Cognitive radar clutter suppression method based on nonlinear time-varying weighting of dual basis projection angles

Abstract

The application provides a clutter suppression method for cognitive radar based on double-base projection angle nonlinear time-varying weighting, calculates a double-base distance sum of an lth double-base distance unit to be detected of the echo signal; calculates a distance between an intersection of a double-base distance ring in a receiving main beam azimuth direction and a transceiver radar according to the double-base distance sum; calculates a projection angle of a double-base angle on the ground and an updating variable according to the system prior information and the distance between the intersection of the double-base distance ring in the receiving main beam azimuth direction and the transceiver radar; judges whether the projection angle of the double-base angle on the ground is a non-obtuse angle, if yes, calculates an adaptive weight vector of nonlinear time-varying weighting by using the projection angle, the updating variable and echo signal data; and performs space-time adaptive processing on the echo signal according to the adaptive weight vector to obtain a space-time adaptive processing result. The application can improve the clutter suppression performance of the double-base airborne radar.

Description

Technical Field

[0001] This invention belongs to the field of radar signal processing technology, specifically relating to a cognitive radar clutter suppression method based on nonlinear time-varying weighting of bistatic projection angles. Background Technology

[0002] For airborne radar, radar echoes inevitably contain a lot of strong ground clutter, which severely affects the detection of moving ground targets and must be suppressed. Space-time adaptive processing (STAP) can distinguish between targets and clutter in two dimensions (space and time), effectively suppressing clutter. However, to achieve good clutter suppression performance, traditional STAP requires at least twice the system's degrees of freedom of independent, identically distributed training samples to estimate the target unit. But bistatic airborne radar clutter exhibits range non-stationarity, making it difficult to find a sufficient number of independent, identically distributed training samples, resulting in a significant decrease in the clutter suppression performance of STAP.

[0003] Current research on bistatic clutter suppression mainly focuses on the following aspects:

[0004] 1. Researching dimensionality reduction and space-time adaptive processing methods to reduce the system's sample requirements. The most direct benefit of dimensionality reduction and space-time adaptive methods is reducing the dimensionality and computational cost of system processing. When distance non-stationarity exists in clutter, it can reduce the number of training samples required by the system. Currently, the main dimensionality reduction and space-time adaptive processing methods include: Extended Factor Method (EFA), Auxiliary Channel Method (ACR), pre-Doppler method, and post-Doppler method.

[0005] 2. Research on methods for clutter range nonstationarity compensation and correction; for bistatic clutter range nonstationarity compensation methods, there are Doppler warping (DW), angle-Doppler compensation (ADC), spectral registration (RBC), and derivative update (DBU) methods. The derivative update method, proposed by S.D. Hayward, treats the weight vector as a function that linearly changes with bistatic range. However, the change in the weight vector does not conform to the trend of bistatic airborne radar clutter changing strongly at close range and gradually at long range, resulting in poor clutter suppression capability at close range. Summary of the Invention

[0006] To address the aforementioned problems in the existing technology, this invention provides a cognitive radar clutter suppression method based on nonlinear time-varying weighted weighting of dual-base projection angles. The technical problem to be solved by this invention is achieved through the following technical solution:

[0007] This invention provides a cognitive radar clutter suppression method based on nonlinear time-varying weighted bistatic projection angle, comprising:

[0008] S100, acquires echo signals and prior information of the bistatic airborne radar system;

[0009] S200, based on the system prior information and the echo signal, calculate the sum of bistatic distances of the l-th bistatic distance unit to be detected in the echo signal;

[0010] S300, based on the bibase distance sum, calculate the distance between the intersection point of the bibase distance loop in the azimuth direction of the receiving main beam and the transceiver radar, wherein the transceiver radar includes a receiving radar and a transmitting radar;

[0011] S400, based on the system prior information and the distance between the intersection of the receiving main beam azimuth direction in the bistatic range loop and the transceiver radar, calculate the projection angle of the bistatic angle on the ground and update the variables;

[0012] S500, determine whether the projection angle of the bibasic angle on the ground is a non-obtuse angle. If it is, use the projection angle, update variables and echo signal data to calculate the nonlinear time-varying weighted adaptive weight vector.

