Distributed radar-based range ambiguity and velocity ambiguity cooperative solution method

Abstract

The invention discloses a method for cooperatively solving range ambiguity and velocity ambiguity based on distributed radars. The range ambiguity solving comprises the following steps: measuring a radar apparent distance, and calculating a maximum non-ambiguity distance to carry out distance continuation; calculating a positioning equation of each radar relative to the reference radar; intersection points of the equations are solved and screened, and the real distance of the target is calculated through other intersection points; the solution of velocity ambiguity comprises the steps of measuring radar apparent velocity, calculating maximum non-ambiguity velocity, carrying out velocity continuation to obtain velocity combination, and carrying out information fusion to obtain target ambiguity velocity; taking the target position as a starting point to obtain a target radial speed positioning map; screening intersection points and setting the real speed of the target; and obtaining the real speed according to the fuzzy number of the real speed point. The system and structure advantages of distributed radars are utilized, a chi-square test algorithm based on mahalanobis distance is established on the basis of radar cooperation, and the problems of distance measurement ambiguity, speed measurement ambiguity and speed measurement-distance measurement ambiguity in radar detection are solved on the premise that only one pulse repetition frequency is used.

Description

Technical Field

[0001] The present invention relates to the technical field of radar signal processing, and in particular to a method for collaboratively resolving range ambiguity and speed ambiguity based on distributed radar. Background Art

[0002] Deambiguation is an important research issue in the field of radar. The main task of radar is to receive the echo signal reflected by the target and extract the position information such as distance, speed and angle from it. In the past single radar system, multiple pulse repetition frequencies were often used, and then the Chinese remainder theorem, one-dimensional set algorithm and other methods were used for deambiguation. Radar technology has made great progress in the past few decades and has become an indispensable part of modern science and engineering. With the development of stealth and other technologies, single radar systems are gradually developing towards multi-radar systems. Distributed radar is a multi-radar system composed of multiple independent radars developed in recent years. The system can use the coordination of radars to solve the problems existing in single radar systems, such as single observation angle, low positioning accuracy and low false alarm probability.

[0003] A single radar can also resolve distance ambiguity and speed ambiguity, and there are many mature deambiguation algorithms, but these algorithms all require the use of multiple sets of pulse repetition frequencies, which results in a waste of spectrum resources, high radar signal processing complexity, and low efficiency. Compared with a single radar system, a distributed radar system has more information about targets because its radars are distributed in different locations. Therefore, it is considered to use the advantages of the distributed radar system to solve the ambiguity problem in radar detection. Summary of the invention

[0004] The purpose of the present invention is to overcome the deficiencies of the prior art and provide a method for collaboratively resolving range ambiguity and speed ambiguity based on distributed radar.

[0005] The objective of the present invention is achieved through the following technical solutions: The present invention provides a method for collaboratively resolving range ambiguity and speed ambiguity based on distributed radar, comprising a step of resolving range ambiguity and a step of resolving speed ambiguity; The step of resolving distance ambiguity specifically includes: S1. Measure the radar to obtain the apparent distance, calculate the maximum unambiguous distance of the radar, and perform distance extension based on the maximum unambiguous distance; S2, based on the reference node distance-angle plane collaborative positioning, obtain the positioning equation of each radar relative to the reference radar; S3, finding the intersection point according to the positioning equation of each group of radars; S4, the intersection points are detected and screened based on the chi-square distribution of Mahalanobis distance, and the intersection point target is regarded as the target true distance of the reference node; S5. Solve the real distance of the target in its coordinate system through the remaining intersection points to obtain the distance from the target to each radar; The step of resolving velocity ambiguity specifically includes: a. Measure the radar, obtain the apparent speed, calculate the maximum unambiguous speed of the radar, and perform speed extension based on the maximum unambiguous speed; b. Obtain all possible speed combinations of each radar, perform speed information fusion, and obtain the target fuzzy speed under each combination; c. Taking the target position as the starting point, obtain the target radial velocity positioning map; d. The intersection points are detected and screened based on the chi-square distribution of Mahalanobis distance. The intersection targets are regarded as the true speed of the reference node targets. According to the fuzzy number of the true speed point, the true speed measured by each radar is calculated to obtain the true speed of the target.

