SIMO-mode composite guidance radar angle measurement method

Abstract

The invention relates to a composite guidance radar angle measurement method in an SIMO mode, and belongs to the field of radar signal processing, and the measurement method comprises the steps: carrying out the preprocessing of a radar echo signal, and constructing an angle measurement model matched with a current scene; estimating the angle at the current moment by adopting a fourth-order cumulant maximum likelihood-alternating projection algorithm; estimating track information characteristics at the next moment by using an INS algorithm, constructing a space structure model between the friend and the foe, calculating a relative distance between a radar and a target through HF, and then calculating angle information at the current moment; and finishing angle updating by adopting an INS-based angle measurement fusion algorithm. According to the method, the angle measurement performance of the SIMO mode composite guidance radar is improved, the better angle measurement performance is achieved under the low signal-to-noise ratio, and the multi-valued (fuzzy) problem can be effectively solved.

Description

Technical Field

[0001] The present invention relates to the field of radar signal processing, and in particular to an angle measurement method for a compound guidance radar in SIMO mode. Background Art

[0002] The main task of a radar is to extract the direction information of a target from the echo information scattered by the target, mainly including the azimuth angle and the elevation angle, which respectively reflect the horizontal position and height information of the target, and jointly constitute the accurate positioning ability of the radar for the target.

[0003] Common radar angle measurement methods include the phase comparison method, the amplitude comparison method, and spatial spectrum estimation. The core idea of the phase comparison / amplitude comparison method is to use multiple receiving antennas to receive the radiation source signal, and then use the phase difference / amplitude difference between the incident signals to achieve angle measurement. Spatial spectrum estimation is to use the received data of the spatial domain array to replace the traditional time domain processing method, so as to achieve angle measurement of the spatial signal. However, the angle measurement accuracy of the phase comparison / amplitude comparison method is relatively low; the angle measurement accuracy of the spatial spectrum estimation method is greatly affected by the signal-to-noise ratio and the element spacing. There is a large angle measurement error under low signal-to-noise ratio, and when the adjacent element spacing is greater than 0.5 times the wavelength of the incident signal, a multi-valued (ambiguity) problem will also occur. Therefore, how to solve the problems existing in the existing radar angle measurement methods is currently something to be considered. Summary of the Invention

[0004] The purpose of the present invention is to overcome the shortcomings of the prior art, and provides an angle measurement method for a compound guidance radar in SIMO mode, which solves the deficiencies existing in the prior art.

[0005] The purpose of the present invention is achieved through the following technical solutions: An angle measurement method for a compound guidance radar in SIMO mode, the measurement method includes:

[0006] S1. Preprocess the radar echo signal and construct an angle measurement model matching the current scenario;

[0007] S2. Estimate the angle at the current moment by using the fourth-order cumulant maximum likelihood-alternating projection algorithm;

[0008] S3. Use the INS algorithm to estimate the characteristics of the track information at the next moment, construct a spatial structure model between the friendly and enemy sides, calculate the relative distance between the radar and the target through HF, and then calculate the angle information at the current moment;

[0009] S4. Complete angle update by using the INS-based angle measurement fusion algorithm.

[0010] The construction of the angle measurement model matching the current scenario specifically includes the following content:

[0011] A1. Suppose an array of P elements with a spacing of d is used to measure the angles of K mutually independent integrated echo signals, where K < P. The input signal of the p-th element is s rk (·) represents the k-th incident signal source, and τ k represents the time delay of the k-th signal source arriving at the p-th element relative to the reference element, and n p (t) represents the Gaussian white noise on the p-th element;

[0012] A2. The received signal of the p-th element after preprocessing is obtained as And it is written in vector form as X(t) = A(θ)S(t) + N(t), where X(t) represents the P-dimensional vector form of the received data of each element at time t, A(θ) represents the P×K-dimensional array manifold matrix, S(t) is the K-dimensional signal source vector, and N(t) is the P-dimensional noise vector;

[0013] A3. Finally, it is expanded into vector form as α(θ i ) represents the steering vector in the direction of θ i , and the value of i ranges from 0 to K.

