WO2020037614A1 - 一种提高机载雷达杂波抑制性能的方法及系统 - Google Patents

一种提高机载雷达杂波抑制性能的方法及系统 Download PDF

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WO2020037614A1
WO2020037614A1 PCT/CN2018/101995 CN2018101995W WO2020037614A1 WO 2020037614 A1 WO2020037614 A1 WO 2020037614A1 CN 2018101995 W CN2018101995 W CN 2018101995W WO 2020037614 A1 WO2020037614 A1 WO 2020037614A1
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virtual
space
time snapshot
signal
snapshot signal
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PCT/CN2018/101995
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French (fr)
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阳召成
汪小叶
郑鑫博
黄建军
姜梅
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深圳大学
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/36—Means for anti-jamming, e.g. ECCM, i.e. electronic counter-counter measures

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  • the invention relates to the field of radar signal processing, in particular to a method and a system for improving the clutter suppression performance of an airborne radar.
  • STAP Space-Time Adaptive Processing
  • the main purpose of the present invention is to provide a method and a system for improving the airborne radar clutter suppression performance, which are used to solve the technical problems of slow convergence and high calculation complexity when suppressing airborne radar clutter in the prior art.
  • a first aspect of the present invention provides a method for improving clutter suppression performance of an airborne radar.
  • the method includes:
  • a system for improving clutter suppression performance of an airborne radar includes:
  • a construction module configured to construct a virtual space-time snapshot signal according to the space-time snapshot signal
  • a dimensionality reduction processing module configured to perform dimensionality reduction processing on the virtual space-time snapshot signal according to a dimensionality reduction transformation matrix to obtain a virtual dimensionality reduction space-time snapshot signal;
  • a conversion module configured to obtain a virtual dimension reduction space-time adaptive processing STAP filter according to the covariance matrix of the virtual dimension reduction space-time snapshot signal, so that the virtual dimension reduction STAP filter outputs a clutter-suppressed signal .
  • this method uses a reduced-dimensional transformation matrix to reduce the dimension of the virtual space-time snapshot signal, and the reduced-dimensional transformation matrix reduces the parameter dimensions in the snapshot signal, thereby reducing the computational complexity.
  • the number of independent identically distributed training samples is reduced, and the algorithm's convergence is improved; on the other hand, the technical solution finally obtains a virtual dimensionality reduction STAP filter, which reduces the computational complexity and improves the machine Clutter suppression performance of carrier-borne radars with a small number of samples.
  • FIG. 1 is a schematic flowchart of a method for improving clutter suppression performance of an airborne radar according to an embodiment of the present invention
  • FIG. 2 is a schematic diagram showing the relationship between different reduced-dimensional Doppler channels and the output signal-to-interference and noise ratio;
  • 3 is a schematic diagram showing the relationship between the output signal-to-interference and noise ratio and the number of snapshots
  • 4 is a schematic diagram showing the relationship between the output signal-to-interference and noise ratio and different Doppler frequencies
  • FIG. 5 is a schematic structural diagram of a system for improving clutter suppression performance of an airborne radar according to an embodiment of the present invention
  • FIG. 6 is a schematic structural diagram of a refinement module of an acquisition module
  • FIG. 7 is a structural schematic diagram of a refinement module of a conversion module
  • FIG. 8 is a schematic structural diagram of a computing device according to another embodiment of the present invention.
  • the present invention proposes a method for improving the clutter suppression performance of an airborne radar when the number of samples is small.
  • FIG. 1 is a schematic flowchart of a method for improving clutter suppression performance of an airborne radar according to an embodiment of the present invention.
  • the method includes:
  • Step 101 Obtain a space-time snapshot signal.
  • an echo coprime array space-time snapshot signal of the unit to be measured in the interprime array airborne radar is obtained, and a covariance of the coprime array space-time snapshot signal is obtained according to the coprime array space-time snapshot signal. matrix.
  • the method is specifically used in a coprime array airborne radar.
  • the coprime array airborne radar antenna is a coprime array.
  • the coprime array airborne radar antenna includes N receiving elements and N receiving elements. Consists of two uniform linear arrays, one linear array with 2N 1 sensors, the sensor spacing is N 2 d 0 , and the other linear array with N 2 sensors, the sensor spacing is N 1 d 0 .
  • the radar transmits M pulses in a coherent processing unit.
  • the signal is a space-time snapshot signal. In the case of no target signal, the coprime array space-time snapshot signal x u can be used specifically.
  • the matrix I the NM ⁇ Nc-dimensional ideal space-time snapshot signal steering matrix
  • I the steering vector of the space-time snapshot
  • N c the number of static independent clutter plates in a given range.
  • Vector s [r c, 1 , r c, 2 , ..., r c, Nc ] T is a vector of Nc ⁇ 1-dimensional clutter signals
  • r c, i the complex amplitude of each clutter.
  • n thermal noise.
  • a covariance matrix of the space-time snapshot signal of the coprime array is also obtained.
  • the covariance matrix of the coprime array space-time snapshot signal is obtained according to a covariance matrix acquisition algorithm, and the algorithm for acquiring the covariance matrix is as follows:
  • R is the covariance matrix of the co-prime array space-time snapshot signal x u
  • p [r 2 c, 1 , r 2 c, 2 , ..., r 2 c, Nc ]
  • T is Nc ⁇ 1 dimension
  • p is the diagonal matrix after p diagonalization
  • Is the power of thermal noise
  • I NM is the identity matrix of NM ⁇ NM dimension.
