A radar echo-based three-dimensional coordinate system registration method without prior information

Abstract

The application discloses a priori information-free three-dimensional coordinate system registration method based on radar echo, and the method comprises the following steps: obtaining the distance-Doppler domain signals of two radars through the moving target detection of the target echo signals obtained by the two radars; screening the unit with the highest amplitude and further obtaining the distance-Doppler domain index of the radar; registering the distance-Doppler domain index through a conversion formula; establishing a target function according to the registered distance-Doppler domain index and solving the target function through a genetic algorithm; and finally, efficiently and accurately obtaining the parameters of the rotation matrix and the translation vector between the radar A and the radar B through gradient descent optimization.

Description

Technical Field

[0001] The present invention belongs to the technical field of radar echoes, and in particular relates to a three-dimensional coordinate system registration method without prior information based on radar echoes. Background Art

[0002] A denial environment refers to environmental conditions that hinder, restrict, or prevent specific actions or activities. In the military, this can include powerful enemy air defense systems, anti-ship missile networks, and electronic warfare jamming, making it difficult for a force to successfully conduct air, sea, or land combat operations within the area. When a radar is in a denial environment—that is, when it is heavily jammed by enemy radar—our command center cannot obtain its specific location and posture.

[0003] In the prior art, the patent application "A Spatial-Temporal Registration Method for Arbitrarily Motion Networked Radars" with publication number CN112346024A discloses a range-Doppler domain spatial registration algorithm for networked radars at low communication rates. After moving target detection, part of the signal is transmitted to the fusion center, and the correlation of the signal extracted by the local radar is constructed as the objective function. The maximization problem of the objective function is solved by a genetic algorithm to obtain the estimated values ​​of the rotation matrix and translation vector.

[0004] Low communication volume is achieved by filtering out the information with larger amplitude in the signal after moving target detection and transmitting it. In the subsequent process, multiple coherent processing interval cycles are performed on each target, that is, several points in the trajectory of a target are selected for alignment. Moreover, due to the short coherent processing interval time, the target trajectory is approximately on a straight line, which will cause redundant and wasteful data.

[0005] In the paper "Application of SVD Algorithm in Building Point Cloud Data Registration", the SVD algorithm is used in point cloud data registration to calculate the rotation matrix and translation vector to achieve the registration of physical scanned point cloud data. When the prerequisites for using the quaternion array method cannot be met, the SVD algorithm can still perform the calculation.

[0006] The algorithm uses an original point set consisting of 1800 3D points and rotates them around the x, y, and z axes by known angles to obtain the matrix required for registration. Using the SVD decomposition algorithm, the calculated rotation matrix is ​​compared with the known rotation matrix to determine the algorithm's accuracy. This algorithm uses 1800 data pairs and simulates a scene without translation changes. The data volume is large and the scene is too simple. When the point cloud data is small, the rotation matrix and translation vector cannot be accurately calculated. Summary of the Invention

[0007] In order to solve the above problems existing in the prior art, the present invention provides a three-dimensional coordinate system registration method without prior information based on radar echo:

[0008] The technical problem to be solved by the present invention is achieved by the following method, comprising the following steps:

[0009] Initial scene setting steps: Create an initial scene including the target, radar A and radar B, and detect the target through radar A and radar B respectively to obtain the target echo signal of radar A and the target echo signal of radar B;

[0010] Moving target detection step: performing moving target detection on the target echo signal of radar A and the target echo signal of radar B respectively to obtain the range-Doppler domain signal of radar A and the range-Doppler domain signal of radar B;

[0011] Screening and registration steps: The range-Doppler domain signal of radar A and the range-Doppler domain signal of radar B are screened to obtain the highest amplitude unit of the target echo signal of radar A and the highest amplitude unit of the target echo signal of radar B, and the range-Doppler domain index (D) of the target in the coordinate system of radar A is determined according to the highest amplitude unit of the target echo signal. A ,R A ) and the range-Doppler domain index (D B ,R B ), according to the conversion formula (D A ,R A ) and (D B ,R B ) are respectively registered to obtain the range-Doppler domain index (X B ,Y B ) and the range-Doppler domain index (X A ,Y A );

