Signal subspace matrix rotation invariance-based azimuth multichannel SAR channel phase error estimation method and device, electronic equipment and storage medium

Abstract

The invention discloses an azimuth multichannel SAR channel phase error estimation method and device based on signal subspace matrix rotation invariance, electronic equipment and a storage medium. The method comprises the following steps: converting echo data of each channel into a two-dimensional frequency domain, and compensating a part of a steering vector changing along with Doppler frequency for the echo data of each channel to obtain compensated echo data; dividing the compensation echo data into first sub-array echo data and second sub-array echo data, and respectively obtaining a first signal subspace matrix and a second signal subspace matrix; according to the projection matrix of the first signal subspace matrix and the rotation invariance of the first signal subspace matrix and the second signal subspace matrix, determining a cost function including a channel phase error; and solving the cost function to obtain a phase error estimation value. According to the method, the algorithm operand is reduced, and more stable channel phase error estimation can be realized.

Description

Technical Field

[0001] The present invention relates to the field of radar technology, and in particular, to a method, device, electronic device, and storage medium for estimating azimuth multi-channel SAR channel phase error based on the rotational invariance of the signal subspace matrix. Background Technique

[0002] Synthetic Aperture Radar (SAR) has attracted increasing attention due to its significant advantages such as all-weather, all-day, and high-resolution capabilities, and has become one of the key technical means in the field of earth observation. Limited by the "minimum antenna area" of the SAR system, it is difficult for a single-channel SAR system to simultaneously meet the requirements of high resolution and wide swath. To solve this problem, researchers have adopted the strategy of arranging multiple receiving channels along the flight direction on the same platform, and each receiving channel can receive the echo signals within the wide swath. By performing digital beamforming on the echo data received by multiple channels, unambiguous azimuth Doppler information and range information can be obtained. In digital beamforming technology, it is required that the amplitudes and phases between channels must be highly consistent. However, in practical applications, it is difficult to avoid introducing phase errors between channels. If these errors are not accurately corrected, the digital beamforming algorithm will fail, resulting in obvious false targets in the azimuth direction of the SAR image.

[0003] A large number of studies have been carried out on channel phase error estimation methods at home and abroad. Among the currently developed channel phase error estimation algorithms, the signal subspace comparison method has high estimation accuracy, but requires multiple covariance matrix estimations and eigenvalue decompositions, resulting in a large amount of computation. The space-time cross-correlation method has a small amount of computation, but requires accurate estimation of the Doppler center frequency and has high requirements for the uniformity of the illuminated scene. Summary of the Invention

[0004] To solve the above problems existing in the prior art, the present invention provides a method, device, electronic device, and storage medium for estimating azimuth multi-channel SAR channel phase error based on the rotational invariance of the signal subspace matrix. The technical problems to be solved by the present invention are realized through the following technical solutions:

[0005] The first aspect of the embodiments of the present invention provides a method for estimating azimuth multi-channel SAR channel phase error based on the rotational invariance of the signal subspace matrix, including the following steps:

[0006] Receiving the echo data of multiple channels, converting the echo data of each channel into the two-dimensional frequency domain, and compensating the part of the steering vector that changes with the Doppler frequency for the echo data of each channel to obtain compensated echo data;

[0007] Divide the compensated echo data into first sub-array echo data and second sub-array echo data, calculate the covariance matrix after combining the first sub-array echo data and the second sub-array echo data, perform eigenvalue decomposition on the covariance matrix, and respectively obtain a first signal subspace matrix and a second signal subspace matrix for receiving the first sub-array echo data and the second sub-array echo data;

[0008] According to the projection matrix of the first signal subspace matrix and the rotational invariance of the first signal subspace matrix and the second signal subspace matrix, determine a cost function including channel phase error;

[0009] Solve the cost function to obtain a phase error estimation value.

[0010] In an embodiment of the present invention, the receiving echo data of multiple channels, converting the echo data of each channel into a two-dimensional frequency domain, and compensating the part of the steering vector that changes with the Doppler frequency for the echo data of each channel to obtain compensated echo data includes:;

[0011] Receive echo data of multiple channels, convert the echo data of each channel into a two-dimensional frequency domain to obtain echo data in the two-dimensional frequency domain;

[0012] Convert the echo data in the two-dimensional frequency domain of each channel into a signal matrix;

[0013] Decompose the steering vector that changes with the Doppler frequency in the signal matrix into a part that changes with the Doppler frequency and a vector matrix that does not change with the Doppler frequency;

[0014] Compensate the part that changes with the Doppler frequency and noise for the echo data of each channel of the signal matrix to obtain compensated echo data.

