A systematic error correction method using the target track information feedback from the fusion center

Abstract

The present invention discloses a system error correction method using the target track information fed back by a fusion center, including: constructing a first pseudo-linear equation based on the distance measurement value, angle measurement value, radar position information measured by the radar and the target track information output by the fusion center; solving the first pseudo-linear equation based on the least squares method and the weighted least squares method to obtain a first solution result; constructing a second pseudo-linear equation according to the first solution result; solving the second pseudo-linear equation using the weighted least squares method to obtain a second solution result; the second solution result includes the radar position information after error correction, the distance deviation estimate value and the angle deviation estimate value. The present invention is simple and easy to implement, has high average estimation accuracy and wide application.

Description

Technical Field

[0001] The present invention belongs to the field of radar detection, and particularly relates to a system error correction method using the target track information fed back by a fusion center. Background Art

[0002] Radar technology plays an important role in both military and civilian fields. In order to adapt to the increasingly complex electromagnetic environment and geographical environment, the radar system has been continuously innovated, experiencing several important development stages such as solid-state, coherent, digital, and arrayed. Up to now, a single-station radar has been difficult to meet the continuously improving detection performance requirements, and a networked radar detection system has emerged as the times require.

[0003] In a networked radar detection system, multiple radars perform networked collaborative detection, and the fusion center fuses the data measured by each radar, so as to realize detection and tracking and positioning and other detection functions based on the fused data.

[0004] However, in an actual networked radar detection system, a series of uncontrollable external factors cause different degrees of system errors in each radar station itself, including radar position errors, measurement deviations caused by system registration errors, etc. If the radar observation data containing system errors is used in data fusion, it will affect the accuracy of detection and tracking and positioning, resulting in a reduction in radar detection performance. Summary of the Invention

[0005] In order to solve the above problems existing in the prior art, the present invention provides a system error correction method using the target track information fed back by a fusion center.

[0006] The technical problems to be solved by the present invention are realized through the following technical solutions:

[0007] A system error correction method using the target track information fed back by a fusion center includes:

[0008] Construct a first pseudo-linear equation according to the distance measurement value, angle measurement value, radar position information measured by the radar, and the target track information output by the fusion center:

[0009] Solve the first pseudo-linear equation based on the least squares method and the weighted least squares method to obtain a first solution result;

[0010] Construct a second pseudo-linear equation according to the first solution result:

[0011] Solve the second pseudo-linear equation using the weighted least squares method to obtain a second solution result; the second solution result includes the radar position information after error correction, the distance deviation estimate value, and the angle deviation estimate value;

[0012] In the first pseudo-linear equation:

[0013]

[0014]

[0015] Δu = u - u o ,

[0016]

[0017] where, O represents the zero matrix, and I represents the identity matrix; Δt k represents the error between the target track information t k output by the fusion center at the k-th (k ∈ [1, N]) time and the corresponding true target track information ; u represents the radar position information measured by the radar, and u o represents the true radar position information; b r represents the range measurement deviation caused by the system registration error, and b θ represents the angle measurement deviation caused by the system registration error; the superscript T represents the matrix transpose; is the unknown of the first pseudo-linear equation;

[0018]

[0019] Δr = [Δr 1 , Δr 2 , …, Δr N T , Δθ = [Δθ 1 , Δθ 2 , …, Δθ N T ,

[0020]

[0021]

[0022]

[0023] where, m rk represents the range measurement value measured by the radar at the k-th (k ∈ [1, N]) time; m θk represents the angle measurement value measured by the radar at the k-th (k ∈ [1, N]) time, Δr k represents the range measurement error affected by noise existing in m rk , and Δθ k ​​Denote m θk The angular measurement error caused by noise in

[0024] In the second pseudo-linear equation:

[0025]

[0026] Δx 1 Denote the first solution result x obtained by solving the unknowns of the first pseudo-linear equation 1 The error between the corresponding true value, x 1 (3) and x 1 (4) respectively take the 3rd and 4th elements in x 1 As the unknowns of the second pseudo-linear equation.

[0027] Preferably, the construction method of the first pseudo-linear equation includes:

[0028] Construct a pseudo-linear equation related to distance measurement:

[0029] Construct a pseudo-linear equation related to angle measurement:

[0030] Simultaneously solve the pseudo-linear equation related to distance measurement, the pseudo-linear equation related to angle measurement, and the linear equation about radar position information Δu = u - u o To obtain the first pseudo-linear equation.