[0012] Rounding steps: Use the rounding formula to round (X B ,Y B ) and (X A ,Y A ) are rounded to obtain the rounded range-Doppler domain index (X B1 ,Y B1 ) and (X A1 ,Y A1 );

[0013] Objective function setting and solution steps: According to (X B1 ,Y B1 ) and (X A1 ,Y A1) setting an objective function; using a genetic algorithm to calculate and solve the objective function to obtain parameters that determine the rotation matrix between radar A and radar B and the parameters of the translation vector between radar A and radar B;

[0014] Gradient descent optimization step: The parameters of the rotation matrix between radar A and radar B and the parameters of the translation vector between radar A and radar B are optimized by the gradient descent method to obtain the optimized parameters of the rotation matrix between radar A and radar B and the parameters of the translation vector between radar A and radar B.

[0015] Furthermore, moving target detection includes:

[0016] The target echo signals of radar A and radar B are divided into range units, and the slow-time signals within the range units are discrete Fourier transformed to obtain a first one-dimensional spectrum and a second one-dimensional spectrum. The range-Doppler domain signal of radar A is determined based on the first one-dimensional spectrum and the range units, and the range-Doppler domain signal of radar B is determined based on the second one-dimensional spectrum and the range units.

[0017] Furthermore, the conversion formula includes a conversion formula for converting the target under radar B coordinates to radar A coordinates and a conversion formula for converting the target under radar A coordinates to radar B coordinates;

[0018] The conversion formula for converting the target from the radar B coordinate to the radar A coordinate is:

[0019] PA=R×PB+r

[0020] The conversion formula for converting the target from the radar A coordinate to the radar B coordinate is:

[0021] PB=R×PA+r

[0022] Where PA is the target in the radar A coordinate system, PB is the target in the radar B coordinate system, R is the 3×3 rotation matrix between radar A and radar B, and r is the 3×1 translation vector between radar A and radar B.

[0023] Furthermore, R is represented as follows:

[0024]

[0025] Among them, α, β, and γ represent the angles of rotation around the z, y, and x axes respectively.

[0026] Furthermore, r is represented as follows:

[0027] r=[rx,ry,rz]

[0028] Among them, rx, ry, and rz represent the translation distances along the x, y, and z axes, respectively.

[0029] Furthermore, the polar coordinate representations of PA and PB are:

[0030]

[0031] Among them, ρ A ,θ A , are the range, elevation and azimuth of the target acquired by radar A in the local coordinate system; ρ B ,θ B , are the range, elevation angle, and azimuth of the target in the local coordinate system obtained by radar B respectively.

[0032] Furthermore, the range-Doppler domain index corresponding to the target echo signal of radar A in radar B is expressed as follows:

[0033]

[0034] The range-Doppler domain index corresponding to the target echo signal of radar B in radar A is expressed as follows:

[0035]

[0036] Among them, X B Y is the velocity unit index corresponding to the target echo signal of radar B in radar A, B is the range unit index corresponding to the target echo signal of radar B in radar A; X A Y is the speed unit index corresponding to the target echo signal of radar A in radar B, A is the range unit index corresponding to the target echo signal of radar A in radar B; Δf k =v A / Δd A -v B / Δd B , Δf k is the discretization degree of the target radial velocity at the velocity resolution unit scale, v A 、v B are the radial velocities of the target relative to radars A and B, Δd A , Δd B are the velocity resolution units of radar A and radar B, ΔR A , ΔR B are the range resolution units of radar A and B, R A 、D Aare the distance unit index of the target in the radar A coordinate system and the speed unit index of the target in the radar A coordinate system, R B 、D B They are the range unit index of the target in the radar A coordinate system and the speed unit index of the target in the radar A coordinate system respectively.