[0015] In an embodiment of the present invention, the dividing the compensated echo data into first sub-array echo data and second sub-array echo data, calculating the covariance matrix after combining the first sub-array echo data and the second sub-array echo data, performing eigenvalue decomposition on the covariance matrix, and respectively obtaining a first signal subspace matrix and a second signal subspace matrix for receiving the first sub-array echo data and the second sub-array echo data includes:

[0016] Take the first M - 1 receiving antennas and the last M - 1 receiving antennas among the M receiving antennas distributed along the radar flight direction as the first sub-array and the second sub-array respectively. The compensated echo data corresponding to the first sub-array is the first sub-array echo data, and the compensated echo data corresponding to the second sub-array is the second sub-array echo data;

[0017] Combine the echo data of the first sub-array and the echo data of the second sub-array to obtain combined echo data;

[0018] Calculate the covariance matrix of the combined echo data;

[0019] Perform eigenvalue decomposition on the covariance matrix to determine a first signal subspace matrix for receiving the echo data of the first sub-array and a second signal subspace matrix for receiving the echo data of the second sub-array.

[0020] In an embodiment of the present invention, the determining a cost function including channel phase error according to the projection matrix of the first signal subspace matrix and the rotational invariance of the first signal subspace matrix and the second signal subspace matrix includes:

[0021] Calculate the projection matrix of the first signal subspace matrix;

[0022] Construct a first objective function according to the rotational invariance of the first signal subspace matrix and the second signal subspace matrix;

[0023] Solve the auxiliary parameters of the first objective function by using the least squares method;

[0024] Determine a second objective function according to the least squares solution of the auxiliary parameters and the first objective function;

[0025] Transform the second objective function into a cost function including channel phase error according to the sum of the squares of the matrix F norm and the matrix trace.

[0026] In an embodiment of the present invention, the method further includes:

[0027] Reconstruct the Doppler spectrum of the echo data after being corrected by the phase error estimation value, and use the CS imaging algorithm to obtain an unambiguous high-resolution wide-swath SAR image.

[0028] In an embodiment of the present invention, the rotational invariance of the first signal subspace matrix and the second signal subspace matrix satisfies: ΔΓ H U s2 =U s1 Ψ;

[0029] Wherein, U s1 represents the first signal subspace matrix, U s2 represents the second signal subspace matrix, Ψ = T -1 DT, D represents the rotation matrix of the steering vectors of the first sub-array and the second sub-array, ΔΓ represents the phase error between adjacent channels, and T represents a non-singular matrix;

[0030] The first objective function is:

[0031] The least squares solution of Ψ is:

[0032] The second objective function is:

[0033]

[0034] In an embodiment of the present invention, the cost function is:

[0035]

[0036] Wherein,

[0037] represents the channel phase error,

[0038] The second aspect of the embodiments of the present invention provides an azimuth multi-channel SAR channel phase error estimation device based on the rotational invariance of the signal subspace matrix, including:

[0039] A compensation module, configured to receive echo data of multiple channels, convert the echo data of each channel into the two-dimensional frequency domain, and compensate for the part of the steering vector that changes with the Doppler frequency in the echo data of each channel to obtain compensated echo data;

[0040] A calculation module, configured to divide the compensated echo data into first sub-array echo data and second sub-array echo data, calculate the covariance matrix after combining the first sub-array echo data and the second sub-array echo data, and perform eigenvalue decomposition on the covariance matrix to respectively obtain a first signal subspace matrix and a second signal subspace matrix for receiving the first sub-array echo data and the second sub-array echo data;

[0041] A determination module, configured to determine a cost function including channel phase error according to the projection matrix of the first signal subspace matrix and the rotational invariance of the first signal subspace matrix and the second signal subspace matrix;

[0042] A solution module, configured to solve the cost function to obtain a phase error estimation value.

[0043] The third aspect of the embodiments of the present invention provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements an azimuth multi-channel SAR channel phase error estimation method provided in the first aspect of the embodiments of the present invention.

[0044] The fourth aspect of the embodiments of the present invention provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements a method for estimating channel phase errors of azimuth multi-channel SAR based on the rotational invariance of the signal subspace matrix provided in the first aspect of the embodiments of the present invention.

[0045] Advantages of the present invention:

[0046] The present invention only needs to perform covariance matrix estimation and eigenvalue decomposition once, significantly reducing the computational complexity of the algorithm. In addition, this method does not require accurate estimation of the Doppler center frequency and can achieve more robust channel phase error estimation.