[0013] S600, perform space-time adaptive processing on the echo signal according to the adaptive weight vector to obtain the space-time adaptive processing result.

[0014] Beneficial effects:

[0015] First, the method of this invention utilizes the variation law of the projection angle correlation function of the bistatic azimuth angle of the main beam received by a bistatic airborne radar on the ground with the bistatic distance, so that the weight vector becomes the aforementioned projection angle correlation function. This makes the weight vector change more drastically at close range and change more gradually at long range, which better conforms to the variation law of clutter characteristics of bistatic airborne radar at close and long ranges, thereby enabling the method of this invention to suppress clutter more effectively.

[0016] Second, this invention retains the first three terms of the Taylor expansion of the weight vector, making the weight vector estimation more accurate and further improving the clutter suppression performance of bistatic airborne radar.

[0017] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0018] Figure 1 The flowchart shows the nonlinear time-varying weighted clutter suppression method based on bibasic angle projection function of the present invention.

[0019] Figure 2(a) shows the improvement factor curve when the base distance is 168 km;

[0020] Figure 2(b) shows the improvement factor curve when the base distance is 240 km;

[0021] Figure 2(c) shows the improvement factor curve when the base distance is 300 km;

[0022] Figure 3(a) Clutter power spectrum of the direct processing method when the bistatic distance is 168 km;

[0023] Figure 3(b) Clutter power spectrum of the DBU method at a bistatic distance of 168 km;

[0024] Figure 3(c) shows the clutter power spectrum of the method of the present invention when the bibase distance is 168 km. Detailed Implementation

[0025] The present invention will be further described in detail below with reference to specific embodiments, but the implementation of the present invention is not limited thereto.

[0026] Please see Figure 1 , Figure 1 This is a flowchart of a cognitive radar clutter suppression method based on nonlinear time-varying weighted bistatic projection angle provided by an embodiment of the present invention. The cognitive radar clutter suppression method based on nonlinear time-varying weighted bistatic projection angle of the present invention includes:

[0027] S100, acquires echo signals and prior information of the bistatic airborne radar system;

[0028] S200, based on the system prior information and the echo signal, calculate the sum of bistatic distances of the l-th bistatic distance unit to be detected in the echo signal;

[0029] S300, based on the bibase distance sum, calculate the distance between the intersection point of the bibase distance loop in the azimuth direction of the receiving main beam and the transceiver radar, wherein the transceiver radar includes a receiving radar and a transmitting radar;

[0030] S400, based on the system prior information and the distance between the intersection of the receiving main beam azimuth direction in the bistatic range loop and the transceiver radar, calculate the projection angle of the bistatic angle on the ground and update the variables;

[0031] S500, determine whether the projection angle of the bibasic angle on the ground is a non-obtuse angle. If it is, use the projection angle, update variables and echo signal data to calculate the nonlinear time-varying weighted adaptive weight vector.

[0032] S600, perform space-time adaptive processing on the echo signal according to the adaptive weight vector to obtain the space-time adaptive processing result.

[0033] In one optional embodiment of this application, S300 includes:

[0034] S310, calculate the first distance between the target intersection point and the receiving radar, wherein the target intersection point is the intersection point of the receiving main beam azimuth direction and the l-th bistatic range loop to be detected;

[0035] The first distance between the target intersection point and the receiving radar is expressed as:

[0036]

[0037] Among them, R sum H is the sum of bibase distances of the l-th bibase distance unit to be detected. r To receive radar altitude, H t The altitude of the transmitting radar is L, the baseline length is θ. r The angle between the azimuth of the receiving main beam and the baseline of the transmitting and receiving radar;

[0038] S320, the first distance is processed to obtain the processed first distance; the processed first distance is expressed as:

[0039]

[0040] Where A = L 2 +2H r H t -2H r 2 B = L 2 -(H r -H t ) 2 ;

[0041] S330, using the bibase distance of the l-th bibase distance unit to be detected and R sum Based on the first distance after adjustment, calculate the second distance between the target intersection point and the transmitting radar. The second distance is expressed as:

[0042] R t (l)=R sum (l)-R r (l) (3).

