of a structure using the pulse parameters, and continue to eliminate false positioning points using the full pulse segment matching method to determine whether the full pulse segment is matched successfully. 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.

[0042] The technical solution of the present invention is specifically described as follows.

[0043] Assume that there are S observation platforms and the radiation source target are in the same plane, such as the X0Y plane, where S ≥ 2; each observation platform can normally receive the signals emitted by all radiation sources, and there is no signal overlap. For example, if there are n targets, n sets of data can be observed; the data received by each observation platform has the same measurement accuracy and has been aligned in time and space.

[0044] In order to facilitate drawing and calculation, the scheme of the present invention is described in detail by taking two observation platforms as examples.

[0045] Assume that there are N radiation source targets in the common viewing area of ​​the sea, land and air of the two observation platforms, N ≥ 1, and the sea, land and air situation information shows that there are M0 radiation source targets in the common viewing area of ​​the two observation platforms. After the cross positioning of the direction finding lines, M cross positioning points are obtained; theoretically, the direction finding cross positioning of the two observation platforms can be at most N 2 Cross-location points, where M≤N 2 .

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

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

[0048] Assume that the observation platform i measures the observation data of the jth target, which can be expressed 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 arrival angle, TOA is the pulse arrival time, PA is the pulse amplitude, i = 1, 2; j = 1, 2, ... N. (Basic pulse parameters such as pulse repetition period and intra-pulse modulation characteristics can also be added). Each observation platform randomly selects a direction finding line, and the intersection of the two direction finding lines forms an intersection point C m . Intersection C m The measurement data set is:

[0049]

[0050] Where m=1,2,......N 2 , is the observation data of the j1th radiation source target detected by observation platform 1, It is the observation data of the j2th radiation source target detected by observation platform 2, j1, j2 = 1, 2, ... N.

[0051] If the intersection point C m is a true intersection point, then the observed quantity

[0052] The above observations can be used to calculate the correlation of the observation data using the minimum distance method, the least squares method, the threshold method, the residual method and other data association methods to determine the intersection point C. m Whether the observed data of the intersection point comes from the same target. If the observed data do not come from the same target, the intersection point is a false positioning point; if the observed data come from the same target, the intersection point may be a real positioning point.

[0053] If the number of suspected real positioning points after removing the false intersection points by the parameter matching method is C n Not more than the number of sea and air targets in the common view area M0 and the number of theoretically calculated real positioning points N 2 The minimum value of C n ≤min(M0,N 2 ), indicating that the above parameter matching method has removed the false positioning points, and the suspected real positioning points are the real target positions. n It is greater than the minimum value of the number of sea and air targets in the common view area and the number of theoretically calculated real positioning points, that is, C n >min(M0,N 2 ), indicating that the above parameter matching method has not completely removed the false positioning points. The same type of targets or multiple targets with similar parameters that cannot be distinguished are difficult to distinguish using the parameter matching method, and there will be false positioning points that have not been eliminated. In this case, full pulse segment matching is used to continue to eliminate false positioning points. Figure 4 shown.

[0054] The pulse parameters are generated as a full pulse segment parameter set in the form of a structure. The full pulse segment parameter set consists of a multi-dimensional parameter sequence, which can more comprehensively, detailedly and accurately describe the characteristic parameters of the radiation source target that change over time. The full pulse segment parameter set structure can be expressed as follows Figure 5 form:

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

[0056]

[0057] Among them, W ID is the parameter set of the whole pulse segment, ID is the serial number of the whole pulse segment; PP ID is the pulse sample diagram; W TOA is the pulse arrival time of the whole pulse segment, W TW is the pulse duration of the whole pulse segment, N is the number of pulse groups, W Type It is the full pulse segment pulse type, including: single pulse, pulse train, pulse train group, etc.; n1~n N are the pulse numbers of the 1st to Nth groups of pulses respectively; RF is the carrier frequency; PRI1~PRI N is the pulse repetition period, PW1~PW N is the pulse width, MOP1~MOP N For pulse modulation, X1~X N It is the intra-pulse characteristic number.

