Singular value decomposition sea clutter suppression method based on fractional Fourier transform

Abstract

The invention discloses a singular value decomposition sea clutter suppression method based on fractional Fourier transform. The singular value decomposition sea clutter suppression method comprises the steps that phase demodulation is carried out after target echo signals are rearranged; performing fractional Fourier transform on the demodulated target echo signal to obtain a transformed target echo signal, and performing inverse Fourier transform on the transformed target echo signal to transform the transformed target echo signal back to a time domain; singular value decomposition is carried out on the target echo signal transformed back to the time domain, a signal subspace matrix is constructed, and the target echo signal transformed back to the time domain is projected into the signal subspace matrix to obtain a target echo signal after sea clutter suppression; and finally, Fourier transform and inverse fractional Fourier transform are carried out to obtain a target echo signal after sea clutter suppression. According to the method, the problem that an existing traditional clutter suppression method cannot suppress the sea clutter overlapped with the target on the time-frequency map is solved, and the detection capability of the radar is improved.

Description

Technical Field

[0001] The invention belongs to the technical field of radars, and in particular relates to a singular value decomposition sea clutter suppression method based on fractional Fourier transform. Background Art

[0002] When the radar processes the received signal, the clutter component will have an adverse effect on the detection of the target signal. If it is not suppressed, the target cannot be accurately and effectively detected. In the real sea clutter environment, the sea clutter interference intensity is high, the types of interference faced by the radar are complex, the frequency center of the spectrum changes greatly, and it often has non-Gaussian and non-stationary characteristics; at the same time, the Doppler frequency shift of low-speed targets on the sea surface is small, and there is a serious overlap with strong clutter and low-frequency clutter in the Doppler domain.

[0003] Although traditional clutter suppression methods such as MTI and MTD algorithms can filter out clutter in the time domain or frequency domain, it is difficult to distinguish between target and clutter signals when slow-moving target detection is performed when the target and zero-frequency clutter overlap severely in the Doppler domain. The subspace-based algorithm is based on signal characteristics. It decomposes the echo signal matrix and separates the clutter subspace, thereby suppressing the clutter components in the signal, and has a strong ability to extract weak target features and highlight the target. Sira et al. proposed a sea clutter suppression technology based on the subspace method. The sea clutter is suppressed by estimating the sea clutter subspace. The selection of the clutter subspace directly affects the suppression effect; Rafaat Khan et al. proposed a sea clutter suppression technology based on singular value decomposition. This algorithm is difficult to effectively suppress sea clutter when the Bragg peak of sea clutter is not significant.

[0004] In order to solve the above problems, the prior art with publication number CN116224277A combines time-frequency spectrum energy distribution with singular value decomposition, which can effectively filter out sea clutter signals and protect the energy of low-speed target signals. While filtering out interference signals, it can reduce the loss of effective signals, improve the target signal-to-noise ratio, and avoid the problem that the sea clutter suppression effect is poor in a strong sea clutter environment and weak targets are easily suppressed as clutter; however, when the target overlaps with the clutter on the time-frequency diagram, it is difficult for the prior art method to effectively separate the target from the clutter. Summary of the invention

[0005] In order to solve the above problems existing in the prior art, the present invention provides a sea clutter suppression method based on singular value decomposition of fractional Fourier transform:

[0006] The technical problem to be solved by the present invention is achieved by the following method, comprising the following steps:

[0007] Echo signal rearrangement step: rearrange the target echo signal into a fast and slow time dimension matrix according to the period;

[0008] The echo signal phase demodulation step: performing phase demodulation on the target echo signal rearranged into a fast and slow time dimension matrix according to the period to obtain a demodulated target echo signal;

[0009] Fractional Fourier transform step: performing a fractional Fourier transform on the demodulated target echo signal according to the optimal fractional order to obtain a target echo signal transformed into the optimal fractional order fractional domain;

[0010] Inverse Fourier transform step: performing inverse Fourier transform on the target echo signal transformed into the optimal fractional order fractional domain to obtain the target echo signal transformed back into the time domain;

[0011] Singular value decomposition clutter suppression steps: perform singular value decomposition on the target echo signal transformed back to the time domain to obtain the singular value decomposition result of the target echo signal, select the large singular value representing the clutter component to obtain a singular value set, and construct a signal subspace matrix orthogonal to the clutter subspace based on the singular value set; project the target echo signal transformed back to the time domain into the signal subspace matrix to obtain the target echo signal after clutter suppression;

[0012] Fourier transform step: perform Fourier transform on the target echo signal after clutter suppression to obtain the target echo signal after clutter suppression transformed back to the fractional domain;

[0013] Inverse fractional Fourier transform step: performing inverse fractional Fourier transform on the clutter-suppressed target echo signal transformed back to the fractional domain in sequence according to the slow time dimension to obtain the target echo signal with clutter suppressed.