[0043] In one optional embodiment of this application, S400 includes:

[0044] S410, calculate the projection angle of the bistatic angle of the l-th bistatic range cell to be detected in the azimuth direction of the receiving main beam; the projection angle is expressed as:

[0045]

[0046] Where d is the distance between the projection points of the transmitting and receiving radars on the ground, d t (l) is the projected length of the target intersection point and the slant range of the transmitting radar on the ground, dr (l) is the projected length of the target intersection point and the receiving radar slant range on the ground;

[0047] S420, using the projection angle from S410, calculates the update variable for the l-th bistatic distance cell to be detected. The update variable is expressed as:

[0048] W(l)=1-cosC(l) (5);

[0049] in,

[0050] In one optional embodiment of this application, S500 includes:

[0051] Determine whether the projection angle of the bibase angle on the ground is a non-obtuse angle. If so, based on the projection angle, update variables and echo signal data, use the NTW method to solve for the adaptive weight vector to obtain the nonlinear time-varying weighted adaptive weight vector.

[0052] Only when the projection angle C of the bistatic angle of the main beam azimuth direction received by the bistatic range unit is not greater than 90°, as the bistatic distance increases, the projection angle gradually decreases, and the update variable W = 1 - cosC will change from drastic change to gradual change. This is in line with the purpose of this invention to make the weight vector fit the characteristics of bistatic airborne radar clutter, which changes drastically at close range and gradually at long range.

[0053] In one optional embodiment of this application, the nonlinear time-varying weighted adaptive weight vector is obtained by solving the adaptive weight vector using the NTW method, including:

[0054] S510, the Taylor expansion is obtained by performing a Taylor expansion on the update variable W(l) corresponding to the l-th bibasic distance unit to be detected and the corresponding space-time adaptive weight vector;

[0055] Let the update variable of the reference bibase distance cell be W(m), and the corresponding space-time adaptive processing weight vector be w. NTW (m), then the update variable corresponding to the l-th bibasic distance unit is W(l), and the space-time adaptive weight vector w NTW The Taylor expansion of (l) is expressed as:

[0056]

[0057] Where m is the index of the reference bibase distance cell, W(m) is the update variable of the reference bibase distance cell, and w NTW (m) is the space-time adaptive processing weight vector corresponding to W(m), w NTW (l) is the space-time adaptive weight vector of the l-th bibase distance cell to be detected. Indicates wNTW The first derivative of (m) Indicates w NTW The second derivative of (m);

[0058] S520, ignoring the influence of cubic terms and higher-order terms in the Taylor expansion of the space-time adaptive weight vector, yields a simplified space-time adaptive weight vector;

[0059] If we consider (W(l)-W(m)) to be very small and ignore the influence of the cubic terms and higher-order terms in equation (6), the simplified space-time adaptive weight vector can be expressed as:

[0060]

[0061] In the formula, the correlation function of the projection angle of the bistatic angle at the intersection of the receiving main beam azimuth and the bistatic range loop on the ground is used instead of the bistatic range as the update variable. When the projection angle is less than or equal to 90°, the update variable changes from drastic to gradual as the bistatic range increases, which is more consistent with the trend of bistatic airborne radar clutter changing drastically at close range and gradually at long range. The space-time adaptive weight vector calculated by the above formula is different for different bistatic range cells. However, in practical applications, a segment or the entire range of data from the radar echo is generally used to estimate the clutter covariance matrix by maximum likelihood. The estimated clutter covariance matrix and the space-time steering vector are then used to suppress clutter. The space-time adaptive weight vector for the processed bistatic range cell should be consistent.