[0058] As can be seen from formula (1), the parameters can be in three forms. Taking the carrier frequency parameter as an example, it can be a numerical value RF, a vector Or [RF min ,RF max ], corresponding to the cases where RF is fixed, periodic, and random. When the carrier frequency parameter is fixed, a single value can represent the carrier frequency parameter. When the carrier frequency parameter is periodic, the periodic variation needs to be described clearly. For example, if the periodic variation is in step form, the carrier frequency description method with fixed parameters can be referred to, and written as RF = [f1, Δf], where

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

[0060] The suspected real positioning point C n The full pulse segments of the corresponding two direction-finding lines are matched. The two sets of full pulse segment parameter set structures are input into the target recognition model for multi-dimensional parameter matching. The target recognition model will give two sets of full pulse segment calibration full pulse segment serial numbers. At this time, it is only necessary to compare whether the full pulse segment serial numbers are the same to determine whether the two direction-finding lines come from the same radiation source target. If the two sets of full pulse segment serial numbers are the same, the two direction-finding lines come 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 come from different radiation source targets, and the suspected real positioning point is a false positioning point, which needs to be eliminated.

[0061] After two rounds of calculations using the parameter matching method and the full pulse segment matching method to eliminate false positioning points, the remaining cross positioning points are the true target positions.

[0062] When an observation station detects multiple radiation sources, the direction-finding lines of multiple observation stations intersect to generate many positioning points, including false target positioning points. False positioning points cause great disturbance and confusion to the observation of the real target trajectory. Compared with the prior art, the present invention introduces a full pulse segment matching method, which is more accurate in eliminating false positioning points than existing parameter matching methods such as data association and minimum distance method. Existing parameter matching methods are difficult to identify targets of the same type or targets with similar parameters, and there will be false positioning points that are not completely eliminated. The present invention first uses the parameter matching method to eliminate a large number of false positioning points, and then uses the full pulse segment matching method to eliminate the same type of targets or false positioning points with parameters that are difficult to distinguish. This method has less computational complexity and lower complexity than directly using the full pulse segment matching method. In practical applications, the amount of data transmission is smaller, and it is easier to implement.

[0063] The technical solution of the present invention can be applied to the coordinated reconnaissance and positioning of multiple ground reconnaissance stations, and can also be applied to the coordinated reconnaissance and positioning of ground reconnaissance stations and avionic electronic countermeasure equipment. The technical solution of the present invention can realize the rapid positioning of radiation source targets, grasp the electromagnetic threat situation in front of it, enhance the reconnaissance and surveillance capabilities of air and sea, and realize the surveillance of key targets in the air and sea through the coordinated reconnaissance and direction finding between ground-ground and air-ground.

[0064] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for eliminating false positioning points by multi-station collaborative reconnaissance positioning, characterized in that: The method comprises the following steps: S1. Multiple observation platforms detect and receive radiation source target signals; S2, multiple observation platforms respectively measure, sort and identify parameters to obtain batch data with direction; S3. When multiple targets appear, use the direction finding line cross positioning method to obtain the cross positioning point; S4, determine whether the number of intersection positioning points M is greater than 1, if not, the intersection position calculated by the direction finding line intersection positioning algorithm is the real target position; if yes, there is a false intersection point, and execute S5; S5, using the parameter matching method to eliminate false intersection points, and judging whether the parameters are matched successfully. If so, the intersection positioning point is a suspected real positioning point, and S6 is executed; otherwise, the intersection positioning point is a false positioning point, and false positioning point elimination is executed; S6. Determine the number of suspected real positioning points C n Not more than the number of sea and air targets in the common view area M0 and the number of theoretically calculated real positioning points N 2 The minimum value of C n ≤min(M0,N 2 ), then the suspected real positioning point is the real target position; Otherwise, execute S7; S7. Generate a full pulse segment parameter set in the form of a structure using the pulse parameters, and continue to eliminate false positioning points using the full pulse segment matching method to determine whether the full pulse segment is matched successfully. 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.

2. The method for eliminating false positioning points by multi-station collaborative reconnaissance and positioning as claimed in claim 1, characterized in that: Assume that there are S observation platforms and the radiation source target are in the same plane, where S ≥ 2; each observation platform can normally receive the signals emitted by all radiation sources, and there is no signal overlap; the data received by each observation platform has the same measurement accuracy and has been time-space aligned.