[0014] Furthermore, in the echo signal rearrangement step, it also includes:

[0015] The target signal transmitted by the radar is phase modulated to obtain a modulated target signal, and the modulated target signal is received by the receiving end to obtain a target echo signal.

[0016] Furthermore, the target signal emitted by the radar is phase modulated to obtain a modulated target signal, which is implemented as follows:

[0017]

[0018] Among them, S(t) is the modulated target signal, N is the number of sampling points, T r is the pulse repetition period, S1(t) is the target signal emitted by the radar, m and n represent parameters ranging from 1 to N, t is the continuous time, and C1(m) is the phase coded signal.

[0019] Furthermore, the modulated target signal is received by the receiving end to obtain a target echo signal, which is implemented as follows:

[0020] X(l)=fs S(l)+Z b (l)+Z s (l)l=0,…,N-1

[0021]

[0022] Z b (l)=[z b1 (l),…,z bm (l),…,z bN (l)]

[0023] Z s (l) = [z s1 (l),…,z sm (l),…,z sN (l)]

[0024] Where X(l) represents the target echo signal, f s represents the Doppler vector of the target echo, f d represents the Doppler vector frequency, S(l) is the echo signal of the modulated target signal S(t), and Z b (l) represents the clutter of the lth range unit, Z s (l) represents the noise vector of the lth distance unit, and l represents a distance unit with a value range of 0 to N-1.

[0025] Furthermore, the echo signal rearrangement step is implemented as follows:

[0026]

[0027] Among them, the M×N-order matrix X is the target echo signal rearranged into a fast and slow time dimension matrix according to the period, and M is the number of pulses.

[0028] Furthermore, the echo signal phase demodulation step is implemented as follows:

[0029]

[0030] Wherein, R(t) is the demodulated target echo signal, C2(m) is the demodulated signal, j is a complex number, and μ is the frequency modulation slope.

[0031] Furthermore, the optimal fractional order p is calculated as follows:

[0032]

[0033] Among them, B is the signal bandwidth and Fs is the signal sampling frequency.

[0034] Furthermore, in the singular value decomposition clutter suppression step, the target echo signal transformed back to the time domain is subjected to singular value decomposition to obtain a singular value decomposition result of the target echo signal matrix, which is implemented as follows:

[0035]

[0036] Among them, X c To transform the target echo signal back to the time domain, C, T, and N represent the singular value set of the clutter subspace, the singular value set of the target subspace, and the singular value set of the noise subspace, respectively. The superscript () H represents the conjugate transpose of the matrix, u i For X c The i-th left singular vector of i For X c The i-th right singular vector of i is the i-th singular value, and u i >v i >λ i And they are arranged in descending order from largest to smallest.

[0037] Furthermore, in the singular value decomposition clutter suppression step, a signal subspace matrix orthogonal to the clutter subspace is constructed based on the singular value set, including:

[0038] The vector product of the singular vectors representing the clutter subspace is calculated based on the singular value set. The calculation formula is as follows:

[0039]

[0040] Among them, P c represents the singular vector cross product;

[0041] The signal subspace matrix orthogonal to the clutter subspace is constructed based on the least squares method according to the vector product of the singular vectors. The calculation formula is as follows:

[0042]

[0043] in, is the signal subspace matrix, I represents the identity matrix, P c represents the singular vector cross product.

[0044] Furthermore, in the singular value decomposition clutter suppression step, the target echo signal transformed back to the time domain is projected into the signal subspace matrix to obtain the target echo signal after clutter suppression, and the calculation formula is as follows:

[0045]

[0046] Among them, X rIt indicates the target echo signal after clutter suppression. represents the signal subspace matrix, X c Represents the target echo signal transformed back to the time domain.

[0047] Compared with the prior art, the present invention has the following beneficial effects:

[0048] The present invention adopts a sea clutter suppression processing method based on singular value decomposition of fractional Fourier transform, and utilizes the characteristic that radar signals have better energy concentration in the fractional domain compared with single-frequency signals. Fractional Fourier transform is combined with singular value decomposition, which can effectively filter out sea clutter signals, and at the same time reduce the loss of effective signals and improve the target signal-to-noise ratio. It overcomes the problem that the existing traditional clutter suppression method cannot suppress the sea clutter overlapping with the target on the time-frequency diagram, and improves the detection capability of the radar.