[0031] Preferably, solve the first pseudo-linear equation based on the least squares method and the weighted least squares method to obtain the first solution result, including:

[0032] Use the least squares method to solve the first pseudo-linear equation to obtain the least squares solution

[0033] Use the 3rd element in the least squares solution As the approximation of b r And use The 4th element in θ As the approximation of b m To calculate B t And B

[0034] Substitute the calculated B m And B t Back into the first pseudo-linear equation, and use the weighted least squares method to solve the third pseudo-linear equation obtained at this time to obtain the first solution result;

[0035] ​​Among them, the weight coefficient used when solving the third pseudo-linear equation by weighted least squares method is:

[0036]

[0037] Among them, is the variance at, and diag(·) is a function for constructing a diagonal matrix according to the input parameters; represents the covariance matrix at, and blkdiag(·) is a function for constructing a block diagonal matrix according to the input parameters.

[0038] Preferably, the weight coefficient used when solving the second pseudo-linear equation by weighted least squares method is:

[0039] Preferably, the system error correction method is applied to a networked radar detection system.

[0040] The present invention also provides a system error correction device using the target track information fed back by the fusion center, including:

[0041] An acquisition module, configured to acquire the distance measurement value, angle measurement value, radar position information measured by the radar, and the target track information output by the fusion center;

[0042] A first solving module, configured to substitute the distance measurement value, the angle measurement value, the radar position information, and the target track information into a pre-constructed first pseudo-linear equation, and solve the first pseudo-linear equation based on the least squares method and the weighted least squares method to obtain a first solving result;

[0043] A second solving module, configured to substitute the first solving result into a pre-constructed second pseudo-linear equation, and solve the second pseudo-linear equation by using the weighted least squares method to obtain a second solving result; the second solving result includes the radar position information after error correction, the distance deviation estimation value, and the angle deviation estimation value;

[0044] The first pseudo-linear equation is:

[0045] Among them,

[0046]

[0047] Δu = u - u o ,

[0048]

[0049] Among them, O represents the zero matrix, and I represents the identity matrix; Δt k represents the target track information t output by the fusion center at the k-th (k ∈ [1, N]) time k and the corresponding true target track information The error between them; u represents the radar position information measured by the radar, and u o represents the true radar position information; b r represents the range measurement deviation caused by the system registration error, and b θ represents the angle measurement deviation caused by the system registration error; the superscript T represents the matrix transpose; is the unknown of the first pseudo-linear equation;

[0050]

[0051] Δr = [Δr 1 , Δr 2 , …, Δr N T , Δθ = [Δθ 1 , Δθ 2 , …, Δθ N T ,

[0052]

[0053]

[0054]

[0055] Among them, m rk represents the range measurement value measured by the radar at the k-th (k ∈ [1, N]) time; m θk represents the angle measurement value measured by the radar at the k-th (k ∈ [1, N]) time, Δr k represents the range measurement error affected by noise existing in m rk , and Δθ k represents the angle measurement error affected by noise existing in m θk ;

[0056] The second pseudo-linear equation is:

[0057] Among them,

[0058] Δx 1 represents the unknown of the first pseudo-linear equation ​​The first solution result x obtained by solving 1 The error between the corresponding true value and x 1 (3) and x 1 (4) respectively take the 3rd and 4th elements in x 1 as the unknowns of the second pseudo-linear equation.

[0059] Preferably, the construction method of the first pseudo-linear equation used in the first solution module includes:

[0060] Construct a pseudo-linear equation related to distance measurement:

[0061] Construct a pseudo-linear equation related to angle measurement:

[0062] Simultaneously solve the pseudo-linear equation related to distance measurement, the pseudo-linear equation related to angle measurement, and the linear equation Δu = u - u about radar position information o to obtain the first pseudo-linear equation.

[0063] Preferably, the first solution module solves the first pseudo-linear equation based on the least squares method and the weighted least squares method to obtain the first solution result, including:

[0064] Solve the first pseudo-linear equation using the least squares method to obtain the least squares solution

[0065] Use the 3rd element in the least squares solution as the approximation of b r and use the 4th element in as the approximation of b θ to calculate B m and B t ;

[0066] Substitute the calculated B m and B t back into the first pseudo-linear equation, and use the weighted least squares method to solve the third pseudo-linear equation obtained at this time to obtain the first solution result;

[0067] Among them, when the first solution module uses the weighted least squares method to solve the third pseudo-linear equation, the weight coefficient used is:

[0068]

[0069] Among them, is ​The variance at a certain time, where diag(·) is a function for constructing a diagonal matrix based on the input parameters; denote the covariance matrix at a certain time, where blkdiag(·) is a function for constructing a block diagonal matrix based on the input parameters.