[0047] Other features and advantages of the present invention will be described in the subsequent specification, and some will become obvious from the specification, or be understood by implementing the present invention. The objectives and other advantages of the present invention can be achieved and obtained through the structures specifically pointed out in the written specification, claims, and drawings.

[0048] The technical solutions of the present invention will be further described in detail below through the drawings and embodiments. Description of the Drawings

[0049] The drawings are used to provide a further understanding of the present invention and constitute a part of the specification. They are used together with the embodiments of the present invention to explain the present invention and do not constitute a limitation to the present invention. In the drawings:

[0050] Figure 1 is a schematic flowchart of a method for estimating channel phase errors of azimuth multi-channel SAR based on the rotational invariance of the signal subspace matrix provided by the embodiments of the present invention;

[0051] Figure 2 is a schematic diagram of the working mechanism of spaceborne azimuth multi-channel SAR provided by the embodiments of the present invention;

[0052] Figure 3 is a schematic diagram of the imaging result without channel phase error correction;

[0053] Figure 4 is a schematic diagram of the imaging result after correcting the channel phase error by a method for estimating channel phase errors of azimuth multi-channel SAR based on the rotational invariance of the signal subspace matrix provided by the embodiments of the present invention;

[0054] Figure 5 is a schematic diagram of a device for estimating channel phase errors of azimuth multi-channel SAR based on the rotational invariance of the signal subspace matrix provided by the embodiments of the present invention. Detailed Embodiments

[0055] The present invention will be further described in detail below in conjunction with specific embodiments, but the implementation manners of the present invention are not limited thereto.

[0056] As Figure 1 shown, in the first aspect of the embodiment of the present invention, a method for estimating the channel phase error of an azimuth multi-channel SAR based on the rotational invariance of the signal subspace matrix includes the following steps:

[0057] Step 11: Receive the echo data of multiple channels, convert the echo data of each channel into the two-dimensional frequency domain, and compensate the part of the steering vector that varies with the Doppler frequency for the echo data of each channel to obtain the compensated echo data.

[0058] The specific steps of Step 11 include Step 111 - Step 114:

[0059] Step 111: Receive the echo data of multiple channels, convert the echo data of each channel into the two-dimensional frequency domain to obtain the echo data in the two-dimensional frequency domain.

[0060] The working mechanism of the spaceborne azimuth multi-channel SAR is as Figure 2 shown. Along the flight direction, M receiving antennas are distributed. They simultaneously receive the beam emitted by the transmitting antenna. Assume the flight speed is V s , the altitude is h, the closest slant range from the radar to the target point is R o , the azimuth size of the entire antenna is D a , and the spacing between receiving channels is d. At this time, considering the multi-channel echo signal with phase errors between channels is expressed as follows:

[0061] S m (τ, η) = exp(jσ m )S 1 (τ, η + Δt m )(1)

[0062] where j represents the imaginary unit, S 1 (τ, η) represents the echo data received by the first channel,

[0063] S m (τ, η) represents the echo data received by the m-th channel, σ m represents the phase error of the m-th channel relative to the first channel, τ and η respectively represent the fast time in the range direction and the slow time in the azimuth direction, and Δt m represents the time delay amount of the m-th channel relative to the reference channel after the equivalent phase center compensation, satisfying the following formula:

[0064]

[0065] Convert the two-dimensional time-domain echo signal S m (τ, η) into the two-dimensional frequency domain and express it as:

[0066] S m (f τ ,f η ) = S 1 (f τ ,f η )exp(jσ m )exp(j2πf η Δt m )(3)

[0067] Where f η represents the azimuth Doppler domain, and f τ represents the range frequency domain. Since the pulse repetition frequency of the system is less than the Doppler bandwidth of the signal, aliasing occurs in the Doppler domain for the echo signal. At this time, the echo signal expression is as follows:

[0068]

[0069] Where k represents the number of the ambiguous frequency of any Doppler, and f p represents the pulse repetition frequency of the system, satisfying S m (f τ ,f η ) represents the echo data received by the m-th channel in the two-dimensional frequency domain. Assuming only the spectrum within the signal bandwidth is considered, and the ambiguous number of Doppler is set to an odd number N, and the numbers of the ambiguous frequencies are -l, 1 - l,..., l - 1, l, satisfying and -l ≤ k ≤ l.