[0049] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 is a flow chart of a method for suppressing sea clutter based on singular value decomposition of fractional Fourier transform provided by an embodiment of the present invention;

[0051] Figure 2 This is the spectrum diagram before sea clutter suppression in the present invention;

[0052] Figure 3 (a) is the range-Doppler diagram before sea clutter suppression in the present invention;

[0053] Figure 3 (b) is a normalized amplitude diagram of range-velocity before sea clutter suppression in the present invention;

[0054] Figure 4 (a) is the range-Doppler diagram of the suppression result of the traditional singular value decomposition sea clutter suppression method;

[0055] Figure 4 (b) is the normalized amplitude diagram of the range-velocity suppression result of the traditional singular value decomposition sea clutter suppression method;

[0056] Figure 5 (a) A range-Doppler diagram of the sea clutter suppression result of the sea clutter suppression method based on fractional Fourier transform singular value decomposition provided by the present invention;

[0057] Figure 5 (b) is a normalized amplitude diagram of distance-velocity of sea clutter suppression results of the sea clutter suppression method based on singular value decomposition of fractional Fourier transform provided by the present invention;

[0058] Figure 6(a) A range-Doppler diagram of target detection results after sea clutter suppression using the singular value decomposition (SVD) sea clutter suppression method based on fractional Fourier transform provided by the present invention;

[0059] Figure 6 (b) is a normalized amplitude diagram of the distance-speed of the target detection result after sea clutter suppression by the singular value decomposition method based on fractional Fourier transform provided by the present invention. DETAILED DESCRIPTION

[0060] The present invention will be further described below in conjunction with the accompanying drawings and embodiments, but the embodiments of the present invention are not limited thereto.

[0061] In order to overcome the problem that the existing traditional clutter suppression method cannot suppress the sea clutter overlapping with the target in the time-frequency diagram and improve the radar's detection capability of the target, the embodiment of the present invention provides a sea clutter suppression method based on singular value decomposition of fractional Fourier transform, such as Figure 1 As shown, the method comprises the following steps:

[0062] Step 1: The signal transmitted by the radar is a linear frequency modulation signal. The target signal transmitted by the radar is phase modulated to obtain a modulated target signal. The implementation is as follows:

[0063]

[0064] Among them, S(t) is the modulated target signal, N is the number of sampling points, T r is the pulse repetition period, S1(t) is the target signal emitted by the radar, m and n represent parameters ranging from 1 to N, t is the continuous time, and C1(m) is the phase coded signal.

[0065]

[0066] Among them, τ is the signal time width, B is the signal bandwidth, μ is the frequency modulation slope,

[0067] Where C1(m) is the phase encoding signal, expressed as:

[0068]

[0069] in, is the frequency modulation slope.

[0070] The modulated target signal is received by the receiving end to obtain the target echo signal. The implementation is as follows:

[0071] X(l)=f s S(l)+Z b (l)+Z s (l)l=0,…,N-1

[0072]

[0073] Z b (l) = [z b1 (l),…,z bm (l),…,z bN (l)]

[0074] Z s (l) = [z s1 (l),…,z sm (l),…,z sN (l)]

[0075] Where, X(l) represents the target echo signal, f s represents the Doppler vector of the target echo, f d represents the Doppler vector frequency, S(l) is the echo signal of the modulated target signal S(t), and Z b (l) represents the clutter of the lth range unit, Z s (l) represents the noise vector of the lth distance unit, and l represents a distance unit with a value range of 0 to N-1.

[0076] The target echo signal is rearranged into a fast and slow time dimension matrix according to the period, which can be realized as follows:

[0077]

[0078] Among them, the M×N-order matrix X is the target echo signal rearranged into a fast and slow time dimension matrix according to the period, and M is the number of pulses.