[0070] Preferably, when the second solving module solves the second pseudo-linear equation using the weighted least squares method, the weight coefficient used is:

[0071] Preferably, the system error correction device is applied to a networked radar detection system.

[0072] In the system error correction method using the target track information fed back by the fusion center provided by the present invention, a reasonable model of the system error is established, considering the distance measurement deviation b r caused by the system registration error and the angle measurement deviation b θ , and also considering the distance measurement value m rk output by the fusion center and the errors Δr k and Δθ k caused by noise in the angle measurement value; based on this, by using the target track information fed back by the fusion center and the distance measurement and angle measurement of the target by the radar station, two pseudo-linear equations are constructed, converting the difficult-to-directly-solve non-linear non-convex parameter estimation problem into a linear parameter estimation problem, and a two-step least squares method is proposed to solve the pseudo-linear equations. During the implementation of the entire scheme, it is not necessary to estimate the actual observation noise variance, and it is not necessary to calculate the noise distribution of the pseudo-observation equation, with high calculation efficiency and being simple and easy to implement. Experimental data shows that in the case of low measurement noise, the system error estimation accuracy of the present invention can reach the Cramer-Rao lower bound (CRLB), and it can be widely applied to system error correction in networked radars.

[0073] In summary, the present invention has the characteristics of being simple and easy to implement, high average estimation accuracy, and wide application.

[0074] The following will further elaborate on the present invention in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] Figure 1 is a flowchart of a system error correction method using the target track information fed back by the fusion center provided by an embodiment of the present invention;

[0076] Figure 2 is Figure 1 a simplified flowchart of

[0077] Figure 3It is a schematic diagram of the motion scenario in the simulation verification of the present invention;

[0078] Figure 4 It is a diagram showing the position update results of the radar station under different observation noise conditions of the present invention;

[0079] Figure 5 It is a diagram showing the results of distance measurement deviation estimation under different observation noise conditions of the present invention;

[0080] Figure 6 It is a diagram showing the results of angle measurement deviation estimation under different observation noise conditions of the present invention. Detailed implementation manners

[0081] The following further describes the present invention in detail with reference to specific embodiments, but the implementation manners of the present invention are not limited thereto.

[0082] In order to accurately correct the errors of the networked radar detection system and thus improve the radar detection performance, the embodiments of the present invention provide a system error correction method using the target track information fed back by the fusion center, and this method can be applied to the networked radar detection system.

[0083] The method provided by the embodiments of the present invention utilizes the target track information fed back by the fusion center of the networked radar to each radar to assist in system error correction, which can not only improve the calibration accuracy of the system error, but also reduce the solution space of parameter estimation. Among them, the networked radar belongs to the category of cognitive radar, and its fusion center can not only feed back the target track information, but also feed back the motion state information of the target.

[0084] See Figure 1 As shown, the system error correction method using the target track information fed back by the fusion center provided by the embodiments of the present invention includes the following steps:

[0085] S10: Construct a first pseudo-linear equation according to the distance measurement value, angle measurement value, radar position information measured by the radar, and the target track information output by the fusion center:

[0086] In the application of networked radar target positioning, assume that the true radar position information of the radar station to be corrected is The true value of the target track information is N is the number of times the fusion center feeds back the target track and also the number of observations of the radar; thus, the true distance between the radar and the target track and the true angle measurement value can be expressed as

[0087]

[0088]

[0089] However, in practice, the radar position information measured by the radar and the target track information output by the fusion center will both be affected by measurement and estimation errors, that is

[0090] u = u o + Δu,

[0091]

[0092] where Δu represents the error between u and u o and Δt k represents the error between t k and .

[0093] Assume that Δu and Δt k respectively follow a zero-mean Gaussian distribution, that is, Δu ~ N(0, Q u ) and where Q u represents the covariance matrix of the error contained in u, represents Δt k the covariance matrix of the error contained in it, and these two can be constructed by equations and . Where I represents the identity matrix, is the variance of Δu obtained by pre-measurement, and similarly is the variance of the measured Δt k .