[0037] Furthermore, the rounding formula is:

[0038] X B1 =X B

[0039]

[0040] X A1 =X A

[0041]

[0042] Among them, X B1 Y is the velocity unit index corresponding to the rounded target echo signal of radar B in radar A, B1 is the range unit index corresponding to the rounded target echo signal of radar B in radar A; X A1 Y is the velocity unit index corresponding to the rounded target echo signal of radar A in radar B, A1 is the range unit index corresponding to the rounded target echo signal of radar A in radar B; Yes B The index value obtained by rounding down, Yes B The index value obtained by rounding up, Yes A The index value obtained by rounding down, Yes A The index value obtained by rounding up, floor is the floor function, and ceil is the ceiling function.

[0043] Furthermore, according to (X B1 ,Y B1 ) and (X A1 ,Y A1 )Set the objective function:

[0044] According to (X B1 ,Y B1 ) and (X A1 ,Y A1 ) respectively determining a first specific unit of the target echo signal and a second specific unit of the target echo signal;

[0045] The target echo signal amplitude in the first specific unit and the target echo signal amplitude corresponding to the range-Doppler domain index of the target in the radar A coordinate system are normalized and then multiplied to obtain the correlation coefficient corr1;

[0046] The target echo signal amplitude in the second specific unit and the target echo signal amplitude corresponding to the range-Doppler domain index of the target in the radar B coordinate system are normalized and multiplied to obtain the correlation coefficient corr2;

[0047] Normalize the target echo signal amplitude in the first specific unit and the target echo signal amplitude in the second specific unit respectively and then multiply them to obtain the correlation coefficient corr3;

[0048] According to the correlation coefficients corr1, corr2 and corr3, the objective function is set as:

[0049] -min([abs(corr1),abs(corr2),abs(corr3)])

[0050] Here, abs stands for absolute value function.

[0051] Compared with the prior art, the present invention has the following beneficial effects:

[0052] The present invention performs moving target detection on target echo signals acquired by two radars to obtain range-Doppler domain signals of the two radars, screens the units with the highest amplitude therein to further obtain the range-Doppler domain indexes of the radars, aligns the range-Doppler domain indexes using a conversion formula, establishes an objective function based on the aligned range-Doppler domain indexes, and solves the problem using a genetic algorithm. Finally, the parameters of the rotation matrix and the parameters of the translation vector between radar A and radar B are obtained through gradient descent optimization. The present invention only requires the coordinates of the target under the two radars and aligns the units with the maximum amplitude of the range-Doppler domain signals, thereby reducing the amount of alignment data and improving the efficiency of parameter solution operations. At the same time, a one-dimensional correlation coefficient is considered, and the parameters obtained by the genetic algorithm are optimized using a gradient descent method, making the parameters more accurate.

[0053] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 This is a flow chart of a three-dimensional coordinate system registration method based on radar echoes without prior information provided by an embodiment of the present invention;

[0055] Figure 2 is an initial scene graph provided by an embodiment of the present invention;

[0056] Figure 3is a graph of the number of iterations and the corresponding objective function provided by an embodiment of the present invention;

[0057] Figure 4 1 is a diagram of an iterative process of the parameter α of the rotation matrix between radar A and radar B provided by an embodiment of the present invention;

[0058] Figure 5 1 is a diagram of an iterative process of the parameter β of the rotation matrix between radar A and radar B provided by an embodiment of the present invention;

[0059] Figure 6 is a diagram of the iterative process of the parameter γ of the rotation matrix between radar A and radar B provided by an embodiment of the present invention;

[0060] Figure 7 1 is a diagram of an iterative process of a parameter rx of a translation vector between radar A and radar B provided by an embodiment of the present invention;

[0061] Figure 8 is a diagram of an iterative process of the parameter ry of the translation vector between radar A and radar B provided by an embodiment of the present invention;

[0062] Figure 9 is a diagram of an iterative process of a parameter rz of a translation vector between radar A and radar B provided by an embodiment of the present invention;

[0063] Figure 10 It is a graph of the objective function after the genetic algorithm solves and then the gradient descent optimization is performed, provided by an embodiment of the present invention;

[0064] Figure 11 This is a target image of radar B registered to the radar A coordinate system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0065] In order to further illustrate the technical means and effects adopted by the present invention to achieve the predetermined purpose of the invention, the scheme according to the present invention is described in detail below with reference to the accompanying drawings and specific implementation methods.