[0014] The specific content of S2 is as follows:

[0015] S21. Let the first characteristic function of the random variable x be Then the k-th moment of x is Taking the logarithm of the first characteristic function to obtain the second characteristic function of the random variable x as where, f(x) is the PDF of x, and c k represents the k-th moment of x;

[0016] S22. According to the first characteristic function and the k-th moment of x, the multi-dimensional first characteristic function and r-th moment of the random vector (x1, x2,..., x n ) are respectively Similarly, the multi-dimensional second characteristic function and r-th moment of the random vector (x1, x2,..., x n ) are

[0017] S23. Express the fourth-order cumulant of the zero-mean stationary random sequence as where, is the fourth moment of x, and are the second moments of x;

[0018] Suppose the number of elements is M and the number of incident signal sources is N. Then the value range of the variables in the fourth-order cumulant is 1 ≤ k1, k2, k3, k4 ≤ M. Therefore, its fourth-order cumulant has a total of M4 A numerical value is put into a new covariance matrix R4 of M×M. The three forms represented by R4 are

[0019] S25. If the covariance matrix R4 takes the second form, then where B(θ) represents

[0020] S26. Apply the fourth-order cumulant to the maximum likelihood-alternating projection algorithm, and the maximum likelihood function obtained is And solve the maximum likelihood estimates of the parameters s and σ to obtain σ represents the variance of Gaussian white noise in the received data, M represents the M antennas in SIMO, N represents the number of set steering vectors, x i represents the received vector of the i-th sampling, s i represents the signal vector included in the i-th sampling;

[0021] S27. According to the maximum likelihood function and Calculate the maximum likelihood estimate value of θ as Denoise, P B(θ) is the projection matrix of the noise subspace.

[0022] The specific content of the above S3 includes the following:

[0023] S31. Select the navigation coordinate system as the e-system, then the inertial specific force velocity equation is expressed as where represents the initial attitude matrix, f b represents the specific force acceleration measured by the accelerometer, g n represents the gravitational acceleration, v n represents the velocity of the carrier relative to the earth, represents the Coriolis acceleration caused by the rotation of the carrier and the rotation of the earth;

[0024] S32. When only considering the update time from t k to t k+1 , integrate the inertial specific force velocity equation in time to obtain According to the chain rule of the attitude matrix, there is Substitute it into the integrated formula and multiply both the left and right sides by to obtain And write the left part according to the Coriolis force theorem as

[0025] S33. Substitute into to obtain the velocity integration formula as And continue to solve the position integral formula to obtain the position equation as Wherein, includes three position information of longitude, latitude and altitude, and R c is the earth curvature matrix;

[0026] S34. Similarly, for Integrate over time t k ~t k+1 to obtain

[0027] S35. Calculate the relative distance between the radar and the target through the haversine formula where r e is the radius of the earth, and represent the latitudes of radar A and target B, and λ A and λ B represent the longitudes of radar A and target B.

[0028] S36. Solve the maximum likelihood estimate value of the relative azimuth angle between the radar and the target through the spherical cosine formula as where θ C represents the central angle corresponding to the great circle distance between the radar and the target on the sphere.

[0029] The S4 specifically includes the following contents:

[0030] S41. Let the angle measurement estimate value at a certain moment be and its array manifold estimate matrix be where λ represents the wavelength of the signal carrier frequency;

[0031] S42. Let the angle measurement received signal at this moment be X(t), and its fitting residual be expressed as ε = ||X(t) - A(θ)S(t)||, where S(t) represents the signal source vector;

[0032] S43. The confidence level of the angle measurement algorithm estimate value is expressed as

[0033] The present invention has the following advantages: A compound guidance radar angle measurement method in SIMO mode improves the angle measurement performance of the SIMO mode compound guidance radar, has better angle measurement performance under low signal-to-noise ratio and can effectively solve the multi-valued (ambiguity) problem. Brief Description of the Drawings

[0034] Figure 1 is the flow schematic diagram of the present invention;

[0035] Figure 2 is the flow schematic diagram of the tracking angle measurement algorithm based on INS;

[0036] Figure 3 It is a schematic diagram of the angle measurement fusion algorithm based on INS. Specific implementation manners

[0037] To make the objectives, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part rather than all of the embodiments of the present application. The components of the embodiments of the present application usually described and illustrated in the accompanying drawings here can be arranged and designed in various different configurations. Therefore, the detailed description of the embodiments of the present application provided below with reference to the accompanying drawings is not intended to limit the protection scope of the claimed present application, but only represents the selected embodiments of the present application. All other embodiments obtained by those skilled in the art based on the embodiments of the present application without creative efforts fall within the protection scope of the present application. The present invention will be further described below with reference to the accompanying drawings.