[0094] Thus, the range measurement value m rk and the angle measurement value m θk measured by the radar station are modeled as:

[0095]

[0096]

[0097] where represents the true range between the radar and the target, represents the true angle between the radar and the target, b r represents the range measurement deviation caused by the misregistration error, b θ represents the angle measurement deviation caused by the system registration error; Δr k represents the range measurement error affected by noise existing in m rk , Δθ k represents the angle measurement error affected by noise existing in m θk . Δrk and Δθ k can both be modeled as zero - mean Gaussian distributions, i.e., where is the variance when is the variance when

[0098] Since the problems constructed based on radar observation measurements and target positions are often non - linear and non - convex problems and are difficult to solve directly, the embodiments of the present invention solve this problem by constructing pseudo - linear equations.

[0099] Specifically, first, the unknowns of the first pseudo - linear equation are defined:

[0100]

[0101] where the superscript T represents the matrix transpose, and the superscript T in the rest of the text also represents the matrix transpose. It can be seen that the embodiments of the present invention introduce and u oT b θ two intermediate variables in the constructed first pseudo - linear equation, thereby constructing the first pseudo - linear equation for realizing linear parameter estimation.

[0102] Then, construct the pseudo - linear equation related to distance measurement:

[0103]

[0104] where

[0105]

[0106] Then, construct the pseudo - linear equation related to angle measurement:

[0107]

[0108] where Δθ = [Δθ 1 , Δθ 2 , …, Δθ N T ,

[0109]

[0110]

[0111]

[0112] ​

[0113] For the above-mentioned pseudo-linear equations related to distance measurement, pseudo-linear equations related to angle measurement, and linear equations about radar position information Δu=uu o Perform the combination and get the first pseudo linear equation:

[0114]

[0115] In the second pseudo-linear equation,

[0116]

[0117] Δu=uu o ,

[0118]

[0119] Where O represents the zero matrix, I represents the identity matrix; Δt k represents the target track information t output by the fusion center for the k∈[1,N]th time k Corresponding real target track information The error between them; u represents the radar position information measured by the radar, u o Indicates the real radar position information; b r represents the distance measurement deviation caused by the system registration error, b θ represents the angle measurement deviation caused by the system registration error; the superscript T represents the matrix transpose; is the unknown quantity of the first pseudo linear equation;

[0120]

[0121] Δr=[Δr 1 ,Δr 2 ,…,Δr N ] T , Δθ=[Δθ 1 ,Δθ 2 ,…,Δθ N ] T ,

[0122]

[0123]

[0124]

[0125] Among them, m rk represents the distance measurement value measured by the radar for the k∈[1,N]th time; mθk Denote the angle measurement value obtained by the radar at the \(k^{th}\) (\(k\in[1,N]\)) measurement, \(\Delta r\) k denote \(m\) rk the distance measurement error caused by noise in \(m\), \(\Delta\theta\) k denote \(m\) θk the angle measurement error caused by noise in \(m\).

[0126] S20: Solve the first pseudo-linear equation based on the least squares method and the weighted least squares method to obtain the first solution result.

[0127] Specifically, first use the least squares method to solve the first pseudo-linear equation to obtain the least squares solution

[0128] Since \(B\) in the first pseudo-linear equation m and \(B\) t contain the unknowns \(b\) r and \(b\) θ , so first use the least squares method to solve the first pseudo-linear equation to obtain the least squares solution

[0129] Then, use the third element in the least squares solution as the approximation of \(b\) r , and use the fourth element in it as the approximation of \(b\) θ to calculate \(B\) m and \(B\) t ; Next, substitute the calculated \(B\) m and \(B\) t back into the first pseudo-linear equation, and use the weighted least squares method to solve the third pseudo-linear equation obtained at this time to obtain the first solution result

[0130] where, denote the weight coefficient used when solving the third pseudo-linear equation by the weighted least squares method; is the variance when, \(diag(\cdot)\) is a function to construct a diagonal matrix according to the input parameters; denote the covariance matrix when, \(blkdiag(\cdot)\) is a function to construct a block diagonal matrix according to the input parameters.