[0062] S530, a space-time adaptive processing formula is constructed based on the echo signal data and the simplified space-time adaptive vector, and the space-time adaptive processing formula is reconstructed to obtain a reconstructed space-time adaptive processing formula; the reconstructed space-time adaptive processing formula includes an adaptive weight vector that does not change with the bistatic distance. and the extended data matrix composed of echo signal data

[0063] The space-time adaptive processing formula is expressed as:

[0064]

[0065] The reconstruction process of the space-time adaptive processing formula in formula (8) above can be expressed as:

[0066]

[0067] in, That is, an adaptive weight vector that does not change with the bibase distance. The covariance matrix is ​​an extended matrix composed of echo signal data. Maximum likelihood estimation can be used to estimate this matrix. The adaptive weight vector, after reconstruction of the output, no longer changes with the bibasic distance cells; the relationship between the weight vector and distance is transferred to the cell data, reflecting compensation for the non-stationarity of the echo data.

[0068] S540 compensates the echo signal data of Q bistatic range cells received by the receiving radar to obtain the compensation result and estimates the covariance matrix of the compensation result.

[0069] At this point, the weight vector does not change with the bistatic distance. The maximum likelihood estimation method can be used to estimate the covariance matrix of the echo signal data, and then the adaptive weight vector can be calculated. Different choices of the reference bistatic distance cell result in different clutter suppression effects; generally, the middle bistatic distance cell is chosen as the reference bistatic distance cell. The covariance matrix is ​​expressed as:

[0070]

[0071] The superscript H indicates the conjugate transpose operation. The dimension of is 3NK×1, k≠l. 1 NK×1 ξ represents an NK×1 dimensional column vector with all elements equal to 1. 1NK×1 =ξ11 NK×1 ξ 2NK×1 =ξ21 NK×1 ξ1 and ξ2 represent normalization coefficients, used to avoid ill-conditioned problems when the condition number of the covariance matrix is ​​too large, thus expanding the echo signal data. The covariance matrix when only white noise exists is the identity matrix;

[0072] S550, Based on the covariance matrix, the optimal weight vector w is obtained using the linear constraint minimum variance (LCMV) criterion;

[0073] This step can use the Linearly Constrained Minimum Variance (LCMV) criterion to determine the optimal weight vector w for the cognitive radar clutter suppression method based on nonlinear time-varying weighted bibasic projection angles. The optimal weight vector w is expressed as:

[0074]

[0075] in, s is an extended spacetime steering vector with dimensions 3NK×1. 0 NK×1 It is a vector matrix of dimension NK×1. S represents the Kronek product, s TThe spatial guide vector has dimensions N×1, s S This is a time-domain guided vector with dimensions K×1. Its expression is as follows:

[0076]

[0077] s T =[1exp(j2π·f d / PRF)...exp(j2π·(K-1)f d / PRF)] T

[0078] Where, N L N represents the number of elements in the azimuth matrix. C f is the number of elements in the pitch array. sL f is the spatial frequency of the radar array azimuth direction of the target. sC f is the spatial frequency of the radar array's elevation direction for the target. d The Doppler frequency of the target is expressed as follows:

[0079]

[0080]

[0081]

[0082] Where λ is the wavelength, d L d represents the azimuth spacing of the array elements. C For the elevation interval of the array elements, ψ L Let ψ be the spatial cone angle between the target and the azimuth of the array. C v is the spatial cone angle between the target and the elevation of the array. T For the speed of the radar platform, ψ VT v is the velocity cone angle between the target and the launching radar. R To receive the speed of the radar platform, ψ VR The velocity cone angle between the target and the receiving radar is denoted by , and PRF is the pulse repetition frequency.

[0083] S560, replace the optimal weight vector w in the reconstructed space-time adaptive processing formula Extended data of the echo signal data X(l) of the l-th bistatic distance cell to be detected Space-time adaptive processing is performed to obtain the space-time adaptive processing result of the echo signal data.