[0038] The present invention specifically relates to a method for measuring the angle of a compound guidance radar in a SIMO mode. First, the radar echo is preprocessed to remove interference factors such as noise, and an applicable angle measurement mathematical model is constructed; then, the fourth-order cumulant maximum likelihood-alternating projection algorithm (Maximum Likelihood-Alternating Projection, ML-AP) is used to complete the angle estimation at the current moment, the relative distance between the radar and the target is calculated through the Haversine Formula (HF), and the tracking angle measurement algorithm based on the Inertial Navigation System (INS) is used to complete the angle prediction at the next moment. At the same time, an angle measurement fusion algorithm based on INS is designed, thereby improving the angle measurement performance of the SIMO mode compound guidance radar.

[0039] Further, as Figure 1 shown, it specifically includes the following contents:

[0040] (1) Preprocess the integrated echo signal of detection and jamming. The integrated waveform of detection and jamming has the characteristics of a large time-bandwidth product, and the backscattering characteristics of the target are related to the frequency. Accurately calculating the integrated echo signal requires a large number of convolution or Fourier transform operations. In addition, during the process of the echo signal being processed by the receiver, information such as phase and carrier frequency is easily lost, which will directly affect the real-time performance and angle measurement accuracy of the guidance radar angle measurement algorithm. Therefore, before measuring the angle of the integrated echo, echo preprocessing means are required.

[0041] Taking the LFM signal as an example, the method of removing the frequency modulation can be used for preprocessing. Assume that the expression of the LFM signal is:

[0042]

[0043] Among them, a is the signal amplitude, f0 is the carrier frequency, and μ is the signal slope. is the initial phase.

[0044] Omitting coefficients such as the transceiver antenna gain and RCS, the echo signal can be briefly recorded as:

[0045] s r (t) = s(t) + n(t) (2),

[0046] where n(t) is complex Gaussian white noise.

[0047] Using the Discrete Polynomial Phase Transform (DPT) algorithm to perform delay correlation operations on the LFM signal sequence, the estimated value of the signal slope can be calculated Thereby constructing the de-chirp coefficient signal s j (t), that is:

[0048]

[0049] Multiplying Equation (2) by Equation (3), we can obtain:

[0050]

[0051] When the signal s z (t) can be regarded as a single-frequency narrowband signal with Gaussian white noise.

[0052] The phase-coded signal can be preprocessed by searching for phase transformation points and splitting the echo signal into several narrowband signals according to the coding sequence.

[0053] For the echo signal with complex modulation, a general method of narrowband filtering can be used for preprocessing. Suppose the integrated echo signal is radiated to two element arrays of the guidance radar receiver from different directions, and the received signals are respectively expressed as:

[0054]

[0055] In the formula, τ is the time delay between the two elements, u(t) is the signal amplitude, w is the signal frequency, is the signal phase.

[0056] Mixing s r1 (t) in Equation (5) with the local oscillator, its time-frequency domain expression is:

[0057]

[0058] where τ is the time delay between two array elements, u(t) is the signal amplitude, w lo is the local oscillator frequency, and S R1 (w) is the spectrum of s r1 (t).

[0059] Furthermore, after intermediate-frequency band-pass filtering, the signal obtained is:

[0060] S Mid (w) = S Mix1 (w) * H(w) (7),

[0061] By performing a Hilbert transform on the intermediate-frequency output signal, the expression for its complex analytic signal can be obtained as:

[0062]

[0063] where H(w) is the intermediate-frequency band-pass filter in Equation (7), df represents the differential symbol, and f in df refers to f in w = 2πf, and f a and f b represent the lower positive limit and the upper positive limit cut-off frequencies of the band-pass filter, respectively.

[0064] According to Equation (8), the expression for the complex analytic signal obtained by filtering s r2 (t) in Equation (5) is:

[0065]

[0066] According to Equations (8) and (9), the received signals of the two array elements after filtering satisfy the following relationship:

[0067]

[0068] (2) Construct the angle measurement model for the preprocessed echo signal: Assume that an array of P array elements with a spacing of d is used to measure the angles of K mutually independent integrated echo signals. Among them, K < P, and the input signal of the p-th array element is:

[0069]

[0070] where s rk (·) represents the k-th incident signal source, τ k represents the time delay of the k-th signal source arriving at the p-th array element relative to the reference array element, and n p (t) represents the Gaussian white noise on the p-th array element.