[0006] Further, the step S1 includes: the coordinates of radar I in the distributed radar system are , where I = A, B, C, D, and the coordinates of the target P are ; Measure the apparent distance of radar I ,pass Calculate the maximum unambiguous range of radar I ;in HPRF is the radar pulse repetition frequency, c is the speed of light, and the actual distance from radar I to target P , the apparent distance of radar I Maximum unambiguous distance to radar I satisfy ,in is the maximum detection distance of radar I, and INT means rounding down.

[0007] Preferably, step S2 comprises: assuming radar A as a reference radar, constituting a first radar-target system with radar B and target P, and establishing a target positioning equation by the cosine theorem: ,in is the acute angle between the line connecting the radar A and the target P and the horizontal coordinate axis, is the distance between radar A and radar B; similarly, determine the target positioning equation constructed by radar C, radar D and radar A, and get the total target positioning equation as follows: ,in is the fuzzy number of radar B, is the fuzzy number of radar C, is the fuzzy number of radar D, is the distance between radar A and radar C, is the distance between radar A and radar D; each fuzzy number is traversed to obtain the function curve.

[0008] Preferably, the method comprises: after obtaining all the curves, solving the intersection of two sets of target positioning equations consisting of radar A and radar B, radar A and radar C, solving the intersection of two sets of target positioning equations consisting of radar A and radar B, radar A and radar D, and obtaining the intersection of the function curve of radar A and radar B and the function curve of radar A and radar C as , the intersection of the function curves of radar A and radar B and the function curves of radar A and radar D is .

[0009] Preferably, step S4 comprises: For intersection Any point on For intersection Any point on and The squared Mahalanobis distance is ,in is the covariance matrix, T represents transposition, and the hypothesis testing model is established ,set up express The corresponding target distance is the real distance. express The corresponding target distance is the fuzzy distance; the Gaussian distribution of the measurement error is ; According to the operation rules of Gaussian distribution, ,in is the angle measurement accuracy, is the ranging accuracy, if If holds, then the squared Mahalanobis distance obey Distribution, that is, chi-square distribution; the extended distance association test is obtained as: ;in is the threshold, satisfying ,in is the degree of freedom, is the significance level. For this system, the degrees of freedom are 2. The significance level is used to control the number and accuracy of the selected data points.

[0010] Preferably, step a comprises: the coordinates of radar I in the distributed radar system are , where I = A, B, C, D, with target P as the coordinate origin, forming the second radar-target system, is the maximum unambiguous velocity that radar I can measure, where is the signal wavelength, LPRF is the pulse repetition frequency; , is the maximum speed detectable by radar I, INT means rounding down, the apparent speed of radar I , True Speed and the maximum unambiguous speed satisfy: .

[0011] Preferably, the step b comprises: fusing the speed information of the radars, selecting radar A and radar B as a first combination, radar C and radar D as a second combination, and setting , , Substituting into the velocity extension, we have: , ,in is the fuzzy number of radar A, is the fuzzy number of radar B, is the fuzzy number of radar C, is the fuzzy number of radar D. Different fuzzy speeds are obtained by combining different fuzzy numbers.

[0012] Preferably, step c comprises: taking the target position as a starting point, obtaining a scatter plot through all target speeds of the combination of radar A and radar B, and all target speeds of the combination of radar C and radar D.

[0013] Preferably, step d comprises: assuming that radar A and radar B constitute a radar AB system, the actual velocity vector of the target of the radar AB system is: ,in is the acute angle between the line connecting the radar A and the target P and the horizontal coordinate axis, is the acute angle between the line connecting the radar B and the target P and the horizontal coordinate axis. Taking partial derivatives of the variables in this formula, we get the transformation matrix: , get the actual target velocity vector The error covariance is: ,in is a diagonal matrix, is the angle measurement accuracy of radar A, is the angle measurement accuracy of radar B, is the speed measurement accuracy of radar A, is the speed measurement accuracy of radar B, T is the transpose, and similarly we can get , according to Gaussian distribution, the covariance matrix is: ; The squared Mahalanobis distance is: ,in ,Then a chi-square test is performed using a preset threshold to screen out the target speed.