[0131] S30: Construct the second pseudo-linear equation according to the first solution result:

[0132] Among them,

[0133] Δx 1 represents the first solution result x obtained by solving the unknowns of the first pseudo-linear equation and the error between the corresponding true value, x 1 (3) and x 1 (4) respectively take the 3rd and 4th elements in x 1 (17), which are the unknowns of the second pseudo-linear equation, and the other parameters can be referred to the above text. Since two intermediate variables are introduced when constructing the first pseudo-linear equation, resulting in poor estimation accuracy of the first solution result, therefore, in the second pseudo-linear equation of the embodiment of the present invention, two intermediate variables are eliminated in order to obtain a solution with higher accuracy.

[0134] S40: Solve the second pseudo-linear equation by using the weighted least squares method to obtain the second solution result; the second solution result includes the radar position information after error correction, the distance deviation estimation value, and the angle deviation estimation value.

[0135]

[0136] Among them, the second solution result is expressed as Among them, is the weight coefficient used when solving the second pseudo-linear equation by using the weighted least squares method.

[0137] It can be understood that the three parameters included in the second solution result respectively correspond to the u included in oT , b r and b θ , since u oT is a two-dimensional vector, so x 2 (1:2) is the radar position update value, x 2 (3) is the distance deviation estimation value, x 2 (4) is the angle deviation estimation value.

[0138] In the system error correction method using the fusion center to feedback the target track information provided by the embodiment of the present invention, the system error is reasonably modeled, considering the distance measurement deviation b r and the angle measurement deviation b θ caused by the system registration error, and also considering the distance measurement value m rk output by the fusion center and the errors Δr k and Δθ k ​Based on this, by utilizing the target track information fed back by the fusion center and the distance measurement and angle measurement of the target by the radar station, two pseudo-linear equations are constructed, converting the non-linear and non-convex parameter estimation problem that is difficult to solve directly into a linear parameter estimation problem, and a two-step least squares method is proposed to solve the pseudo-linear equations. During the implementation process of the entire scheme, it is not necessary to estimate the actual observation noise variance, and it is not necessary to calculate the noise distribution of the pseudo-observation equation, with high computational efficiency and being simple and easy to implement. Experimental data (see the simulation experiment below) shows that in the case of low measurement noise, the system error estimation accuracy of the embodiment of the present invention can reach the Cramer-Rao lower bound (CRLB), and it can be widely applied to system error correction in networked radars.

[0139] To sum up, the embodiment of the present invention has the characteristics of being simple and easy to implement, high average estimation accuracy, and wide application.

[0140] To verify the beneficial effects of the embodiment of the present invention, the following uses simulation experiments to further illustrate the embodiment of the present invention.

[0141] The simulation process adopts a motion scenario as Figure 3 shown. Assume that the true position of the radar station is u o = [5000, 5000] T m, the true value of the initial position of the target track is The target moving speed is v t = -[200, 300] T m / s, and the target track fed back by the fusion center is at 1-second intervals. The distance measurement deviation and angle measurement deviation are set to b r = 30m and b θ = 0.12°. The covariance matrices of the radar station position acquisition error and the fed-back target track error are respectively set as and where σ u = 1m and σ t = 5m.

[0142] During the simulation process, the estimation performances of two comparison methods (comparison method 1 and comparison method 2) are used as references for comparison with the performance of the embodiment of the present invention. Among them, comparison method 1 uses the maximum likelihood estimation method; those skilled in the art are well aware that the maximum likelihood estimation method is a commonly used non-linear estimation method. Since it requires an initial value close to the true value for iterative solution, it is very difficult to use in practice. Therefore, generally only the maximum likelihood estimation method is used as an optimal reference algorithm to measure the estimation performances of other algorithms. Comparison method 2 is a method for estimating the system error by ignoring the measurement deviation on the basis of the embodiment of the present invention. Specifically, although in practice b θ ≠0, b r≠ 0, but in Comparative Method 2, it is considered that b θ = 0, b r = 0, thus only estimating the results generated by the radar position.

[0143] In the simulation, L = 2000 experiments were carried out. During the experiment, different standard deviations σ r of the range observation noise and standard deviation σ θ of the angle observation noise were changed to verify the radar station position update performance, range deviation estimation performance, and angle deviation estimation performance respectively. It is assumed that the measurement errors in each measurement are equal, that is, σ r = σ rk and σ θ = σ θk .

[0144] The estimation performance is measured by the root mean square error (RMSE), and the index definition of RMSE is as follows:

[0145]

[0146] Among them, represents the estimated parameter (x 2 ) in the l-th experiment, and p represents the true value of the estimated parameter L = 2000 represents the total number of experiments.