[0071] From Equations (10) and (11), the received signal of the p-th array element after preprocessing is:

[0072]

[0073] Write Equation (12) in vector form, i.e.:

[0074] X(t) = A(θ)S(t) + N(t) (13),

[0075] where X(t) represents the P-dimensional vector form of the received data at each array element at time t, A(θ) represents the P×K-dimensional array manifold matrix, S(t) is the K-dimensional signal source vector, and N(t) is the P-dimensional noise vector, which can be expanded into the following vector form:

[0076]

[0077] where α(θ i ) represents the steering vector in the direction of θ i , and i takes values from 0 to K.

[0078] (3) Use the fourth-order cumulant ML-AP algorithm to estimate the angle of the echo signal at the current moment.

[0079] Assume that the first characteristic function of the random variable x is:

[0080]

[0081] where f(x) is the PDF (probability density function) of x.

[0082] Then the k-th moment of x is:

[0083]

[0084] where w k represents taking the k-th derivative of w.

[0085] The second characteristic function of x is taking the logarithm of Equation (15), which can be expressed as:

[0086]

[0087] where c k represents the k-th moment of x, O represents the higher-order infinitesimal of w n , and k! represents the factorial of k.

[0088] From Equation (15) and Equation (16), the multi-dimensional first characteristic function and the r-th moment of the random vector (x1, x2,..., x n ) can be obtained as:

[0089]

[0090] where \(j\) represents the imaginary symbol, \(j\) r represents the \(r\) -th power of \(j\).

[0091] Similarly, the multi - dimensional second - characteristic function and the \(r\) -th moment of the random vector \((x_1,x_2,\cdots,x\) n ) can be obtained as follows:

[0092]

[0093] If \(x\) is a Gaussian random process, then its higher - order moments are:

[0094]

[0095] It can be seen from Equation (20) that the higher - order cumulants of a Gaussian process are always 0.

[0096] The commonly used higher - order cumulants are the third - order and fourth - order cumulants. In angle measurement, the fourth - order cumulant is mainly used. It can expand the array aperture, suppress colored noise, and thus improve the angle - measurement accuracy.

[0097] The fourth - order cumulant of a zero - mean stationary random sequence can be expressed as:

[0098]

[0099] where is the fourth - order moment of \(x\), and are the second - order moments of \(x\).

[0100] Assume that the number of array elements is \(M\) and the number of incident signal sources is \(N\). Then the value range of the variables in Equation (21) is \(1\leq k_1,k_2,k_3,k_4\leq M\). Therefore, there are a total of \(M\) 4 values for its fourth - order cumulant. Consider putting them into a new covariance matrix \(R_4\) of size \(M\times M\). \(R_4\) can be expressed in the following three forms:

[0101]

[0102] Using different covariance matrices \(R_4\), the final estimation results will be different. If \(R_4\) takes the first value, then we can obtain:

[0103]

[0104] \(B(\theta)=[b(\theta_1),b(\theta_2),\cdots,b(\theta\) N )](24),

[0105] where \(H\) represents the conjugate transpose, and \(C_s\) represents the function related to the signal covariance matrix in \(R_4\).

[0106] If R4 takes the second value in Equation (24), the following can be obtained:

[0107]

[0108] In the formula, the value of B(θ) is different from that in Equation (24), and B(θ) can be expressed as:

[0109]

[0110] Adopting the covariance matrix of Equation (25) can double the extended aperture of the array. Applying the fourth-order cumulant to the ML-AP algorithm, the maximum likelihood function can be expressed as:

[0111]

[0112] Among them, σ represents the variance of Gaussian white noise in the received data, M represents the number of array elements in SIMO, N represents the number of incident signal sources, x i represents the received vector of the i-th sampling, and s i represents the signal vector included in the i-th sampling.

[0113] Further solving the maximum likelihood estimates of the parameters s and σ, that is:

[0114]

[0115] From Equation (27) and Equation (28), the maximum likelihood estimate value of θ can be obtained as:

[0116]

[0117] In the formula, P B(θ) is the projection matrix of the noise subspace, and P B(θ) = B(θ)[B H (θ)B(θ)] -1 B H (θ).

[0118] (4) INS-based tracking and angle measurement algorithm: As Figure 2 shown, first, using the characteristic that the INS algorithm can estimate the track information at the next moment, a spatial structure model between the enemy and ourselves is constructed; then, the relative distance between the HF radar and the target is calculated; finally, considering the influence of the earth curvature on the line of sight, and using the spherical cosine formula to calculate the angle information (azimuth angle and elevation angle) at the current moment.