[0079] Step 2: Phase demodulate the target echo signal rearranged into a fast and slow time dimension matrix according to the period to obtain the demodulated target echo signal, which is implemented as follows:

[0080]

[0081] Where R(t) is the demodulated target echo signal, C2(m) is the demodulated signal, j is a complex number, μ is the frequency modulation slope, And the demodulated signal C2(m) is the conjugate signal of the phase-encoded signal C1(m), C(m)*C.(m)=1

[0082] After demodulation, the target signal emitted by the radar is restored to a single-frequency signal, and its delay and Doppler information remain unchanged, while the C2(m)Z of the clutter part b (t-nT r ) and C2(m)Z s (t-nT r) is equivalent to expanding the clutter, so that the originally concentrated clutter components are dispersed in the frequency domain, and the clutter signal presents a linear frequency modulation characteristic. At this time, there is a difference between the clutter signal and the target echo signal in the slow time dimension.

[0083] Step 3: Perform a fractional Fourier transform on the demodulated target echo signal according to the optimal fractional order to obtain the target echo signal transformed into the optimal fractional order fractional domain. The specific steps are:

[0084] Performing Fourier transform on a single-frequency signal in the time domain makes the signal appear as an impulse function in the frequency domain, that is, energy concentration is achieved. Similarly, performing fractional Fourier transform (FRFT) on the demodulated target echo signal at a specific fractional order can make the signal appear as an impulse function in the fractional Fourier domain, that is, energy concentration is achieved. However, fractional Fourier transforms of different fractional orders have different energy concentration effects on the same target echo signal. Improper selection of the fractional order will lead to unsatisfactory energy concentration effects. Therefore, it is necessary to determine an optimal fractional order for fractional Fourier transform through peak search. The calculation formula for the optimal fractional order p is as follows:

[0085]

[0086] Among them, B is the signal bandwidth and Fs is the signal sampling frequency.

[0087] After the linear frequency modulation signal is subjected to fractional Fourier transform with the best fractional order, it appears as a pulse with concentrated energy in the fractional domain, while the single-frequency signal can obtain the best energy concentration effect by Fourier transform, that is, the fractional order is 1, and the energy in the other orders is relatively dispersed. Therefore, after phase demodulation of the target echo signal and then fractional Fourier transform, the energy difference between the target and the clutter can be made more obvious in the fractional order. The original target and clutter frequency aliasing is difficult to decompose in the subspace through singular value decomposition (SVD). After transformation to the fractional Fourier transform domain, the two have a large degree of distinction, and the singular value decomposition method can be directly used for clutter suppression.

[0088] Step 4: Perform an inverse Fourier transform (IFFT) on the target echo signal transformed into the optimal fractional-order fractional domain to obtain a target echo signal transformed back into the time domain.

[0089] Singular value decomposition is a clutter suppression method for time domain signals, so it is necessary to transform the target echo signal in the fractional domain back to the time domain before performing clutter suppression.

[0090] Step 5: construct a signal subspace matrix based on the target echo signal transformed back to the time domain, and project the target echo signal transformed back to the time domain into the signal subspace matrix to obtain the target echo signal after suppressing sea clutter. The specific steps are:

[0091] Perform singular value decomposition on the target echo signal transformed back to the time domain to obtain the singular value decomposition result of the target echo signal. The implementation is as follows:

[0092]

[0093] Among them, X c To transform the target echo signal back to the time domain, C, T, and N represent the singular value set of the clutter subspace, the singular value set of the target subspace, and the singular value set of the noise subspace, respectively. The superscript () H represents the conjugate transpose of the matrix, u i For X c The i-th left singular vector of i For X c The i-th right singular vector of i is the i-th singular value, and u i >v i >λ i And they are arranged in descending order from largest to smallest.

[0094] The vector product of the singular vectors representing the sea clutter subspace is calculated based on the singular value set. The calculation formula is as follows:

[0095]

[0096] Among them, P c represents the singular vector cross product.

[0097] The signal subspace matrix orthogonal to the clutter subspace is constructed based on the least squares method according to the vector product of the singular vectors. The calculation formula is as follows:

[0098]

[0099] in, is the signal subspace matrix, I represents the identity matrix, P c represents the singular vector cross product.

[0100] Project the target echo signal transformed back to the time domain into the signal subspace matrix to obtain the target echo signal after suppressing sea clutter. The calculation formula is as follows:

[0101]

[0102] Among them, X r It indicates the target echo signal after suppressing sea clutter. represents the signal subspace matrix, X c Represents the target echo signal transformed back to the time domain.

[0103] Step 6: Perform Fourier transform (FFT) on the target echo signal after suppressing sea clutter to obtain the target echo signal after suppressing sea clutter transformed back to the fractional domain.