[0014] The beneficial effects of the present invention are: 1) Taking advantage of the system and structure advantages of distributed radar, a chi-square test algorithm based on Mahalanobis distance is established on the basis of radar collaboration. Under the premise of using only one pulse repetition frequency, the ranging ambiguity, speed ambiguity and speed-ranging ambiguity problems existing in radar detection are solved.

[0015] 2) Realize single pulse repetition frequency deambiguation. Through the joint work of multiple radar nodes, the collaborative information of radar data is used to improve the effect of target distance and speed deambiguation, and achieve more accurate target positioning. At the same time, distributed radar is used for deambiguation, avoiding the traditional multi-pulse repetition frequency method, reducing the complexity of radar signal processing, increasing the utilization rate of spectrum resources, and improving efficiency.

[0016] 3) The present invention is applied to a distributed radar system, using the data of multiple radar nodes for collaborative deambiguation to achieve accurate positioning of the target. This distributed radar system can effectively improve the coverage range and target detection capability of the radar system. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a flow chart of a distributed radar collaborative range ambiguity resolution method according to an embodiment of the present invention; Figure 2 A first radar-target system model diagram according to an embodiment of the present invention; Figure 3 This is a flow chart of a distributed radar collaborative velocity ambiguity resolution method according to an embodiment of the present invention; Figure 4 A second radar-target system model diagram according to an embodiment of the present invention; Figure 5 4 is a radar AB system model diagram of an embodiment of the present invention. DETAILED DESCRIPTION

[0018] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0019] Exemplarily, the present invention provides a method for collaboratively resolving distance ambiguity and speed ambiguity based on distributed radar, including resolving distance ambiguity and resolving speed ambiguity. Collaboratively resolving distance ambiguity does not require the speed information of the target, while resolving speed ambiguity requires distance information. Therefore, when distance ambiguity and speed ambiguity exist at the same time, it is chosen to resolve the distance ambiguity first and then the speed ambiguity; the distributed radars collaboratively resolve the distance ambiguity to obtain the position of the target, and also obtain the position of the radar relative to the target; coordinate transformation, when resolving the distance ambiguity, one of the radars is used as the coordinate origin, and when resolving the speed ambiguity, in order to facilitate the description of the velocity vector, the target is selected as the coordinate origin, so coordinate transformation is required to connect the resolution of the distance ambiguity and the resolution of the speed ambiguity; the distributed radars collaboratively resolve the speed ambiguity to obtain the radial velocity of the target. When the radar uses high pulse repetition frequency HPRF, there is only ranging ambiguity, not speed ambiguity, and only the range ambiguity needs to be resolved; when using low pulse repetition frequency LPRF, there is only speed ambiguity, not ranging ambiguity, and only the speed ambiguity needs to be resolved; when using medium pulse repetition frequency MPRF, there are ambiguities in both range and speed, because speed information is not used when resolving range ambiguity, and resolving speed ambiguity requires that the relative position of the radar to the target is known, so the range ambiguity needs to be resolved first, and then the speed ambiguity needs to be resolved.