[0119] The relative information between the radar and the target is determined by the relationship between each coordinate system. Commonly used coordinate systems include the vehicle coordinate system (b system) and the earth coordinate system (e system), etc.

[0120] If the navigation coordinate system selects the e - system, the inertial - specific - force velocity equation can be expressed as:

[0121]

[0122] In the formula, represents the initial attitude matrix, f b represents the specific - force acceleration measured by the accelerometer, g n represents the gravitational acceleration, v n represents the velocity of the vehicle relative to the Earth, represents the Coriolis acceleration caused by the rotation of the vehicle and the rotation of the Earth.

[0123] Assume that only considering the update time from t k to t k+1 , integrating Equation (30) over time gives:

[0124]

[0125] According to the chain rule of the attitude matrix, there is Substitute it into Equation (31) and multiply both sides by That is:

[0126]

[0127] The left - hand side of Equation (32) can be written according to the Coriolis force theorem as:

[0128]

[0129] Substitute Equation (33) into Equation (32) to solve the velocity integration formula, that is:

[0130]

[0131] Continue to solve the position integration formula, the position equation can be expressed as:

[0132]

[0133] In the formula, contains 3 position information: longitude (λ), latitude and altitude (h), R c is the Earth curvature matrix, which can be expressed as:

[0134]

[0135] In the formula, R e and R n represent the Earth's equatorial radius and polar - axis radius respectively.

[0136] Similarly, for Equation (35) at time tk ~t k+1 Integrating over this gives:

[0137]

[0138] Obviously, solving the position integration formula requires the help of the velocity integration formula.

[0139] The HF method is used to calculate the relative distance between the radar and the target: This method is an improved method for the error caused by the cos term in the spherical cosine and is the main method for solving the distance problem in the current global positioning system. Therefore, this paper will use this method to calculate the relative distance between the radar and the target, and its core calculation formula is:

[0140]

[0141] In the formula, r e is the radius of the earth, and represent the latitudes of radar A and target B, and λ A and λ B represent the longitudes of radar A and target B.

[0142] The maximum likelihood estimate of the relative azimuth angle between the radar and the target is solved through the spherical cosine formula, that is:

[0143]

[0144] In the formula, θ C represents the central angle corresponding to the great circle distance between the radar and the target on the sphere.

[0145] (5) As Figure 3 shown, the angle update is completed using the INS-based angle measurement fusion algorithm.

[0146] The angle measurement algorithm essentially belongs to the category of parameter estimation. In parameter estimation, the residual is an effective means of analyzing the reliability of the predicted data. Assume that the angle measurement estimate at a certain moment is Its array manifold estimation matrix can be denoted as:

[0147]

[0148] Among them, λ represents the wavelength of the signal carrier frequency.

[0149] Assume that the angle measurement received signal at this moment is X(t), and its fitting residual can be expressed as:

[0150] ε = ||X(t) - A(θ)S(t)|| (41),

[0151] In the formula, S(t) represents the signal source vector.

[0152] The confidence formula for the estimated value of the angle measurement algorithm can be expressed as:

[0153]

[0154] The above are only the preferred embodiments of the present invention. It should be understood that the present invention is not limited to the forms disclosed herein, should not be regarded as excluding other embodiments, but can be used in various other combinations, modifications, and improvements, and can be changed within the scope of the concept described herein through the above teachings or the techniques or knowledge in related fields. Any changes and variations made by those skilled in the art without departing from the spirit and scope of the present invention shall fall within the protection scope of the appended claims of the present invention.

Claims

1. A method for measuring the angle of a compound guidance radar in SIMO mode, characterized in that: The measurement method includes: S1. Preprocess the radar echo signal and construct an angle measurement model matching the current scene; S2. Use the fourth-order cumulant maximum likelihood-alternating projection algorithm to estimate the angle at the current moment; S3. Use the INS algorithm to estimate the characteristics of the track information at the next moment, construct a spatial structure model between the friendly and enemy sides, calculate the relative distance between the radar and the target through HF, and then calculate the angle information at the current moment; S4. Complete the angle update using the INS-based angle measurement fusion algorithm.