[0066] The aforementioned and other technical contents, features, and effects of the present invention are clearly presented in the following detailed description of the specific embodiments in conjunction with the accompanying drawings. Through the description of the specific embodiments, a deeper and more specific understanding of the technical means and effects adopted by the present invention to achieve the intended purpose can be obtained. However, the accompanying drawings are provided for reference and illustration purposes only and are not intended to limit the technical solutions of the present invention.

[0067] It should be noted that, in this document, the terms such as first and second, etc., are used merely to distinguish one entity or operation from another, and are not necessarily required by the presence of these terms in the description to imply that any such actual relationship or order of precedence exists between or among the entities or operations. Also, the terms "include," "comprise" or any other variant are intended to cover a non-exclusive inclusion, such that an item or apparatus that includes a list of elements is not limited to those elements, but can include other elements not expressly listed or inherent to such item or apparatus. Without more limitations, an element defined by the phrase "comprising a..." does not exclude the presence of additional identical elements in the item or apparatus that includes the element.

[0068] As shown in Figure 1 , a flow chart of a radar echo-based three-dimensional coordinate system registration method without prior information provided by an embodiment of the application, the method comprising the following steps:

[0069] Step 1, initial scene setting: an initial scene including a target, radar A and radar B is established, the target echo signal of radar A and the target echo signal of radar B are obtained by detecting the target through radar A and radar B respectively, and the distance p B , pitch angle θ A and azimuth angle of the target obtained by radar A in the local coordinate system are obtained according to the target echo signal. B , pitch angle θ B and azimuth angle

[0070] Step 2, moving target detection: the radar A distance-Doppler domain signal and the radar B distance-Doppler domain signal are obtained by respectively performing moving target detection on the radar A target echo signal and the radar B target echo signal.

[0071] Specifically, the moving target detection divides the radar A target echo signal and the radar B target echo signal according to distance units respectively, and calculates the first one-dimensional spectrum and the second one-dimensional spectrum by performing discrete Fourier transform on the slow-time signal in the distance unit; the radar A distance-Doppler domain signal is determined according to the first one-dimensional spectrum and the distance unit, and the radar B distance-Doppler domain signal is determined according to the second one-dimensional spectrum and the distance unit. Compared with the echo signal, the distance-Doppler domain signal is accumulated in the frequency domain, which is beneficial to extract the target information under low signal-to-noise ratio.

[0072] Step 3, screening and registration.

[0073] Step 3.1: Filter the range-Doppler domain signals of radar A and radar B to obtain the unit with the highest amplitude of the target echo signal of radar A and radar B, and select the unit with the highest amplitude as the unit where the target is located.

[0074] Step 3.2: Determine the range-Doppler domain index (D) of the target in the radar A coordinate system according to the highest amplitude unit of the target echo signal. A ,R A ) and the range-Doppler domain index (D B ,R B ).

[0075] Specifically, (D A ,R A ) includes the range unit index R of the target in the radar A coordinate system A and the target's velocity unit index D in the radar A coordinate system A ,(D B ,R B ) includes the target's range unit index R in the radar B coordinate system B and the target's velocity unit index D in the radar B coordinate system B The distance unit index is obtained by measuring the delay time of the target echo signal, and the speed unit index is obtained by measuring the Doppler frequency shift of the target echo signal.

[0076] Step 3.3: According to the conversion formula, (D A ,R A ) and (D B ,R B ) are respectively registered to obtain the range-Doppler domain index (X B ,Y B ) and the range-Doppler domain index (X A ,Y A ).

[0077] Specifically, the conversion formula includes a conversion formula for converting a target under radar B coordinates to radar A coordinates and a conversion formula for converting a target under radar A coordinates to radar B coordinates;

[0078] The conversion formula for converting the target from the radar B coordinate to the radar A coordinate is:

[0079] PA=R×PB+r

[0080] The conversion formula for converting the target from the radar A coordinate to the radar B coordinate is:

[0081] PB=R×PA+r

[0082] Where PA is the target in the radar A coordinate system, PB is the target in the radar B coordinate system, R is the 3×3 rotation matrix between radar A and radar B, and r is the 3×1 translation vector between radar A and radar B.