3. The method for eliminating false positioning points by multi-station collaborative reconnaissance and positioning as claimed in claim 2, characterized in that: Assume that there are N radiation source targets in the common viewing area of ​​the sea, land and air of the two observation platforms, N ≥ 1, and the sea, land and air situation information shows that there are M0 radiation source targets in the common viewing area of ​​the two observation platforms. After the cross positioning of the direction finding lines, M cross positioning points are obtained; theoretically, the direction finding cross positioning of the two observation platforms can be at most N 2 Cross-location points, where M≤N 2 ; If the number of intersection positioning points M is not greater than 1, that is, M = 1, the intersection position calculated by the direction finding line intersection positioning algorithm is the true target position; 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 intersection points.

4. The method for eliminating false positioning points by multi-station collaborative reconnaissance and positioning as claimed in claim 3, characterized in that: Assume that observation platform i measures the observation data of the jth target, expressed 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 arrival angle, TOA is the pulse arrival time, PA is the pulse amplitude, i = 1, 2; j = 1, 2, ... N; Each observation platform randomly selects a direction finding line, and the intersection of two direction finding lines forms an intersection point C m ; Intersection point C m The measurement data set is: Where m=1,2,......N 2 , is the observation data of the j1th radiation source target detected by observation platform 1, It is the observation data of the j2th radiation source target detected by observation platform 2, j1, j2 = 1, 2, ... N.

5. The method for eliminating false positioning points by multi-station collaborative reconnaissance and positioning as claimed in claim 4, characterized in that: In S5, eliminating false intersections by using a parameter matching method includes: If the intersection point C m is a true intersection point, then the observed quantity The data are very similar; the data association method is used to calculate the correlation degree of the observed data and determine the intersection point C m Whether the observation data come from the same target, if the observation data do not come from the same target, the intersection point is a false positioning point; if the observation data come from the same target, the intersection point may be a real positioning point.

6. The method for eliminating false positioning points by multi-station collaborative reconnaissance and positioning as claimed in claim 5, characterized in that: Data association methods include: minimum distance method, least squares method, threshold method and residual method.

7. The method for eliminating false positioning points by multi-station collaborative reconnaissance and positioning as claimed in claim 5, characterized in that: In S7, generating a full pulse segment parameter set in the form of a structure from the pulse parameters includes: the full pulse segment parameter set is composed of a multi-dimensional parameter sequence, and the full pulse segment parameter set structure is: Among them, W ID is the parameter set of the whole pulse segment, ID is the serial number of the whole pulse segment; PP ID is the pulse sample diagram; W TOA is the pulse arrival time of the whole pulse segment, W TW is the pulse duration of the whole pulse segment, N is the number of pulse groups, W Type It is the full pulse segment pulse type, including: single pulse, pulse train, pulse train group, etc.; n1~n N are the pulse numbers of the 1st to Nth groups of pulses respectively; RF is the carrier frequency; PRI1~PRI N is the pulse repetition period, PW1~PW N is the pulse width, MOP1~MOP N For pulse modulation, X1~X N It is the intra-pulse characteristic number.

8. The method for eliminating false positioning points by multi-station collaborative reconnaissance and positioning as claimed in claim 7, characterized in that: The carrier frequency parameters are numerical RF, vector Or [RF min ,RF max ], corresponding to the cases where RF is a fixed value, periodic change, and random change respectively; when the carrier frequency parameter is a fixed value, a single value is used to represent the carrier frequency parameter; When the carrier frequency parameter changes periodically, the periodic change needs to be described clearly; when the carrier frequency parameter changes randomly, it can only be determined by the boundary value of the parameter change range, expressed as RF = [f min ,f max ], where f min and f max are the upper and lower bounds of the frequency variation range respectively.

9. The method for eliminating false positioning points by multi-station collaborative reconnaissance and positioning as claimed in claim 8, characterized in that: When the carrier frequency parameters change periodically, if the periodic change is in step form, it is written as RF = [f1, Δf], where Δf = f2-f1 = f3-f2 = ...; if the period is shorter, the parameters are listed in an exhaustive form, written as RF = [f1, f2, f3, ...].

10. The method for eliminating false positioning points by multi-station collaborative reconnaissance and positioning as claimed in claim 7, characterized in that: In S7, the full pulse segment matching method is used to continue to eliminate false positioning points, including: The suspected real positioning point C n The full pulse segments of the corresponding two direction finding lines are matched; the two sets of full pulse segment parameter set structures are input into the target recognition model for multi-dimensional parameter matching. The target recognition model will give two sets of full pulse segment calibration full pulse segment serial numbers. At this time, it is only necessary to compare whether the full pulse segment serial numbers are the same 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, which needs to be eliminated.

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