[0147] Figure 4 The RMSE of the radar station position update is given. It can be seen that the embodiment of the present invention can reach the corresponding CRLB, while Comparative Method 2 has poor performance when the measurement noise level is small.

[0148] Figure 5 The estimation performance of the range deviation is given. When the noise level is not high, the performance of the embodiment of the present invention is close to the CRLB. However, when the noise is large, there is a little difference between the embodiment of the present invention and the maximum likelihood estimation method and the CRLB. Although there is only a 1m gap between the embodiment of the present invention and the maximum likelihood estimation, the computational complexity is greatly reduced.

[0149] Figure 6 The estimation performance of the angle deviation is given. It can be seen that the performance of the embodiment of the present invention is also close to the CRLB.

[0150] In summary, it can be seen that when the measurement noise is not large, the measurement estimation accuracy of the embodiment of the present invention can reach the theoretical performance lower bound. When the noise is large, although the performance of the embodiment of the present invention is slightly lower than that of the maximum likelihood estimation method, since the embodiment of the present invention does not need to estimate the actual observation noise variance and does not need to calculate the noise distribution of the pseudo-observation equation, the computational efficiency of the embodiment of the present invention is higher, and the correction result of the system error can be obtained faster than the maximum likelihood estimation method.

[0151] Based on the same inventive concept, an embodiment of the present invention further provides a system error correction device for using the fusion center to feedback target track information, which is applied to a networked radar detection system. The device includes:

[0152] An acquisition module, configured to acquire the distance measurement value, angle measurement value, radar position information measured by the radar, and the target track information output by the fusion center;

[0153] A first solution module, configured to substitute the distance measurement value, the angle measurement value, the radar position information, and the target track information into a pre-constructed first pseudo-linear equation, and solve the first pseudo-linear equation based on the least squares method and the weighted least squares method to obtain a first solution result;

[0154] A second solution module, configured to substitute the first solution result into a pre-constructed second pseudo-linear equation, and solve the second pseudo-linear equation using the weighted least squares method to obtain a second solution result; the second solution result includes the radar position information after error correction, the distance deviation estimation value, and the angle deviation estimation value.

[0155] Among them, the first pseudo-linear equation is:

[0156] Among them,

[0157]

[0158] Δu = u - u o ,

[0159]

[0160] Among them, O represents a zero matrix, and I represents an identity matrix; Δt k represents the error between the target track information t k output by the fusion center at the k-th time (k ∈ [1, N]) and the corresponding true target track information ; u represents the radar position information measured by the radar, and u o represents the true radar position information; b r represents the distance measurement deviation caused by the system registration error, and b θ represents the angle measurement deviation caused by the system registration error; the superscript T represents the matrix transpose; is the unknown of the first pseudo-linear equation;

[0161]

[0162] Δr = [Δr 1 , Δr2 ,…, Δr N T , Δθ = [Δθ 1 , Δθ 2 ,…, Δθ N T ,

[0163]

[0164]

[0165]

[0166] where m rk represents the distance measurement value measured by the radar for the k-th time, where k ∈ [1, N]; m θk represents the angle measurement value measured by the radar for the k-th time, where k ∈ [1, N], Δr k represents the distance measurement error caused by noise in m rk , and Δθ k represents the angle measurement error caused by noise in m θk ;

[0167] The second pseudo-linear equation is:

[0168] where,

[0169] Δx 1 represents the error between the first solution result x obtained by solving the unknowns of the first pseudo-linear equation and the corresponding true value, and x 1 (3) and x 1 (4) respectively take the 3rd and 4th elements in x 1 , 1 which are the unknowns of the second pseudo-linear equation. Specifically, the construction method of the first pseudo-linear equation used in the first solution module includes:

[0170] (1) Construct a pseudo-linear equation related to distance measurement:

[0171] (2) Construct a pseudo-linear equation related to angle measurement:

[0172] (3) For the pseudo-linear equation related to distance measurement, the pseudo-linear equation related to angle measurement, and the linear equation about radar position information Δu = u - u

[0173] (3) For the pseudo-linear equation related to distance measurement, the pseudo-linear equation related to angle measurement, and the linear equation about radar position information Δu = u - u o ​​By combining them, we get the first pseudo linear equation.