[0083] R represents the following:

[0084]

[0085] Among them, α, β, and γ represent the angles of rotation around the z, y, and x axes respectively.

[0086] r represents the following:

[0087] r=[rx,ry,rz]

[0088] Among them, rx, ry, and rz represent the translation distances along the x, y, and z axes, respectively.

[0089] The polar coordinates of PA and PB are:

[0090]

[0091] Among them, ρ A ,θ A , are the range, elevation and azimuth of the target acquired by radar A in the local coordinate system; ρ B ,θ B , are the range, elevation angle, and azimuth of the target in the local coordinate system obtained by radar B respectively.

[0092] The range-Doppler domain index corresponding to the target echo signal of radar A in radar B is expressed as follows:

[0093]

[0094] The range-Doppler domain index corresponding to the target echo signal of radar B in radar A is expressed as follows:

[0095]

[0096] Among them, X B Y is the velocity unit index corresponding to the target echo signal of radar B in radar A, B is the range unit index corresponding to the target echo signal of radar B in radar A; X A Y is the speed unit index corresponding to the target echo signal of radar A in radar B, A is the range unit index corresponding to the target echo signal of radar A in radar B; Δfk = v A / Δd A -v B / Δd B , Δf k is the discretization degree of the radial velocity of the target at the scale of the velocity resolution cell, v A , v B are the radial velocities of the target relative to radar A and B, respectively, Δd A , Δd B are the velocity resolution cells of radar A and radar B, respectively, ΔR A , ΔR B are the range resolution cells of radar A and B, respectively, R A , D A are the range cell index of the target in the radar A coordinate system and the velocity cell index of the target in the radar A coordinate system, respectively, R B , D B are the range cell index of the target in the radar A coordinate system and the velocity cell index of the target in the radar A coordinate system, respectively.

[0097] Step 4, since (X B , Y B ) and (X A , Y A ) are not necessarily integers, it is necessary to take the integer of (X B , Y B ) and (X A , Y A ) to obtain the integer distance-Doppler domain index (X B1 , Y B1 ) and (X A1 , Y A1 ), respectively.

[0098] The integer formula is:

[0099] X B1 = X B

[0100]

[0101] X A1 = X A

[0102]

[0103] wherein X B1 is the integer velocity cell index of the target echo signal of radar B in radar A, Y B1 is the integer range cell index of the target echo signal of radar B in radar A; XA1 Y is the velocity unit index corresponding to the rounded target echo signal of radar A in radar B, A1 is the range unit index corresponding to the rounded target echo signal of radar A in radar B; Yes B The index value obtained by rounding down, Yes B The index value obtained by rounding up, Yes A The index value obtained by rounding down, Yes A The index value obtained by rounding up, floor is the floor function, and ceil is the ceiling function.

[0104] Step 5: Setting and solving the objective function.

[0105] Step 5.1: According to (X B1 ,Y B1 ) and (X A1 ,Y A1 )Set the objective function, specifically:

[0106] According to the rounded range-Doppler domain index (X B1 ,Y B1 ) and (X A1 ,Y A1 ) respectively determine a first specific unit and a second specific unit of the target echo signal; normalize the target echo signal amplitude in the first specific unit and the target echo signal amplitude corresponding to the range-Doppler domain index of the target in the radar A coordinate system, and then multiply them to obtain a correlation coefficient corr1; normalize the target echo signal amplitude in the second specific unit and the target echo signal amplitude corresponding to the range-Doppler domain index of the target in the radar B coordinate system, and then multiply them to obtain a correlation coefficient corr2; normalize the target echo signal amplitude in the first specific unit and the target echo signal amplitude in the second specific unit, and then multiply them to obtain a correlation coefficient corr3; and set the objective function according to the correlation coefficients corr1, corr2, and corr3 as follows:

[0107] -min([abs(corr1),abs(corr2),abs(corr3)])

[0108] Here, abs stands for absolute value function.

[0109] Step 5.2: Use the genetic algorithm to calculate and solve the objective function to obtain the parameters that determine the rotation matrix between radar A and radar B and the parameters of the translation vector between radar A and radar B.