[0070] Step 112, convert the echo data in the two-dimensional frequency domain of each channel into a signal matrix, expressed as:

[0071] s(f τ ,f η ) = ΓH(f η )s 1 (f τ ,f η ) (5)

[0072] Where

[0073] s(f τ ,f η ) = [S 1 (f τ ,f η ), S 2 (f τ ,f η ),..., S M (f τ ,f η )] T (6)

[0074] Γ = diag[exp(jσ 1 ), exp(jσ 2 ),..., exp(jσ M )] (7)

[0075] H(f η ) = [P -l (f η ), P -l+1 (f η ),..., P l (f η )] (8)

[0076] P k (f η ) = [1, exp(j2π(f η + kf p )Δt 2 ),..., exp(j2π(f η + kf p )Δt M )] T (9)

[0077] s 1 (f τ , f η ) = [S 1,-l (f τ , f η ), S 1,-l+1 (f τ , f η ),... S 1,l (f τ , f η )] T (10)

[0078] S 1,k (f τ , f η ) = S 1 (f τ , f η + kf p ) (11)

[0079] diag[·] represents the diagonal matrix of the calculated vector, S 1,k (f τ , f η ) represents the signal when the ambiguity frequency number corresponding to the echo signal of the first channel is k, [·] T represents the transpose of the matrix, Γ represents the phase error matrix between channels, P k (f η) represents the steering vector corresponding to the ambiguity frequency number \(k\), \(H(f η ) represents the steering vector that varies with the Doppler frequency.

[0080] Step 113: Decompose the steering vector that varies with the Doppler frequency in the signal matrix into a part that varies with the Doppler frequency and a vector matrix that does not vary with the Doppler frequency.

[0081] Decompose \(H(f η ) into:

[0082]

[0083] where,

[0084] \(E(f η ) = diag[1, exp(j2πf η Δt 2 ),..., exp(j2πf η Δt M )] (13)

[0085]

[0086] \(E(f η ) represents the part of the steering vector that varies with the Doppler frequency, represents the vector matrix of the steering vector that does not vary with the Doppler frequency, represents the vector that does not vary with the Doppler frequency when the ambiguity frequency number is \(k\).

[0087] Step 114: Compensate the part that varies with the Doppler frequency for the echo data of each channel of the signal matrix to obtain the compensated echo data.

[0088] After compensating the variable \(E(f η ) of the Doppler frequency component \(f\) for the echo data of each channel, we get: η ) for the echo data of each channel, we get:

[0089]

[0090] where, [] H represents the conjugate transpose of the matrix. Considering the noise in each channel, we obtain the compensated echo data:

[0091]

[0092] where, represents the compensated echo data, represents the noise of the echo of each channel.

[0093] Step 12: Divide the compensated echo data into first sub-array echo data and second sub-array echo data, calculate the covariance matrix after combining the first sub-array echo data and the second sub-array echo data, perform eigenvalue decomposition on the covariance matrix, and respectively obtain a first signal subspace matrix and a second signal subspace matrix for receiving the first sub-array echo data and the second sub-array echo data.

[0094] The specific steps of Step 12 include Steps 121 - 124:

[0095] Step 121: Use the first M - 1 receiving antennas and the last M - 1 receiving antennas among the M receiving antennas distributed along the radar flight direction as the first sub-array and the second sub-array respectively. The compensated echo data corresponding to the first sub-array is the first sub-array echo data, and the compensated echo data corresponding to the second sub-array is the second sub-array echo data.

[0096] The first sub-array echo data and the second sub-array echo data are respectively S'(f τ , f η ) and S"(f τ , f η ):

[0097]

[0098] Where:

[0099] Γ" = ΔΓΓ' (19)

[0100]

[0101] D = diag[exp(j2π(lf p )Δt), exp(j2π((1 - l)f p )Δt),.., exp(j2π(lf p )Δt)] (21)

[0102] ΔΓ = diag[exp(j(σ 2 - σ 1 )), exp(j(σ 3 - σ 2 )),..., exp(j(σ M - σ M-1 ))] (22)

[0103]

[0104] Among them, ΔΓ represents the phase error between adjacent channels, D represents the rotation matrix between the steering vector of the first subarray and the steering vector of the second subarray, n' and n" respectively represent the noise of the echoes of each channel of the first subarray and the second subarray, and Δt represents the equivalent delay time between adjacent channels.

[0105] Step 122: Combine the echo data of the first subarray and the echo data of the second subarray to obtain combined echo data.

[0106] The combined echo data G(f τ , f η ) is obtained by:

[0107]

[0108] In the formula:

[0109]

[0110] In the formula, represents the steering vector of the array after the combination of the first subarray and the second subarray, and N(f τ , f η ) represents the noise of the array after the combination of the first subarray and the second subarray.