[0174] Specifically, the first solving module solves the first pseudo linear equation based on the least squares method and the weighted least squares method to obtain a first solution result, including:

[0175] (1) Solve the first pseudo linear equation using the least squares method to obtain the least squares solution

[0176] (2) Least squares solution The third element in b is r The approximate value of The 4th element in b is θ To calculate B m and B t ;

[0177] (3) The calculated B m and B t Substituting back into the first pseudo linear equation, and solving the third pseudo linear equation obtained at this time by using the weighted least square method to obtain a first solution result;

[0178] Among them, when the first solving module uses the weighted least square method to solve the third pseudo linear equation, the weight coefficient used is:

[0179]

[0180] in, for The variance when , diag(·) is a function that constructs a diagonal matrix based on the input parameters; express is the covariance matrix when , and blkdiag(·) is a function that constructs a block diagonal matrix based on the input parameters.

[0181] Specifically, when the second solving module uses the weighted least square method to solve the second pseudo linear equation, the weight coefficient used is:

[0182] It should be noted that, for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0183] It should be noted that the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more features. In the description of the present invention, "a plurality of" means two or more, unless otherwise specifically defined.

[0184] In the description of this specification, the description with reference to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine the different embodiments or examples described in this specification.

[0185] Although the present application has been described in conjunction with various embodiments herein, however, in the process of implementing the claimed present application, those skilled in the art can understand and achieve other variations of the disclosed embodiments by viewing the accompanying drawings, the disclosure, and the appended claims.

[0186] The above content is a further detailed description of the present invention in conjunction with specific preferred embodiments, and it cannot be determined that the specific implementation of the present invention is limited only to these descriptions. For those of ordinary skill in the technical field to which the present invention pertains, without departing from the concept of the present invention, several simple deductions or substitutions can still be made, and all should be regarded as belonging to the protection scope of the present invention.

Claims

1. A system error correction method using the target track information feedback from the fusion center, characterized in that, it includes: Construct a first pseudo-linear equation based on the distance measurement value, angle measurement value, radar position information measured by the radar, and the target track information output by the fusion center: Solving the first pseudo-linear equation based on the least squares method and the weighted least squares method to obtain a first solution result; Construct a second pseudo-linear equation according to the first solution result: Solving the second pseudo-linear equation using the weighted least squares method to obtain a second solution result; the second solution result includes the radar position information after error correction, the distance deviation estimate value, and the angle deviation estimate value; In the first pseudo-linear equation: Δu = u - u o , where, O represents a zero matrix and I represents an identity matrix; Δt k represents the error between the target track information t k output by the fusion center at the k-th (k ∈ [1, N]) time and the corresponding true target track information ; u represents the radar position information measured by the radar, and u o represents the true radar position information; b r represents the range measurement deviation caused by the system registration error, and b θ represents the angle measurement deviation caused by the system registration error; the superscript T represents matrix transpose; is the unknown of the first pseudo-linear equation; Δr = [Δr 1 , Δr 2 , …, Δr N T , Δθ = [Δθ 1 , Δθ 2 , …, Δθ N T ,​​ where m rk represents the distance measurement value measured by the radar for the k-th time where k ∈ [1, N]; m θk represents the angle measurement value measured by the radar for the k-th time where k ∈ [1, N], Δr k represents the distance measurement error caused by noise in m rk , and Δθ k represents the angle measurement error caused by noise in m θk . In the second pseudo-linear equation: Δx 1 represents the error between the first solution result x obtained by solving the unknown of the first pseudo-linear equation and the corresponding true value, x 1 (3) and x 1 (4) respectively take the 3rd and 4th elements in x 1 (4) as the unknown of the second pseudo-linear equation 1 and are the 3rd and 4th elements of respectively.

2. The system error correction method according to claim 1, characterized in that, The construction method of the first pseudo-linear equation includes: Construct a pseudo-linear equation related to distance measurement: Construct a pseudo-linear equation related to angular measurement: The pseudo linear equation related to the distance measurement, the pseudo linear equation related to the angle measurement and the linear equation about the radar position information Δu=uu o By combining them, the first pseudo linear equation is obtained.