[0110] Specifically, each parameter of the genetic algorithm and its upper and lower bounds are specified. The parameters are divided into parameters α, β, γ that determine the rotation matrix R between radar A and radar B, and parameters of the translation vector r between radar A and radar B. α, β, γ represent the angles of rotation around the z, y, and x axes, respectively, and rx, ry, and rz represent the translation distances along the x, y, and z axes, respectively. The upper and lower bounds of the first three angle parameters α, β, and γ are [-pi / 2, pi / 2], and the upper and lower bounds of the last three translation distance parameters are [-3000, 3000].

[0111] 200,000 initial populations were randomly generated and the objective function value of each individual was calculated. The minimum and average values ​​were found, and the individual with the minimum objective function value was selected for crossover mutation to generate a new generation of population until the iteration termination condition was met. The iteration termination condition of the genetic algorithm was that the objective function value was less than -0.95.

[0112] Step 6, gradient descent optimization step: The parameters of the rotation matrix between radar A and radar B and the parameters of the translation vector between radar A and radar B are optimized by the gradient descent method to obtain the optimized parameters of the rotation matrix between radar A and radar B and the parameters of the translation vector between radar A and radar B.

[0113] Specifically, when the genetic algorithm ends and the objective function value is less than -0.95, the gradient descent method is used to optimize the parameters of the rotation matrix between radar A and radar B and the parameters of the translation vector between radar A and radar B. The gradient descent method uses the result obtained by the genetic algorithm as the initial value, calculates the gradient of the objective function for each parameter at this point, and updates the parameter size according to the gradient value to obtain the optimized parameter result. The optimized parameters are the parameters α, β, γ that determine the rotation matrix between radar A and radar B and the parameters rx, ry, rz of the translation vector between radar A and radar B.

[0114] like Figure 2 As shown, this is the initial scene diagram set by the present invention. The initial rotation angles between the radar A coordinate system and the radar B coordinate system are pi / 3, pi / 3, 0, respectively. The origin of the radar B coordinate system in the radar A coordinate system is 500, 200, and 300.

[0115] like Figure 3 As shown in the figure, the number of iterations and the corresponding objective function diagram of the present invention are shown, where the horizontal axis is the number of iterations and the vertical axis is the objective function -min([abs(corr1),abs(corr2),abs(corr3)]). It can be seen that the objective function value tends to be stable after 20 iterations.

[0116] like Figure 4As shown in FIG, it is an iterative process diagram of the parameter α of the rotation matrix between radar A and radar B of the present invention. It can be seen that α gradually tends to be stable during the iterative process of the genetic algorithm.

[0117] like Figure 5 As shown in FIG, it is an iterative process diagram of the parameter β of the rotation matrix between radar A and radar B of the present invention. It can be seen that β gradually tends to be stable during the iterative process of the genetic algorithm.

[0118] like Figure 6 As shown in FIG, it is an iterative process diagram of the parameter γ of the rotation matrix between radar A and radar B of the present invention. It can be seen that γ gradually tends to be stable during the iterative process of the genetic algorithm.

[0119] like Figure 7 FIG. 1 is an iterative process diagram of the parameter rx of the translation vector between radar A and radar B of the present invention. It can be seen that rx gradually tends to be stable during the iterative process of the genetic algorithm.

[0120] like Figure 8 As shown in FIG, it is an iterative process diagram of the parameter ry of the translation vector between radar A and radar B of the present invention. It can be seen that ry gradually tends to be stable during the iterative process of the genetic algorithm.

[0121] like Figure 9 As shown in FIG, it is an iterative process diagram of the parameter rz of the translation vector between radar A and radar B of the present invention. It can be seen that rz gradually tends to be stable during the iterative process of the genetic algorithm.

[0122] like Figure 10 The above is the objective function value corresponding to the number of iterations after the genetic algorithm is solved and then the gradient descent optimization is performed. It can be seen that the objective function value is about -0.997752 after the gradient descent optimization.

[0123] As shown in Table 1, this is the final minimum value of the objective function value and the corresponding optimal solution of the parameters in the embodiment of the present invention.