[0020] The flow chart of the distributed radar collaborative range ambiguity resolution method is as follows: Figure 1 As shown, the steps of resolving distance ambiguity specifically include: S1. Measure the radar to obtain the apparent distance, calculate the maximum unambiguous distance of the radar, and perform distance extension based on the maximum unambiguous distance; S2, based on the reference node distance-angle plane collaborative positioning, obtain the positioning equation of each radar relative to the reference radar; S3, finding the intersection point according to the positioning equation of each group of radars; S4, Chi-square distribution detection based on Mahalanobis distance to select intersection points, the intersection target is regarded as the true distance of the reference node target; S5. Calculate the real distance of the target in its coordinate system through the remaining intersection points; The flow chart of the distributed radar collaborative velocity ambiguity resolution method is as follows: Figure 3 As shown in FIG. 1 , the steps of resolving velocity ambiguity specifically include: a. Measure the radar, obtain the apparent speed, calculate the maximum unambiguous speed of the radar, and perform speed extension based on the maximum unambiguous speed; b. Obtain all possible speed combinations of each radar, perform speed information fusion, and obtain the target fuzzy speed under each combination; c. Taking the target position as the starting point, obtain the target radial velocity positioning map; d. The intersection points are detected and screened based on the chi-square distribution of Mahalanobis distance. The intersection targets are regarded as the true speed of the reference node targets. According to the fuzzy number of the true speed point, the true speed measured by each radar is calculated to obtain the true speed of the target.

[0021] Specifically, step S1 includes: the coordinates of radar I in the distributed radar system are , where I = A, B, C, D, and the coordinates of the target P are ; The position coordinates of radar I are known, while the position coordinates of target P are unknown. The apparent distance is obtained by measuring radar I , which can be directly measured by radar I, through Calculate the maximum unambiguous range of radar I ;in HPRF is the radar pulse repetition frequency, c is the speed of light, and the actual distance from radar I to target P , the apparent distance of radar I Maximum unambiguous distance to radar I satisfy ,in is the maximum detection distance of radar I, which is determined by the structure of radar I itself. INT represents rounding down. To determine the true distance of radar I, we need to determine the value of fuzzy number n. By traversing all possibilities of n, we can obtain all possible distance values ​​after distance extension.

[0022] The step S2 includes: assuming that radar A is a reference radar, and radar B and target P form a first radar-target system. The model diagram of the first radar-target system is as follows: Figure 2 As shown, is the distance between radar A and target P, is the distance between radar B and target P, is the distance between radar A and target P, is the distance between radar D and target P, and the target positioning equation is established by the cosine theorem: ,in is the acute angle between the line connecting the radar A and the target P and the horizontal coordinate axis, is the distance between radar A and radar B; There is ambiguity, but a specific numerical sequence can be obtained through distance extension. and is an unknown variable, and the other variables are known variables. Therefore, the two unknown variables can determine a function curve. Similarly, the target positioning equation constructed by radar C, radar D and radar A is determined, and the total target positioning equation is obtained as follows: ,in is the fuzzy number of radar B, is the fuzzy number of radar C, is the fuzzy number of radar D, is the distance between radar A and radar C, is the distance between radar A and radar D; each fuzzy number is traversed, and each different fuzzy number determines a function curve. After all the curves are obtained, because each set of equation curves traverses the possible positions (distance and angle) of the target, the real position point of the target must be on each set of curves. The intersection points of the two sets of target positioning equations composed of radar A and radar B, radar A and radar C are solved, and these intersection points should contain real points. The intersection points of the two sets of target positioning equations composed of radar A and radar B, radar A and radar D are solved. Because the position parameters of the target are unique, if the target distance values ​​measured by each radar are converted to a coordinate system, these values ​​are relatively concentrated. This concentration depends on the measurement error of each radar. However, even if the measurement error is taken into account, this correlation still exists. This correlation determines that the real distance-angle generated by the same target in all node radars will be relatively concentrated when converted to the same real distance-angle coordinate plane. For fuzzy distances or other points, they are relatively divergent after conversion. “Concentration” and “divergence” are actually a manifestation of similarity. Therefore, the chi-square distribution detection method based on Mahalanobis distance can be introduced to identify the target distance. The intersection of the function curve of radar A and radar B and the function curve of radar A and radar C is obtained as , the intersection of the function curves of radar A and radar B and the function curves of radar A and radar D is .