[0111] Step 123: Calculate the covariance matrix of the combined echo data.

[0112] Calculate the covariance matrix R(f τ , f η ) of the combined echo data G(f η ) as:

[0113]

[0114] In the formula

[0115]

[0116]

[0117] In the formula, represents the expectation operation in the two-dimensional frequency domain, R s represents the signal covariance matrix, and R n represents the noise covariance matrix.

[0118] In this implementation, the echo data received by multiple channels is subjected to two-dimensional Fourier transform, and the part of the steering vector that changes with the Doppler frequency domain is compensated in the Doppler domain. At this time, when estimating the sampling covariance matrix, the sampling covariance matrix can be calculated in the two-dimensional frequency domain, reducing the amount of computation and also reducing the requirement for the signal-to-noise ratio of the data.

[0119] Step 124: Perform eigenvalue decomposition on the covariance matrix to determine the first signal subspace matrix for receiving the echo data of the first subarray and the second signal subspace matrix for receiving the echo data of the second subarray.

[0120] Perform eigenvalue decomposition on the covariance matrix:

[0121]

[0122] where λ i represents the i-th eigenvalue, and e i represents the eigenvector corresponding to the i-th eigenvalue, U s represents the signal subspace matrix, U n represents the noise subspace matrix, Σ s is a diagonal matrix composed of large eigenvalues, and Σ n is a diagonal matrix composed of small eigenvalues. Sort the eigenvalues from large to small. According to the Doppler ambiguity number, the signal subspace and the noise subspace can be determined. Since the space spanned by the signal subspace and the array manifold is the same space, that is

[0123]

[0124] In the formula, span{·} represents the generation space of the matrix. For the azimuth multi-channel synthetic aperture radar system, the signals at different frequency points correspond to the echoes from different beam azimuth angles, and the array manifold matrix is a full-rank matrix. That is, there exists a unique non-singular matrix T such that:

[0125]

[0126] That is:

[0127] U s2 = ΔΓU s1 Ψ(33)

[0128] In the formula, U s1 represents the first signal subspace matrix, U s2 represents the second signal subspace matrix,

[0129] Ψ = T -1 DT. Equation (33) reflects the rotational invariance of the signal subspaces of the data received by the two subarrays.

[0130] Step 13: Determine the cost function including the channel phase error according to the projection matrix of the first signal subspace matrix and the rotational invariance of the first signal subspace matrix and the second signal subspace matrix.

[0131] The specific steps of Step 13 include Step 131 - Step 135:

[0132] Step 131, calculate the projection matrix of the first signal subspace matrix.

[0133] The projection matrix of the first signal subspace matrix U s1 is:

[0134]

[0135] Step 132, construct the first objective function according to the rotational invariance of the first signal subspace matrix and the second signal subspace matrix.

[0136] According to the rotational invariance of the subspace in Equation (33), we can obtain:

[0137] ΔΓ H U s2 =U s1 Ψ(35)

[0138] To make the above equation hold, using the least squares principle, we hope to solve the minimization problem of the following first objective function, that is:

[0139]

[0140] In the formula, argmin(·) represents finding the parameter that satisfies a certain condition, represents the solution when is the smallest.

[0141] Step 133, use the least squares method to solve the auxiliary parameters of the first objective function.

[0142] In the first objective function, the parameter we care about is ΔΓ. At this time, Ψ is only an auxiliary parameter. Therefore, taking ΔΓ as the variable, the least squares solution of Ψ is:

[0143]

[0144] Step 134, determine the second objective function according to the projection matrix of the first signal subspace matrix, the least squares solution of the auxiliary parameter, and the first objective function.

[0145] According to Equation (37) and Equation (34), transform the first objective function into the second objective function of Equation (38):

[0146]

[0147] Among them, I represents the identity matrix.

[0148] Step 135, transform the second objective function into a cost function including channel phase errors according to the square of the matrix F norm and the matrix trace.

[0149] The equality of the square of the matrix F norm and the matrix trace satisfies the following relationship:

[0150]

[0151] Let After further simplification, we have:

[0152]

[0153] where n = M - 1, (·) * represents taking the conjugate, and (·) i,j represents the element in the i-th row and j-th column of the matrix.

[0154] The trace of

[0155]

[0156] can be expressed as the following formula:

[0157]

[0158] where ⊙ represents the matrix dot product operation. It can be seen that the trace of the matrix can be transformed into the following formula:

[0159]

[0160] where Substituting Equation (43) into Equation (39), the cost function can be transformed into the following formula:

[0161]

[0162] Solving for also represents solving the phase error between channels.