[0124] Table 1 Minimum values ​​of target parameters and corresponding optimal solutions

[0125]

[0126] It can be seen from Table 1 that when the minimum value of the objective function is -0.9978, the parameters determining the rotation matrix parameters α, β, γ between radar A and radar B and the parameters rx, ry, rz of the translation vector between radar A and radar B are the optimal solutions.

[0127] like Figure 11As shown, the optimal solution parameters α, β, γ, rx, ry, and rz in Table 1 are substituted into the rotation matrix and the translation vector to obtain the target of radar B being aligned to the coordinate system of radar A. It can be seen that there is a good degree of consistency with the target in the coordinate system of A. Therefore, the parameters α, β, and γ that determine the rotation matrix between radar A and radar B and the parameters rx, ry, and rz of the translation vector between radar A and radar B obtained by the present invention have high accuracy.

[0128] This embodiment of the present invention provides a radar echo-based three-dimensional coordinate system registration method without prior information. The method performs moving target detection on target echo signals acquired by two radars to obtain range-Doppler domain signals of the two radars. The units with the highest amplitude are screened to further obtain the radar range-Doppler domain indexes. The range-Doppler domain indexes are registered using a conversion formula. An objective function is established based on the registered range-Doppler domain indexes and solved using a genetic algorithm. Finally, the parameters of the rotation matrix and the parameters of the translation vector between radar A and radar B are obtained through gradient descent optimization. The method only requires the coordinates of the target under the two radars. By registering the units with the maximum amplitude of the range-Doppler domain signals, the amount of registration data is reduced and the efficiency of parameter solution operation is improved. The method also considers the one-dimensional correlation coefficient and uses the gradient descent method to optimize the parameters obtained by the genetic algorithm, making the parameters more accurate.

[0129] The above is a further detailed description of the present invention in conjunction with specific preferred embodiments, and the specific implementation of the present invention should not be considered to be limited to these descriptions. For those skilled in the art of the present invention, without departing from the concept of the present invention, several simple deductions or substitutions can be made, which should be considered to fall within the scope of protection of the present invention.

Claims

1. A three-dimensional coordinate system registration method based on radar echo without prior information, characterized in that: The following steps are involved: Initial scene setting steps: Create an initial scene including the target, radar A and radar B, and detect the target through radar A and radar B respectively to obtain the target echo signal of radar A and the target echo signal of radar B; Moving target detection step: performing moving target detection on the target echo signal of radar A and the target echo signal of radar B respectively to obtain the range-Doppler domain signal of radar A and the range-Doppler domain signal of radar B; The moving target detection includes: dividing the target echo signal of radar A and the target echo signal of radar B according to range units, performing discrete Fourier transform calculation on the slow-time signal in the range unit to obtain a first one-dimensional spectrum and a second one-dimensional spectrum; determining the range-Doppler domain signal of radar A based on the first one-dimensional spectrum and the range unit, and determining the range-Doppler domain signal of radar B based on the second one-dimensional spectrum and the range unit; Screening and registration steps: The range-Doppler domain signal of radar A and the range-Doppler domain signal of radar B are screened to obtain the highest amplitude unit of the target echo signal of radar A and the highest amplitude unit of the target echo signal of radar B. The range-Doppler domain index of the target in the coordinate system of radar A is determined based on the highest amplitude unit of the target echo signal. and the range-Doppler domain index in the radar B coordinate system , according to the conversion formula and The target echo signal of radar A is registered and the corresponding range-Doppler domain index in radar B is obtained respectively ( ) and the range-Doppler domain index corresponding to the target echo signal of radar B in radar A ( ); The conversion formula includes a conversion formula for converting a target under radar B coordinates to radar A coordinates and a conversion formula for converting a target under radar A coordinates to radar B coordinates; The conversion formula for converting the target from the radar B coordinate to the radar A coordinate is: The conversion formula for converting the target from the radar A coordinate to the radar B coordinate is: in, PA is the target in the radar A coordinate system, PB is the target in the radar B coordinate system, R is the 3×3 dimensional rotation matrix between radar A and radar B, and r is the 3×1 dimensional translation vector between radar A and radar B; Rounding steps: Use the rounding formula to round ( )and( ) are rounded to obtain the rounded range-Doppler domain index ( )and( ); Objective function setting and solution steps: According to ( )and( ) setting an objective function; using a genetic algorithm to calculate and solve the objective function to obtain parameters that determine the rotation matrix between radar A and radar B and the parameters of the translation vector between radar A and radar B; Gradient descent optimization step: The parameters of the rotation matrix between radar A and radar B and the parameters of the translation vector between radar A and radar B are optimized by the gradient descent method to obtain the optimized parameters of the rotation matrix between radar A and radar B and the parameters of the translation vector between radar A and radar B.