[0023] Specifically, step S4 includes: For intersection Any point on For intersection Any point on and The squared Mahalanobis distance is ,in is the covariance matrix, T represents transposition, and the hypothesis testing model is established ,set up express The corresponding target distance is the real distance. express The corresponding target distance is the fuzzy distance; the Gaussian distribution of the measurement error is , the measurement error is an independent distribution and approximately obeys the zero-mean Gaussian distribution of random variables; according to the operation rules of Gaussian distribution, ,in is the angle measurement accuracy, For the ranging accuracy, these two data are generally given directly. If holds, then the squared Mahalanobis distance obey Distribution, that is, chi-square distribution; the extended distance association test is obtained as: ;in is the threshold, satisfying ,in is the degree of freedom, is the significance level. For this system, the degree of freedom is 2. The significance level is used to control the number and accuracy of the selected data points. After setting the significance level, the data point screening process is implemented by programming. Finally, the point that best represents the true distance of the target can be obtained, that is, the single-frequency distance ambiguity solution is achieved.

[0024] For example, the algorithm process of distributed radar collaborative speed ambiguity resolution is similar to that of distance ambiguity resolution. However, when resolving speed ambiguity, it is not necessary to establish a relationship similar to the target positioning equation. Instead, the synthesis of velocity vectors, that is, velocity information fusion, can be directly used to achieve an effect similar to establishing a target equation and then finding the intersection point. Step a includes: the coordinates of radar I in the distributed radar system are , where I = A, B, C, D, with target P as the coordinate origin, forming the second radar-target system. The second radar-target system model is shown in the figure below: Figure 4 As shown, is the distance between radar A and target P, is the distance between radar B and target P, is the distance between radar A and target P, is the distance between radar D and target P, is the distance between radar A and radar B, is the distance between radar A and radar C, is the distance between radar A and radar D, is the true speed of radar A, is the true speed of radar B, is the true speed of radar C, is the true speed of radar D, is the acute angle between the line connecting the radar A and the target P and the horizontal coordinate axis; unlike the system for resolving range ambiguity, in this system the target is used as the reference point, i.e. the origin of the coordinates. The advantage of this is that the coordinates of the end point of the velocity vector can represent the velocity vector, simplifying the vector synthesis problem into a coordinate calculation problem. In this model, only the velocity measurement ambiguity is considered, so the range measurement is accurate, i.e. both the radar position and the target position are known. is the maximum unambiguous velocity that radar I can measure, where is the signal wavelength, LPRF is the pulse repetition frequency; , The maximum speed that radar I can detect can be set according to the actual situation. It only needs to be greater than the actual speed of the target. INT means rounding down. The distance measurement and speed measurement are symmetrical. The apparent speed of radar I , True Speed and the maximum unambiguous speed satisfy: It should be noted that, because the position of each radar is known, the relationship between the velocity vector and the velocity magnitude is: the velocity vector is equal to the product of the velocity magnitude and the radar position vector.

[0025] Specifically, step b includes: fusing the speed information of the radars, selecting radar A and radar B as the first combination, radar C and radar D as the second combination, and setting , Substituting into the velocity extension, we have: , ,in is the fuzzy number of radar A, is the fuzzy number of radar B, is the fuzzy number of radar C, is the fuzzy number of radar D. By combining different fuzzy numbers, different fuzzy speeds are obtained. Step c includes: taking the target position as the starting point, drawing all target speeds of the combination of radar A and radar B, and all target speeds of the combination of radar C and radar D, and obtaining a scatter plot. Since the starting coordinate of the velocity vector is (0,0), its end coordinate can represent its velocity vector, thereby simplifying the vector diagram into a scatter plot.