[0084] The result of the space-time adaptive processing of the echo signal data is expressed as follows:

[0085]

[0086] Wherein, the superscript H indicates the conjugate transpose operation, X(l) is the echo signal data of the l-th bibase distance unit to be detected, with a dimension of NK×1, N is the number of array elements, and K is the number of pulses within one CPI.

[0087] Therefore, the result of all echo signal data space-time adaptive processing is expressed as follows:

[0088] The present invention can be configured with a transmitter height of 10,000m and a speed of 150m / s, a receiver height of 10,000m and a speed of 100m / s, a uniform linear array with 10 array elements, 16 pulses, a pulse repetition frequency of 3000Hz, a system bandwidth of 2MHz, a spurious-to-noise ratio of 50dB, a baseline length of 50km, a wavelength of 0.24m, an element spacing of half the wavelength, and a dual-base configuration of parallel flight.

[0089] Please refer to Figures 2 and 3. Figure 2(a) shows the improvement factor curves for the sum of bistatic distances of the proposed method, the DBU method, the ideal optimal method, and the direct processing method (i.e., the SMI method) after optimal STAP, when the sum of bistatic distances is 168 km. Figure 2(b) shows the improvement factor curves for the sum of bistatic distances of the above methods when the sum of bistatic distances is 240 km. Figure 2(c) shows the improvement factor curves for the sum of bistatic distances of the above methods when the sum of bistatic distances is 300 km. The horizontal axis of Figures 2(a), 2(b), and 2(c) represents the normalized Doppler frequency, and the vertical axis represents the improvement factor (dB). Figure 3(a) shows the clutter power spectrum of the proposed method when the sum of bistatic distances is 168 km. Figure 3(b) shows the clutter power spectrum of the DBU method when the sum of bistatic distances is 168 km. Figure 3(c) shows the clutter power spectrum of the direct processing method when the sum of bistatic distances is 168 km. The horizontal axis of Figures 3(a), 3(b), and 3(c) represents the normalized spatial frequency, and the vertical axis represents the normalized Doppler frequency.

[0090] from Figures 2(a) to 2(c) The comparison shows that the clutter suppression performance is best under ideal conditions, followed by the method of this invention and the DBU method, while the direct processing method performs the worst. The clutter suppression performance of the DBU method improves with increasing bistatic distance, but its clutter suppression performance is poor at close range. However, regardless of the bistatic distance, the clutter suppression performance of the method of this invention is superior to that of the DBU method, especially at close range, where the clutter suppression performance of the method of this invention is significantly better than that of the DBU method. Figures 3(a) to 3(c) In comparison, the clutter power spectrum of the method of this invention has a much smaller diffusion degree compared with the DBU method and the clutter power spectrum after direct processing, which compensates for the distance non-stationarity of clutter to a greater extent and has better clutter suppression performance. This proves that the method has better clutter suppression performance than the DBU method at close range.

[0091] The above simulation experiments verified the good suppression performance of the present invention against bistatic airborne radar clutter, ensuring the correctness, effectiveness and reliability of the present invention.

[0092] In summary, the cognitive radar clutter suppression method based on bistatic projection angle nonlinear time-varying weighting disclosed in this invention solves the problem of significantly reduced clutter suppression performance caused by the range non-stationarity of bistatic airborne radar clutter in traditional space-time adaptive processing technology (STAP). The implementation steps are as follows: acquiring bistatic airborne radar echo signals and system prior information; calculating the bistatic range sum of the l-th bistatic range cell to be detected in the radar echo signal based on the system prior information and the echo signal; calculating the bistatic range sum of the l-th bistatic range cell to be detected based on the system prior information and the sum of the transmit and receive distances of the l-th bistatic range cell to be detected. The distance between the bistatic range loop intersection point in the azimuth direction of the receiving main beam and the transceiver radar is calculated. Based on the system prior information and the distance between the bistatic range loop intersection point and the transceiver radar, the projection angle of the bistatic angle on the ground and the update variable are calculated. Based on the size of the projection angle, it is determined whether the projection angles of the segment or the entire data unit requiring clutter suppression are all non-obtuse angles. Based on the projection angle of the bistatic angle in the azimuth direction of the receiving main beam on the ground and the echo signal data, a nonlinear time-varying weighted adaptive weight vector is calculated. The radar echo signal is compensated based on the adaptive weight vector. This invention treats the weight vector as a function of the projection of the bistatic angle in the azimuth direction of the receiving radar main beam on the ground. The adaptive weight vector is Taylor expanded at the reference cell, and the first three terms are used to improve the derivative update method. This significantly improves the clutter suppression performance when the projection angle is acute or right-angled, and is suitable for clutter suppression of bistatic airborne radar.