2. The radar echo-based three-dimensional coordinate system registration method without prior information according to claim 1, characterized in that: The R is represented as follows: in, Represents the angle of rotation around the z, y, and x axes respectively.

3. The radar echo-based three-dimensional coordinate system registration method without prior information according to claim 1, characterized in that: The r is represented as follows: Among them, rx, ry, and rz represent the translation distances along the x, y, and z axes, respectively.

4. The radar echo-based three-dimensional coordinate system registration method without prior information according to claim 1, characterized in that: PA and PB The polar coordinate representations are: in, , , are the range, elevation and azimuth of the target in the local coordinate system obtained by radar A respectively; , , are the range, elevation angle, and azimuth of the target in the local coordinate system obtained by radar B respectively.

5. The radar echo-based three-dimensional coordinate system registration method without prior information according to claim 1, characterized in that: The range-Doppler domain index corresponding to the target echo signal of radar A in radar B is expressed as follows: The range-Doppler domain index corresponding to the target echo signal of radar B in radar A is expressed as follows: in, is the velocity unit index corresponding to the target echo signal of radar B in radar A, The target echo signal of radar B corresponds to the range unit index in radar A; is the speed unit index corresponding to the target echo signal of radar A in radar B, The target echo signal of radar A corresponds to the range unit index in radar B; , is the discretization degree of the target radial velocity at the velocity resolution unit scale, are the radial velocities of the target relative to radars A and B, respectively, They are the velocity resolution units of radar A and radar B respectively, 、 are the distance resolution units of radar A and B respectively, 、 are the distance unit index of the target in the radar A coordinate system and the speed unit index of the target in the radar A coordinate system, respectively. 、 They are the range unit index of the target in the radar A coordinate system and the speed unit index of the target in the radar A coordinate system respectively.

6. The radar echo-based three-dimensional coordinate system registration method without prior information according to claim 1, characterized in that: The rounding formula is: =( × +( )× =( × +( )× in, is the velocity unit index corresponding to the rounded target echo signal of radar B in radar A, is the range unit index corresponding to the rounded target echo signal of radar B in radar A; is the velocity unit index corresponding to the rounded target echo signal of radar A in radar B, is the range unit index corresponding to the rounded target echo signal of radar A in radar B; = floor( ) , =ceil( ) , = floor( ) , = ceil( ) , Yes The index value obtained by rounding down, Yes The index value obtained by rounding up, Yes The index value obtained by rounding down, Yes The index value obtained by rounding up, floor is the floor function, ceil is the ceiling function.

7. The radar echo-based three-dimensional coordinate system registration method without prior information according to claim 1, characterized in that: The basis ( )and( ) Setting the objective function includes: according to( )and( ) respectively determining a first specific unit of the target echo signal and a second specific unit of the target echo signal; The target echo signal amplitude in the first specific unit and the target echo signal amplitude corresponding to the range-Doppler domain index of the target in the radar A coordinate system are normalized and then multiplied to obtain the correlation coefficient corr1; The target echo signal amplitude in the second specific unit and the target echo signal amplitude corresponding to the range-Doppler domain index of the target in the radar B coordinate system are normalized and multiplied to obtain the correlation coefficient corr2; Normalize the target echo signal amplitude in the first specific unit and the target echo signal amplitude in the second specific unit respectively and then multiply them to obtain the correlation coefficient corr3; According to the correlation coefficients corr1, corr2 and corr3, the objective function is set as: Here, abs stands for absolute value function.

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