[0026] Specifically, similar to solving the distance ambiguity, the two sets of scattered points obtained are similar to the intersection of the two sets of target equations in solving the distance ambiguity. Therefore, the chi-square test algorithm based on Mahalanobis distance can be used to screen the true speed points. The principle is similar to solving the distance ambiguity, mainly to derive the covariance matrix. Step d includes: Assume that radar A and radar B constitute a radar AB system. The radar AB system model diagram is as follows: Figure 5 As shown, the coordinates of radar A are , the coordinates of radar B are , is the distance between radar A and radar B, is the distance from radar A to target P, is the distance from radar B to target P, and the actual velocity vector of the target of radar AB system is: ,in is the acute angle between the line connecting the radar A and the target P and the horizontal coordinate axis, is the acute angle between the line connecting the radar B and the target P and the horizontal coordinate axis. Taking partial derivatives of the variables in this formula, we get the transformation matrix: , get the actual target velocity vector The error covariance is: ,in is a diagonal matrix, is the angle measurement accuracy of radar A, is the angle measurement accuracy of radar B, is the speed measurement accuracy of radar A, is the speed measurement accuracy of radar B, T is the transpose, these values ​​are directly given, and similarly we can get The next steps are similar to solving the range ambiguity problem. We need to find the covariance matrix of the vector difference between the two groups of points, and then find the squared Mahalanobis distance and perform a chi-square test. Because the two groups of radars are independent, their covariances are also independent. Therefore, the covariance matrix of their errors is the covariance matrix of the velocity vector difference. According to the characteristics of the Gaussian distribution, the covariance matrix is ​​obtained as follows: ; The squared Mahalanobis distance is: ,in ,Then a chi-square test is performed using a preset threshold to screen out the target speed.

[0027] The above is only a preferred embodiment of the present invention. It should be understood that the present invention is not limited to the form disclosed herein, and should not be regarded as excluding other embodiments, but can be used in various other combinations, modifications and environments, and can be modified within the scope of the concept described herein through the above teachings or the technology or knowledge of the relevant field. The changes and modifications made by those skilled in the art shall not deviate from the spirit and scope of the present invention, and shall be within the scope of protection of the claims attached to the present invention.

Claims

1. A method for collaboratively resolving range ambiguity and speed ambiguity based on distributed radar, characterized in that: It includes a step of resolving distance ambiguity and a step of resolving speed ambiguity; The step of resolving distance ambiguity specifically includes: S1. Measure the radar to obtain the apparent distance, calculate the maximum unambiguous distance of the radar, and perform distance extension based on the maximum unambiguous distance; S2, based on the reference node distance-angle plane collaborative positioning, obtain the positioning equation of each radar relative to the reference radar; S3, finding the intersection point according to the positioning equation of each group of radars; S4, the intersection points are detected and screened based on the chi-square distribution of Mahalanobis distance, and the intersection point target is regarded as the target true distance of the reference node; S5. Solve the real distance of the target in its coordinate system through the remaining intersection points to obtain the distance from the target to each radar; The step of resolving velocity ambiguity specifically includes: a. Measure the radar, obtain the apparent speed, calculate the maximum unambiguous speed of the radar, and perform speed extension based on the maximum unambiguous speed; b. Obtain all possible speed combinations of each radar, perform speed information fusion, and obtain the target fuzzy speed under each combination; c. Taking the target position as the starting point, obtain the target radial velocity positioning map; d. The intersection points are detected and screened based on the chi-square distribution of Mahalanobis distance. The intersection targets are regarded as the true speed of the reference node targets. According to the fuzzy number of the true speed point, the true speed measured by each radar is calculated to obtain the true speed of the target.

2. The method for collaboratively resolving range ambiguity and speed ambiguity based on distributed radar according to claim 1, characterized in that: The step S1 includes: the coordinates of radar I in the distributed radar system are , where I = A, B, C, D, and the coordinates of the target P are ; Measure the apparent distance of radar I ,pass Calculate the maximum unambiguous range of radar I ;in HPRF is the radar pulse repetition frequency, c is the speed of light, and the actual distance from radar I to target P , the apparent distance of radar I Maximum unambiguous distance to radar I satisfy ,in is the maximum detection distance of radar I, and INT means rounding down.