2. The angle measurement method of the compound guidance radar in SIMO mode according to claim 1, wherein: The specific content of constructing the angle measurement model matching the current scene includes the following: A1. Suppose an array of P elements with a spacing of d is used to measure the angles of K mutually independent integrated echo signals, where K < P. The input signal of the p-th element is s rk (·) represents the k-th incident signal source, and τ k represents the time delay of the k-th signal source arriving at the p-th element relative to the reference element, and n p (t) represents the Gaussian white noise on the p-th element; A2. The received signal of the p-th array element after preprocessing is obtained as and it is written in vector form as X(t) = A(θ)S(t) + N(t), where X(t) represents the P-dimensional vector form of the received data of each array element at the t-th moment, A(θ) represents the P×K-dimensional array manifold matrix, S(t) is the K-dimensional signal source vector, N(t) is the P-dimensional noise vector, and w is the signal frequency; A3. Finally, it is expanded into a vector form as α(θ i ) represents the steering vector in the θ i direction, and the value of i ranges from 0 to K.

3. A method for measuring the angle of a compound guidance radar in SIMO mode according to claim 2, characterized in that: The specific content of S2 includes the following: S21. Let the first characteristic function of the random variable \(x\) be Then the \(k\)-th moment of \(x\) is Taking the logarithm of the first characteristic function gives the second characteristic function of the random variable \(x\) as where f(x) is the PDF of x, and PDF represents the probability density function, and c k represents the k-th moment of x, and j represents the imaginary unit; S22. The multi-dimensional first characteristic function and the r-th moment of the random vector (x1, x2, …, x n ) are respectively Similarly, the multi-dimensional second characteristic function and the r-th moment of the random vector (x1, x2, …, x n ) are S23. Express the fourth-order cumulant of a zero-mean stationary random sequence as where is the fourth-order moment of x, and are the second-order moments of x; S24. Let the number of array elements be M and the number of incident signal sources be N. Then, the value range of the variables in the fourth-order cumulant is 1 ≤ k1, k2, k3, k4 ≤ M. Therefore, there are M 4 numerical values in total. These values are placed into a new covariance matrix R4 of M×M. The three forms represented by R4 are S25. If the covariance matrix R4 takes the second form, then we get S26. Apply the fourth-order cumulant to the maximum likelihood-alternating projection algorithm to obtain the maximum likelihood function as and solve for the maximum likelihood estimates of the parameters s and σ to obtain σ represents the variance of Gaussian white noise in the received data, M represents the M antennas in SIMO, N represents the number of set steering vectors, x i represents the received vector of the i-th sampling, s i represents the signal vector contained in the i-th sampling, and H represents the conjugate transpose; S27. According to the maximum likelihood function and The maximum likelihood estimate of θ calculated is Denoising, P B(θ) is the projection matrix of the noise subspace.

4. A method for measuring the angle of a compound guidance radar in SIMO mode according to claim 1, characterized in that: The specific content of S3 includes the following: S31. Select the navigation coordinate system as the e - system, then the inertial - specific - force velocity equation is expressed as where represents the initial attitude matrix, f b represents the specific - force acceleration measured by the accelerometer, g n represents the gravitational acceleration, v n represents the velocity of the vehicle relative to the Earth, represents the Coriolis acceleration caused by the rotation of the vehicle and the rotation of the Earth; S32. When only considering the update time from t k to t k+1 , integrate the inertial navigation specific force velocity equation over time to obtain According to the chain rule of the attitude matrix, we have Substitute it into the integrated formula and multiply both sides by to obtain And write the left part according to the Coriolis force theorem as S33. Substitute into to obtain the velocity integration formula as and continue to solve the position integration formula to obtain the position equation as wherein, includes three position information of longitude, latitude and altitude, and R c is the earth curvature matrix; S34. Similarly, for at time t k ~t k+1 perform integration to obtain S35. By the haversine formula Calculate the relative distance between the radar and the target, where r e is the radius of the earth, and represent the latitudes of radar A and target B, λ A and λ B represent the longitudes of radar A and target B. S36. The maximum likelihood estimate value of the relative azimuth angle between the radar and the target is solved by the spherical cosine formula as where θ C represents the central angle corresponding to the great circle distance between the radar and the target on the sphere.

5. A method for measuring the angle of a compound guidance radar in SIMO mode according to claim 1, characterized in that: The specific content of S4 includes the following: S41. Let the angle measurement estimate at a certain moment be Its array manifold estimation matrix is where λ represents the wavelength of the signal carrier frequency; S42. Let the angle measurement received signal at this moment be X(t), and its fitting residual be expressed as ε = ||X(t) - A(θ)S(t)||, where S(t) represents the signal source vector; The confidence level of the estimated value of the angle measurement algorithm is expressed as

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