[0163] Step 14: Solve the cost function to obtain the phase error estimate.

[0164] Solving the solution of the cost function, finally obtain the accurate channel phase error estimate

[0165] Minimizing the F-norm to solve the phase error between channels can be transformed into solving the optimal solution of the following formula (45):

[0166]

[0167] where

[0168]

[0169] Solving the phase error problem between channels is transformed into a constant modulus quadratic programming problem. Assuming the optimal solution of the above problem is Then the estimated value of the phase deviation of the i-th channel relative to the reference channel is:

[0170]

[0171] Step 15: Reconstruct the Doppler spectrum of the echo data after being corrected by the phase error estimation value, and use the CS imaging algorithm to obtain an unambiguous high-resolution wide-swath SAR image. The present invention selects the classical CS imaging algorithm to perform focusing imaging on the signal after reconstructing the Doppler spectrum.

[0172] The method of the present invention can quickly and accurately estimate the phase error between channels, obtain a high-resolution wide-swath SAR image, is not affected by the estimation accuracy of the Doppler center frequency, has a low computational complexity, and improves the reliability of accurate correction of the channel phase error.

[0173] The algorithm of the present invention is verified and described as follows:

[0174] Select the airborne six-channel measured data for processing. This scene image was collected on April 22, 2017.

[0175] Figure 3 The imaging result without channel phase error correction is shown. The azimuth ambiguity in the image is very serious, and the generated false targets submerge the original real targets.

[0176] Figure 4 The result after correcting the channel phase error by the algorithm of the present invention is shown. It can be seen that Figure 3 the azimuth ambiguity in [reference] has almost completely disappeared, and the false targets are effectively suppressed.

[0177] As Figure 5 shown, the second aspect of the embodiment of the present invention provides an azimuth multi-channel SAR channel phase error estimation device based on the rotational invariance of the signal subspace matrix, including:

[0178] A compensation module 21, configured to receive the echo data of multiple channels, convert the echo data of each channel into the two-dimensional frequency domain, and compensate the part of the steering vector that changes with the Doppler frequency for the echo data of each channel to obtain compensated echo data;

[0179] A calculation module 22, configured to divide the compensated echo data into first sub-array echo data and second sub-array echo data, calculate the covariance matrix after combining the first sub-array echo data and the second sub-array echo data, perform eigenvalue decomposition on the covariance matrix, and respectively obtain a first signal subspace matrix and a second signal subspace matrix for receiving the first sub-array echo data and the second sub-array echo data;

[0180] A determination module 23, configured to determine a cost function including channel phase errors according to a projection matrix of a first signal subspace matrix and the rotational invariance of the first signal subspace matrix and a second signal subspace matrix;

[0181] A solution module 24, configured to solve the cost function to obtain an estimated value of the phase error.

[0182] In an embodiment of the present invention, receiving echo data of multiple channels, converting the echo data of each channel into a two-dimensional frequency domain, and compensating a part of the steering vector that varies with the Doppler frequency for the echo data of each channel to obtain compensated echo data, including:

[0183] Receiving echo data of multiple channels, converting the echo data of each channel into a two-dimensional frequency domain to obtain echo data in the two-dimensional frequency domain;

[0184] Converting the echo data of each channel in the two-dimensional frequency domain into a signal matrix;

[0185] Decomposing the steering vector that varies with the Doppler frequency in the signal matrix into a part that varies with the Doppler frequency and a vector matrix that does not vary with the Doppler frequency;

[0186] Compensating the part that varies with the Doppler frequency and noise for the echo data of each channel of the signal matrix to obtain compensated echo data.

[0187] In an embodiment of the present invention, dividing the compensated echo data into first sub-array echo data and second sub-array echo data, calculating a covariance matrix after combining the first sub-array echo data and the second sub-array echo data, and performing eigenvalue decomposition on the covariance matrix to respectively obtain a first signal subspace matrix for receiving the first sub-array echo data and a second signal subspace matrix for receiving the second sub-array echo data, including:

[0188] Regarding the first (M - 1) receiving antennas and the last (M - 1) receiving antennas among the M receiving antennas distributed along the radar flight direction as the first sub-array and the second sub-array respectively, the compensated echo data corresponding to the first sub-array is the first sub-array echo data, and the compensated echo data corresponding to the second sub-array is the second sub-array echo data;

[0189] Combining the first sub-array echo data and the second sub-array echo data to obtain combined echo data;

[0190] Calculating the covariance matrix of the combined echo data;

[0191] Performing eigenvalue decomposition on the covariance matrix to determine a first signal subspace matrix for receiving the first sub-array echo data and a second signal subspace matrix for receiving the second sub-array echo data.