[0104] Step 7: Perform inverse fractional Fourier transform (IFRFT) on the target echo signals after sea clutter suppression that have been transformed back to the fractional domain in the slow time dimension to obtain target echo signals that have completed sea clutter suppression.

[0105] The present invention adopts a sea clutter suppression processing method based on singular value decomposition of fractional Fourier transform, which utilizes the characteristic that the linear frequency modulation signal emitted by the radar has better energy concentration in the fractional domain compared with the single-frequency signal, and combines the fractional Fourier transform with the singular value decomposition, which can effectively filter out the sea clutter signal, and at the same time can reduce the loss of effective signals and improve the target signal-to-noise ratio. It overcomes the problem that the existing traditional clutter suppression method cannot suppress the sea clutter overlapping with the target on the time-frequency diagram, and improves the detection capability of the radar.

[0106] Figure 2 This is the frequency spectrum diagram before sea clutter suppression in the present invention. It can be seen that the clutter and the target overlap on the time-frequency diagram before suppression and cannot be effectively distinguished.

[0107] Figure 3 (a) is the range-Doppler diagram before sea clutter suppression in the present invention. In order to obtain the target velocity information intuitively, the Doppler frequency is converted to the velocity to obtain the normalized amplitude of the range-velocity diagram, as shown in FIG. Figure 3 (b) as shown.

[0108] Figure 4 (a) is the range-Doppler diagram of the suppression result of the traditional singular value decomposition sea clutter suppression method. Figure 4 (b) is the normalized amplitude diagram of the range-velocity suppression result of the traditional singular value decomposition sea clutter suppression method. It can be seen that when the target and clutter overlap on the time-frequency diagram, the traditional singular value decomposition algorithm is difficult to effectively separate the target from the clutter.

[0109] Figure 5 (a) is a range-Doppler diagram of the sea clutter suppression result of the sea clutter suppression method based on fractional Fourier transform singular value decomposition provided by the present invention, Figure 5 (b) is a normalized amplitude diagram of distance-velocity of the sea clutter suppression result of the sea clutter suppression method based on fractional Fourier transform provided by the present invention. It can be seen that after using the method of the present invention, the sea clutter component is greatly reduced and well suppressed, the target component can be highlighted, and the sea clutter suppression effect of the method of the present invention is significantly better than that of the traditional singular value decomposition method.

[0110] Figure 6(a) is a range-Doppler diagram of target detection results after sea clutter suppression using the singular value decomposition sea clutter suppression method based on fractional Fourier transform provided by the present invention, Figure 6 (b) is a normalized amplitude diagram of the distance-speed of the target detection result after sea clutter suppression by the singular value decomposition based on fractional Fourier transform provided by the present invention. It can be seen that the obtained target distance and speed information are very close to the initially set parameter values, indicating that after sea clutter suppression by the method of the present invention, the output signal-to-noise ratio is improved, which is convenient for subsequent target detection and other processing.

[0111] The above contents are further detailed descriptions of the present invention in combination with specific preferred embodiments, and it cannot be determined that the specific implementation of the present invention is limited to these descriptions. For ordinary technicians in the technical field to which the present invention belongs, several simple deductions or substitutions can be made without departing from the concept of the present invention, which should be regarded as falling within the protection scope of the present invention.

Claims

1. A sea clutter suppression method based on singular value decomposition of fractional Fourier transform, characterized in that: The following steps are involved: Echo signal rearrangement step: rearrange the target echo signal into a fast and slow time dimension matrix according to the period; The echo signal phase demodulation step: performing phase demodulation on the target echo signal rearranged into a fast and slow time dimension matrix according to the period to obtain a demodulated target echo signal; Fractional Fourier transform step: performing a fractional Fourier transform on the demodulated target echo signal according to an optimal fractional order to obtain a target echo signal transformed into a fractional domain of the optimal fractional order; Inverse Fourier transform step: performing inverse Fourier transform on the target echo signal transformed into the optimal fractional order fractional domain to obtain the target echo signal transformed back into the time domain; Singular value decomposition clutter suppression step: performing singular value decomposition on the target echo signal transformed back to the time domain to obtain a singular value decomposition result of the target echo signal, selecting a large singular value characterizing the clutter component to obtain a singular value set, and constructing a signal subspace matrix orthogonal to the clutter subspace based on the singular value set; projecting the target echo signal transformed back to the time domain into the signal subspace matrix to obtain a target echo signal after clutter suppression; Fourier transform step: performing Fourier transform on the target echo signal after clutter suppression to obtain the target echo signal after clutter suppression transformed back into the fractional domain; Inverse fractional Fourier transform step: performing inverse fractional Fourier transform on the clutter-suppressed target echo signal transformed back to the fractional domain in sequence according to the slow time dimension to obtain the target echo signal with clutter suppressed.