3. The method for collaboratively resolving range ambiguity and speed ambiguity based on distributed radar according to claim 2, characterized in that: The step S2 includes: assuming that radar A is a reference radar, and radar B and target P form a first radar-target system, and establishing a target positioning equation by the cosine theorem: ,in is the acute angle between the line connecting the radar A and the target P and the horizontal coordinate axis, is the distance between radar A and radar B; similarly, determine the target positioning equation constructed by radar C, radar D and radar A, and get the total target positioning equation as follows: ,in is the fuzzy number of radar B, is the fuzzy number of radar C, is the fuzzy number of radar D, is the distance between radar A and radar C, is the distance between radar A and radar D; each fuzzy number is traversed to obtain the function curve.

4. The method for collaboratively resolving range ambiguity and speed ambiguity based on distributed radar according to claim 3, characterized in that: include: After all the curves are obtained, the intersection of the two sets of target positioning equations consisting of radar A and radar B, radar A and radar C is solved, and the intersection of the two sets of target positioning equations consisting of radar A and radar B, radar A and radar D is solved. The intersection of the function curve of radar A and radar B and the function curve of radar A and radar C is obtained as , the intersection of the function curves of radar A and radar B and the function curves of radar A and radar D is .

5. The method for collaboratively resolving range ambiguity and speed ambiguity based on distributed radar according to claim 4, characterized in that: The step S4 comprises: For intersection Any point on For intersection Any point on and The squared Mahalanobis distance is ,in is the covariance matrix, T represents transposition, and the hypothesis testing model is established ,set up express The corresponding target distance is the real distance, express The corresponding target distance is the fuzzy distance; the Gaussian distribution of the measurement error is ; According to the operation rules of Gaussian distribution, ,in is the angle measurement accuracy, is the ranging accuracy, if If holds, then the squared Mahalanobis distance obey Distribution, that is, chi-square distribution; the extended distance association test is obtained as: ;in is the threshold, satisfying ,in is the degree of freedom, is the significance level. For this system, the degrees of freedom are 2. The significance level is used to control the number and accuracy of the selected data points.

6. The method for collaboratively resolving range ambiguity and speed ambiguity based on distributed radar according to claim 1, characterized in that: The step a comprises: the coordinates of radar I in the distributed radar system are , where I = A, B, C, D, with target P as the coordinate origin, forming the second radar-target system, is the maximum unambiguous velocity that radar I can measure, where is the signal wavelength, LPRF is the pulse repetition frequency; , is the maximum speed detectable by radar I, INT means rounding down, the apparent speed of radar I , True Speed and the maximum unambiguous speed satisfy: .

7. The method for collaboratively resolving range ambiguity and speed ambiguity based on distributed radar according to claim 6, characterized in that: The step b includes: fusing the speed information of the radars, selecting radar A and radar B as a first combination, radar C and radar D as a second combination, and , , Substituting into the velocity extension, we have: , ,in is the fuzzy number of radar A, is the fuzzy number of radar B, is the fuzzy number of radar C, is the fuzzy number of radar D. Different fuzzy speeds are obtained by combining different fuzzy numbers.

8. The method for collaboratively resolving range ambiguity and speed ambiguity based on distributed radar according to claim 7, characterized in that: The step c comprises: taking the target position as a starting point, obtaining a scatter plot through all target speeds of the combination of radar A and radar B, and all target speeds of the combination of radar C and radar D.

9. The method for collaboratively resolving range ambiguity and speed ambiguity based on distributed radar according to claim 8, characterized in that: The step d comprises: assuming that radar A and radar B constitute a radar AB system, the actual velocity vector of the target of the radar AB system is: ,in is the acute angle between the line connecting the radar A and the target P and the horizontal coordinate axis, is the acute angle between the line connecting the radar B and the target P and the horizontal coordinate axis. Taking partial derivatives of the variables in this formula, we get the transformation matrix: , get the actual target velocity vector The error covariance is: ,in is a diagonal matrix, is the angle measurement accuracy of radar A, is the angle measurement accuracy of radar B, is the speed measurement accuracy of radar A, is the speed measurement accuracy of radar B, T is the transpose, and similarly we can get , according to Gaussian distribution, the covariance matrix is: ; The squared Mahalanobis distance is: ,in ,Then a chi-square test is performed using a preset threshold to screen out the target speed.

Images