[0192] In one embodiment of the present invention, determining a cost function including channel phase error based on the projection matrix of the first signal subspace matrix and the rotational invariance of the first signal subspace matrix and the second signal subspace matrix includes:

[0193] Calculating the projection matrix of the first signal subspace matrix;

[0194] Constructing a first objective function according to the rotational invariance of the first signal subspace matrix and the second signal subspace matrix;

[0195] Solving the auxiliary parameters of the first objective function by using the least squares method;

[0196] Determining a second objective function according to the least squares solution of the auxiliary parameters and the first objective function;

[0197] Converting the second objective function into a cost function including channel phase error according to the sum of the squares of the matrix F norm and the matrix trace.

[0198] In one embodiment of the present invention, it further includes:

[0199] A reconstruction module 25, configured to reconstruct the Doppler spectrum of the echo data after being corrected by the phase error estimation value, and obtain an unambiguous high-resolution wide-swath SAR image by using the CS imaging algorithm.

[0200] In one embodiment of the present invention, the rotational invariance of the first signal subspace matrix and the second signal subspace matrix satisfies: ΔΓ H U s2 = U s1 Ψ;

[0201] Wherein, U s1 represents the first signal subspace matrix, U s2 represents the second signal subspace matrix, Ψ = T -1 DT, represents the auxiliary parameter, D represents the rotation matrix of the steering vectors of the first subarray and the second subarray, ΔΓ represents the phase error between adjacent channels, and T represents a non-singular matrix;

[0202] The first objective function is:

[0203] The least squares solution of Ψ is:

[0204] The second objective function is:

[0205]

[0206] In one embodiment of the present invention, the cost function is:

[0207]

[0208] Among them,

[0209] represents the channel phase error,

[0210] The third aspect of the embodiments of the present invention provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements the method for estimating the azimuth multi-channel SAR channel phase error based on the rotational invariance of the signal subspace matrix provided by the embodiments of the present invention.

[0211] The fourth aspect of the embodiments of the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the steps of the method for estimating the azimuth multi-channel SAR channel phase error based on the rotational invariance of the signal subspace matrix provided by the embodiments of the present invention.

[0212] Among them, the memory may include a random access memory (RAM), and may also include a non-volatile memory (NVM), such as at least one disk memory. Optionally, the memory may also be at least one storage device located far from the aforementioned processor.

[0213] The aforementioned processor may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware devices.

[0214] The method provided by the embodiments of the present invention can be applied to an electronic device. Specifically, the electronic device may be: a desktop computer, a portable computer, a smart mobile terminal, a server, etc. This is not limited herein. Any electronic device that can implement the present invention belongs to the protection scope of the present invention.

[0215] For the device / electronic device embodiments, since they are basically similar to the method embodiments, the description is relatively simple. For related parts, refer to the partial description of the method embodiments.

[0216] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatus (devices), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, and combinations of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing device to produce a machine such that the instructions executed by the processor of the computer or other programmable data processing device generate means for implementing the functions specified in one Figure 1 flow or multiple flows and / or blocks Figure 1 block or multiple blocks.

[0217] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufactured article including instruction means that implement the functions specified in one Figure 1 flow or multiple flows and / or blocks Figure 1 block or multiple blocks.

[0218] These computer program instructions can also be loaded onto a computer or other programmable data processing device such that a series of operation steps are executed on the computer or other programmable device to produce a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one Figure 1 flow or multiple flows and / or blocks Figure 1 block or multiple blocks.

[0219] Obviously, those skilled in the art can make various modifications and variations to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. A method for estimating channel phase errors of azimuth multi-channel SAR based on rotational invariance of signal subspace matrix, characterized in that: The following steps are involved: Receiving echo data of multiple channels, converting the echo data of each channel into a two-dimensional frequency domain, and compensating the echo data of each channel for the part of the steering vector that varies with the Doppler frequency to obtain compensated echo data; Dividing the compensated echo data into first subarray echo data and second subarray echo data, calculating a covariance matrix after merging the first subarray echo data and the second subarray echo data, and performing eigendecomposition on the covariance matrix to obtain a first signal subspace matrix and a second signal subspace matrix for receiving the first subarray echo data and the second subarray echo data, respectively; Determining a cost function including a channel phase error according to a projection matrix of the first signal subspace matrix and a rotation invariance of the first signal subspace matrix and the second signal subspace matrix; The cost function is solved to obtain a phase error estimate.