2. The sea clutter suppression method based on singular value decomposition of fractional Fourier transform according to claim 1 is characterized in that: The echo signal rearrangement step also includes: The target signal emitted by the radar is phase modulated to obtain a modulated target signal, and the modulated target signal is received by a receiving end to obtain a target echo signal.

3. The sea clutter suppression method based on singular value decomposition of fractional Fourier transform according to claim 2 is characterized in that: The target signal emitted by the radar is phase modulated to obtain the modulated target signal, which is implemented as follows: Among them, S(t) is the modulated target signal, N is the number of sampling points, T r is the pulse repetition period, S1(t) is the target signal emitted by the radar, m and n represent parameters ranging from 1 to N, t is the continuous time, and C1(m) is the phase coded signal.

4. The sea clutter suppression method based on singular value decomposition of fractional Fourier transform according to claim 2 is characterized in that: The modulated target signal is received by the receiving end to obtain a target echo signal, which is implemented as follows: X(l)=f s S(l)+Z b (l)+Z s (l)l=0,…,N-1 Z b (l)=[z b1 (l),…,z bm (l),…,z bN (l)] Z s (l)=[z s1 (l),…,z sm (l),…,z sN (l)] Where, X(l) represents the target echo signal, f s represents the Doppler vector of the target echo, f d represents the Doppler vector frequency, S(l) is the echo signal of the modulated target signal S(t), and Z b (l) represents the clutter of the lth range unit, Z s (l) represents the noise vector of the lth distance unit, and l represents a distance unit with a value range of 0 to N-1.

5. The sea clutter suppression method based on singular value decomposition of fractional Fourier transform according to claim 1 is characterized in that: The echo signal rearrangement step is implemented as follows: Among them, the M×N-order matrix X is the target echo signal rearranged into a fast and slow time dimension matrix according to the period, and M is the number of pulses.

6. The method for suppressing sea clutter based on singular value decomposition of fractional Fourier transform according to claim 1, characterized in that: The echo signal phase demodulation step is implemented as follows: Wherein, R(t) is the demodulated target echo signal, C2(m) is the demodulated signal, j is a complex number, and μ is the frequency modulation slope.

7. The method for suppressing sea clutter based on singular value decomposition of fractional Fourier transform according to claim 1, characterized in that: The optimal fractional order p is calculated as follows: Among them, B is the signal bandwidth and Fs is the signal sampling frequency.

8. The sea clutter suppression method based on singular value decomposition of fractional Fourier transform according to claim 1 is characterized in that: In the singular value decomposition clutter suppression step, singular value decomposition is performed on the target echo signal transformed back to the time domain to obtain a singular value decomposition result of the target echo signal matrix, which is implemented as follows: Among them, X c To transform the target echo signal back to the time domain, C, T, and N represent the singular value set of the clutter subspace, the singular value set of the target subspace, and the singular value set of the noise subspace, respectively. The superscript () H represents the conjugate transpose of the matrix, u i For X c The i-th left singular vector of i For X c The i-th right singular vector of i is the i-th singular value, and u i >v i >λ i And they are arranged in descending order from largest to smallest.

9. The method for suppressing sea clutter based on singular value decomposition of fractional Fourier transform according to claim 1, characterized in that: In the singular value decomposition clutter suppression step, a signal subspace matrix orthogonal to the clutter subspace is constructed based on the singular value set, including: The vector product of the singular vectors representing the clutter subspace is calculated based on the singular value set, and the calculation formula is as follows: Among them, P c represents the singular vector cross product; A signal subspace matrix orthogonal to the clutter subspace is constructed based on the least squares method according to the singular vector vector product. The calculation formula is as follows: in, is the signal subspace matrix, I represents the identity matrix, P c represents the singular vector cross product.

10. The method for suppressing sea clutter based on singular value decomposition of fractional Fourier transform according to claim 1, characterized in that: In the singular value decomposition clutter suppression step, the target echo signal transformed back to the time domain is projected into the signal subspace matrix to obtain the target echo signal after clutter suppression. The calculation formula is as follows: Among them, X r It indicates the target echo signal after clutter suppression. represents the signal subspace matrix, X c Represents the target echo signal transformed back to the time domain.

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