3. The system error correction method according to claim 1, characterized in that, Solving the first pseudo-linear equation based on the least squares method and the weighted least squares method to obtain a first solution result, including: Solve the first pseudo-linear equation using the least squares method to obtain the least squares solution Using the least squares solution Take the third element in r as the approximation of b, and use Take the fourth element in θ as the approximation of b to calculate B m and B t ; Substitute the calculated B m and B t back into the first pseudo-linear equation, and use the weighted least squares method to solve the third pseudo-linear equation obtained at this time to obtain the first solution result; wherein, the weight coefficient used when solving the third pseudo-linear equation using the weighted least squares method is: Among them, is the variance at, and diag(·) is a function that constructs a diagonal matrix based on the input parameters; denotes the covariance matrix at, and blkdiag(·) is a function that constructs a block diagonal matrix based on the input parameters.

4. The system error correction method according to claim 3, characterized in that, The weight coefficient used when solving the second pseudo-linear equation by weighted least squares method is as follows:

5. The system error correction method according to any one of claims 1 to 4, characterized in that, It is applied to a networked radar detection system.

6. A system error correction device using the target track information feedback from the fusion center, characterized in that, it includes: An acquisition module for acquiring the distance measurement value, angle measurement value, radar position information measured by the radar, and the target track information output by the fusion center; A first solution module for substituting the distance measurement value, the angle measurement value, the radar position information, and the target track information into a pre-constructed first pseudo-linear equation, and solving the first pseudo-linear equation based on the least squares method and the weighted least squares method to obtain a first solution result; A second solution module for substituting the first solution result into a pre-constructed second pseudo-linear equation, and solving the second pseudo-linear equation using the weighted least squares method to obtain a second solution result; the second solution result includes the radar position information after error correction, the distance deviation estimate value, and the angle deviation estimate value; The first pseudo-linear equation is as follows: Among them, Δu = u - u o , where, O represents a zero matrix, and I represents an identity matrix; Δt k represents the error between the target track information t k output by the fusion center at the k-th (k ∈ [1, N]) time and the corresponding true target track information ; u represents the radar position information measured by the radar, and u o represents the true radar position information; b r represents the range measurement deviation caused by the system registration error, and b θ represents the angle measurement deviation caused by the system registration error; the superscript T represents matrix transpose; is the unknown of the first pseudo-linear equation; Δr = [Δr 1 , Δr 2 , …, Δr N T , Δθ = [Δθ 1 , Δθ 2 , …, Δθ N T ,​​ where m rk represents the distance measurement value measured by the radar for the k-th time where k ∈ [1, N]; m θk represents the angle measurement value measured by the radar for the k-th time where k ∈ [1, N], Δr k represents the distance measurement error caused by noise in m rk , and Δθ k represents the angle measurement error caused by noise in m θk ; The second pseudo-linear equation is as follows: Among them, Δx 1 represents the error between the first solution result x obtained by solving the unknowns of the first pseudo-linear equation 1 and the corresponding true value, x 1 (3) and x 1 (4) respectively take the 3rd and 4th elements of x 1 as the unknowns of the second pseudo-linear equation. ​ 7. The system error correction device according to claim 6, characterized in that, The construction method of the first pseudo-linear equation used in the first solution module includes: Construct a pseudo-linear equation related to distance measurement: Construct a pseudo-linear equation related to angular measurement: The pseudo linear equation related to the distance measurement, the pseudo linear equation related to the angle measurement and the linear equation about the radar position information Δu=uu o By combining them, the first pseudo linear equation is obtained.

8. The system error correction device according to claim 6, characterized in that, The first solution module solves the first pseudo-linear equation based on the least squares method and the weighted least squares method to obtain a first solution result, including: Solve the first pseudo-linear equation using the least squares method to obtain the least squares solution Using the least squares solution Take the third element in r as the approximation of b, and use Take the fourth element in θ as the approximation of b to calculate B m and B t ; Substitute the calculated B m and B t back into the first pseudo-linear equation, and use the weighted least squares method to solve the third pseudo-linear equation obtained at this time to obtain the first solution result; wherein, when the first solution module solves the third pseudo-linear equation using the weighted least squares method, the weight coefficient used is: wherein, is the variance at, and diag(·) is a function for constructing a diagonal matrix based on the input parameter; denotes the covariance matrix at, and blkdiag(·) is a function for constructing a block diagonal matrix based on the input parameter.

9. The system error correction device according to claim 8, characterized in that, When the second solution module solves the second pseudo-linear equation using the weighted least squares method, the weight coefficient used is:

10. The system error correction device according to any one of claims 6 to 9, characterized in that, It is applied to a networked radar detection system.

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