2. The method according to claim 1, characterized in that The receiving of echo data of multiple channels, converting the echo data of each channel into a two-dimensional frequency domain, and compensating the echo data of each channel for the part of the steering vector that varies with the Doppler frequency to obtain compensated echo data, includes: Receiving echo data of multiple channels, converting the echo data of each channel into a two-dimensional frequency domain, and obtaining echo data in the two-dimensional frequency domain; Convert the two-dimensional frequency domain echo data of each channel into a signal matrix; Decomposing the steering vector in the signal matrix that changes with the Doppler frequency into a part that changes with the Doppler frequency and a vector matrix that does not change with the Doppler frequency; The echo data of each channel of the signal matrix is ​​compensated for the part and noise that vary with the Doppler frequency to obtain compensated echo data.

3. The method according to claim 1, characterized in that The method of dividing the compensated echo data into first subarray echo data and second subarray echo data, merging the first subarray echo data and the second subarray echo data, calculating a covariance matrix, and performing eigendecomposition on the covariance matrix to obtain a first signal subspace matrix and a second signal subspace matrix for receiving the first subarray echo data and the second subarray echo data, respectively, includes: The first M-1 receiving antennas and the last M-1 receiving antennas of the M receiving antennas distributed along the flight direction of the radar are respectively used as the first subarray and the second subarray, the compensation echo data corresponding to the first subarray is the first subarray echo data, and the compensation echo data corresponding to the second subarray is the second subarray echo data; Merging the first sub-array echo data and the second sub-array echo data to obtain merged echo data; Calculating a covariance matrix of the combined echo data; Performing eigendecomposition on the covariance matrix to determine a first signal subspace matrix for receiving the first subarray echo data and a second signal subspace matrix for receiving the second subarray echo data.

4. The method according to claim 1, characterized in that Determining a cost function including a channel phase error according to a projection matrix of the first signal subspace matrix and rotational invariance of the first signal subspace matrix and the second signal subspace matrix comprises: Calculating a projection matrix of the first signal subspace matrix; Constructing a first objective function according to the rotation invariance of the first signal subspace matrix and the second signal subspace matrix; The auxiliary parameters of the first objective function are solved by the least square method; Determining a second objective function according to a least square solution of the auxiliary parameter and the first objective function; The second objective function is converted into a cost function including channel phase error according to the square of the norm of the matrix F and the matrix trace.

5. The method according to claim 1, characterized in that The method further comprises: The Doppler spectrum of the echo data after phase error estimation correction is reconstructed, and the CS imaging algorithm is used to obtain an unambiguous high-resolution wide-swath SAR image.

6. The method according to claim 4, characterized in that The rotation invariance of the first signal subspace matrix and the second signal subspace matrix satisfies: ΔΓ H U s2 =U s1 Ψ; Among them, U s1 represents the first signal subspace matrix, U s2 represents the second signal subspace matrix, Ψ=T -1 DT, D represents the rotation matrix of the first subarray and the second subarray steering vector, ΔΓ represents the phase error between adjacent channels, and T represents a non-singular matrix; The first objective function is: The least squares solution for Ψ is: The second objective function is:

7. The method according to claim 6, characterized in that The cost function is: in, represents the channel phase error, 8. An azimuth multi-channel SAR channel phase error estimation device based on signal subspace matrix rotation invariance, characterized in that: include: A compensation module is used to receive echo data of multiple channels, convert the echo data of each channel into a two-dimensional frequency domain, and compensate the echo data of each channel for the part of the steering vector that changes with the Doppler frequency to obtain compensated echo data; a calculation module, used for dividing the compensated echo data into first sub-array echo data and second sub-array echo data, calculating a covariance matrix after merging the first sub-array echo data and the second sub-array echo data, and performing eigendecomposition on the covariance matrix to obtain a first signal subspace matrix and a second signal subspace matrix for receiving the first sub-array echo data and the second sub-array echo data, respectively; A determination module, configured to determine a cost function including a channel phase error according to a projection matrix of the first signal subspace matrix and a rotational invariance of the first signal subspace matrix and the second signal subspace matrix; The solution module is used to solve the cost function to obtain a phase error estimate.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the azimuth multi-channel SAR channel phase error estimation method based on signal subspace matrix rotation invariance as described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the azimuth multi-channel SAR channel phase error estimation method based on signal subspace matrix rotation invariance according to any one of claims 1 to 7 is implemented.

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