[0093] Furthermore, 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 number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0094] Although this application has been described herein in conjunction with various embodiments, those skilled in the art will understand and implement other variations of the disclosed embodiments by reviewing the accompanying drawings, the disclosure, and the appended claims in carrying out the claimed application. In the claims, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude a plurality.

[0095] The above description, in conjunction with specific preferred embodiments, provides a further detailed explanation of the present invention. It should not be construed that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art, various simple deductions or substitutions can be made without departing from the concept of the present invention, and all such modifications and substitutions should be considered within the scope of protection of the present invention.

Claims

1. A cognitive radar clutter suppression method based on nonlinear time-varying weighted bistatic projection angle, characterized in that, include: S100, acquires echo signals and prior information of the bistatic airborne radar system; S200, based on the system prior information and the echo signal, calculate the first... The sum of bistatic distances of the bistatic distance units to be detected; S300, based on the bibase distance sum, calculate the distance between the intersection point of the bibase distance loop in the azimuth direction of the receiving main beam and the transceiver radar, wherein the transceiver radar includes a receiving radar and a transmitting radar; S400, based on the system prior information and the distance between the intersection of the receiving main beam azimuth direction in the bistatic range loop and the transceiver radar, calculate the projection angle of the bistatic angle on the ground and update the variables; S500, determine whether the projection angle of the bibasic angle on the ground is a non-obtuse angle. If it is, use the projection angle, update variables and echo signal data to calculate the nonlinear time-varying weighted adaptive weight vector. S600, perform space-time adaptive processing on the echo signal according to the adaptive weight vector to obtain the space-time adaptive processing result.

2. The cognitive radar clutter suppression method based on nonlinear time-varying weighting of bi-base projection angles according to claim 1, characterized in that, The S300 includes: S310, calculate the first distance between the target intersection point and the receiving radar, wherein the target intersection point is the distance between the receiving main beam azimuth and the second... The intersection of the two-base distance loops to be tested; S320, the first distance is processed to obtain the processed first distance; S330, utilizing the first The bistatic distance of each bistatic distance unit to be detected and Based on the first distance after adjustment, calculate the second distance between the target intersection point and the transmitting radar.

3. The cognitive radar clutter suppression method based on nonlinear time-varying weighting of bistatic projection angles according to claim 2, characterized in that, The first distance between the target intersection point and the receiving radar, as described in S310, is expressed as: （1）； in, For the first The sum of bistatic distances of the bistatic distance units to be detected. In order to receive radar altitude, For the radar launch altitude, For the long baseline, The angle between the azimuth of the receiving main beam and the baseline of the transmitting and receiving radar; The first distance after rearrangement in S320 is represented as: （2）； in, , ; The second distance in S330 is represented as: （3）。 4. The cognitive radar clutter suppression method based on nonlinear time-varying weighting of bistatic projection angles according to claim 3, characterized in that, The S400 includes: S410, calculate the... The projection angle of the bistatic distance element to be detected in the azimuth direction of the receiving main beam; S420, calculate the first projection angle using the projection angle from S410. Update variables for each bistatic distance cell to be detected.

5. The cognitive radar clutter suppression method based on nonlinear time-varying weighting of bistatic projection angles according to claim 4, characterized in that, The projection angle in S410 is expressed as: （4）； in, The distance between the radar's projection point on the ground. The projection length of the target intersection point and the slant range of the transmitting radar onto the ground. The projected length of the target intersection point and the slant range of the receiving radar on the ground; , , ; The update variable in S420 is represented as follows: （5）； in, .

6. The cognitive radar clutter suppression method based on nonlinear time-varying weighting of bistatic projection angles according to claim 5, characterized in that, The S500 includes: Determine whether the projection angle of the bibase angle on the ground is a non-obtuse angle. If so, based on the projection angle, update variables and echo signal data, use the NTW method to solve for the adaptive weight vector to obtain the nonlinear time-varying weighted adaptive weight vector.

7. The cognitive radar clutter suppression method based on nonlinear time-varying weighting of bistatic projection angles according to claim 6, characterized in that, The adaptive weight vector obtained by solving the adaptive weight vector using the NTW method includes: S510, will the first Update variables corresponding to each bistatic distance unit to be detected The Taylor expansion of the corresponding space-time adaptive weight vector is obtained by performing a Taylor expansion. S520, ignoring the influence of cubic terms and higher-order terms in the Taylor expansion of the space-time adaptive weight vector, yields a simplified space-time adaptive weight vector; S530, a space-time adaptive processing formula is constructed based on the echo signal data and the simplified space-time adaptive vector, and the space-time adaptive processing formula is reconstructed to obtain a reconstructed space-time adaptive processing formula; the reconstructed space-time adaptive processing formula includes an adaptive weight vector that does not change with the bistatic distance. and the extended data matrix composed of echo signal data ; S540 compensates the echo signal data of Q bistatic range cells received by the receiving radar to obtain the compensation result and estimates the covariance matrix of the compensation result. S550, Based on the covariance matrix, the optimal weight vector is obtained using the linearly constrained minimum variance (LCMV) criterion. ; S560, the optimal weight vector Replace the reconstructed space-time adaptive processing formula in , in order to the first Echo signal data of each bistatic distance cell to be tested Extended data Space-time adaptive processing is performed to obtain the space-time adaptive processing result of the echo data.

8. The cognitive radar clutter suppression method based on nonlinear time-varying weighting of bistatic projection angles according to claim 7, characterized in that, The Taylor expansion in S510 is expressed as: （6）； in, For reference, the serial number of the bibasic distance element, For the update variable of the reference two-base distance cell, To and The corresponding space-time adaptive processing weight vector, For the first The space-time adaptive weight vector of each bibasic distance cell to be detected. express The first derivative, express The second derivative; The simplified space-time adaptive weight vector in S520 is represented as follows: （7）； The space-time adaptive processing formula in S530 is expressed as follows: （8）； The space-time adaptive processing formula reconstruction process in S530 can be expressed as: （9）； in, That is, an adaptive weight vector that does not change with the bibase distance. It is an extended data matrix composed of echo signal data; The covariance matrix mentioned in S540 is: （10）； The superscript H indicates the conjugate transpose operation. The dimension of is , , , express A column vector of dimension whose elements are all 1. , ; and This represents the normalization coefficients, used to avoid ill-conditioned problems when the condition number of the covariance matrix is ​​too large, thus expanding the echo signal data. The covariance matrix when only white noise exists is the identity matrix; ; ; Optimal weight vector in S550 Represented as: （11）； in, , For dimension Extended spacetime steering vector: S560, the result of the space-time adaptive processing is expressed as follows: （12）； The superscript H indicates the conjugate transpose operation. For the first Echo signal data of one bistatic distance cell to be detected, with dimension [dimension missing]. N is the number of array elements, and K is the number of pulses within one CPI.

9. The cognitive radar clutter suppression method based on nonlinear time-varying weighting of bistatic projection angles according to claim 8, characterized in that, The results of all echo signal data space-time adaptive processing are represented as follows: , .

Images