  • the obtained interprime array space-time snapshot signal x u is a space-time snapshot signal without a target signal.
  • the NM ⁇ 1-dimensional space-time snapshot signal x is specific. It can be expressed as:
  • a is the complex amplitude
  • f d is the regularized Doppler frequency
  • f s is the spatial frequency of the target echo signal
  • b (f d ) is a M ⁇ 1-dimensional time-domain steering vector
  • a (f s ) is an N ⁇ 1-dimensional space-oriented vector
  • x u can be expressed as an NM ⁇ 1-dimensional interference signal in the presence of a target signal.
  • Step 102 Construct a virtual space-time snapshot signal according to the space-time snapshot signal.
  • a virtual space-time snapshot signal is constructed in the virtual domain according to the space-time snapshot signal of the interprime array obtained in the previous step.
  • the virtual domain dimension for constructing the virtual space-time snapshot signal is N v M v ⁇ 1 dimension
  • the space-time snapshot signal for the virtual domain is constructed based on the acquired coprime array space-time snapshot signal x u .
  • N v M v ⁇ 1-dimensional virtual space-time snapshot signal is as follows:
  • x u represents a virtual space-time snapshot signal of N v M v ⁇ 1 dimension
  • n v is a thermal noise of a virtual domain
  • a vector s is a vector of a clutter signal
  • Is a steering matrix of a virtual space-time snapshot signal of N v M v ⁇ N c dimension
  • v v (f d , f s ) is a steering vector of a virtual space-time snapshot of N v M v ⁇ 1 dimension.
  • the virtual time-domain steering vector b v (f d, i ) is the same as the actual value b (f d ), Is a time-domain steering vector of M v ⁇ 1 dimension, N v ⁇ 1-dimensional spatial steering vector.
  • the covariance matrix of the virtual space-time snapshot signal can be obtained according to the virtual space-time snapshot signal.
  • the covariance matrix of the virtual space-time snapshot signal is obtained according to the covariance matrix acquisition algorithm of the virtual space-time snapshot signal.
  • the algorithm of obtaining the covariance matrix of the virtual space-time snapshot signal is as follows:
  • R v is the covariance matrix of the virtual space-time snapshot signal x v
  • V v is the steering matrix of the virtual space-time snapshot signal of N v M v ⁇ N c dimension
  • p [r 2 c, 1 , r 2 c, 2 , ..., r 2 c, Nc ]
  • T is a vector of N c ⁇ 1 dimension
  • P is a diagonal matrix after p diagonalization
  • I NvMv is an N v M v ⁇ N v M v dimensional identity matrix.
  • Step 103 Perform a dimensionality reduction process on the virtual space-time snapshot signal according to the dimensionality reduction transformation matrix to obtain a virtual dimensionality reduction space-time snapshot signal.
  • the dimensionality reduction transformation matrix is used in a dimensionality reduction algorithm, and then the virtual space-time snapshot signal is subjected to dimensionality reduction processing using the dimensionality reduction algorithm to obtain a filtered output virtual dimensionality reduction space-time snapshot signal.
  • the algorithm is as follows:
  • x v, r represents mN v ⁇ 1-dimensional virtual dimensionality reduced space-time snapshot signal
  • n v is thermal noise of the virtual domain
  • T r is a signal dimensionality reduction transformation matrix in the virtual domain of MN v ⁇ mN v -dimensional, among them, Is an N v ⁇ N v dimensional identity matrix
  • T t is the dimensionality reduction transformation matrix of the M ⁇ m Doppler domain.
  • n (m-1) / 2
  • m is an odd number
  • m is the number of selected Doppler channels.
  • v v, r (f d , f s ) is a steering vector of mN v ⁇ 1-dimensional virtual dimensionality reduction space-time snapshot.
  • Step 104 Calculate a covariance matrix of the virtual dimensionality-reduced space-time snapshot signal.
  • the covariance matrix of the virtual dimensionality-reduced space-time snapshot signal can be obtained according to the covariance matrix acquisition algorithm of the virtual dimensionality-reduced space-time snapshot signal.
  • the algorithm for acquiring the covariance matrix of the virtual dimensionality-reduced space-time snapshot signal is specific. as follows:
  • R v, r is the covariance matrix of mN v ⁇ mN v dimension, Is the identity matrix of mN v ⁇ mN v dimension, Is the power of thermal noise, T r is the transformation matrix of signal dimensionality reduction, and V v is the steering matrix of virtual dimensionality reduction space-time snapshot signal.
  • Step 105 Obtain a virtual dimension reduction STAP filter according to the covariance matrix of the virtual dimension reduction space-time snapshot signal, so that the virtual dimension reduction STAP filter outputs a signal after clutter suppression.
  • the weight vector of the virtual dimension reduction STAP filter is calculated according to the filter calculation method, and the virtual dimension reduction space-time snapshot signal output by the virtual dimension reduction STAP filter is calculated according to the weight vector, so that the virtual dimension reduction STAP filter is output.
  • Signal with high clutter rejection is calculated.
  • the weight vector of the virtual dimension reduction STAP filter filters the virtual dimension reduction space-time snapshot signal, and can obtain the effective signal in the virtual dimension reduction space-time snapshot signal so that the clutter signal is removed, so that the virtual dimension reduction STAP
  • the filter outputs a virtual dimensionality-reduced space-time snapshot signal after clutter is suppressed.
  • the virtual dimension reduction STAP filter is any one of a virtual dimension reduction sample covariance matrix inversion STAP filter and a virtual dimension reduction principal component analysis STAP filter.
  • the virtual dimension reduction STAP filter is a virtual dimension reduction sample agreement
  • the virtual dimension reduction sample covariance matrix inversion STAP filter filters the virtual dimension reduction space-time snapshot signal according to the virtual dimension reduction sample covariance inversion algorithm;
  • the virtual dimension reduction STAP filter filters the virtual dimension reduction spatial-temporal snapshot signal according to the virtual dimension reduction principal component analysis algorithm.
  • the weight vector of the virtual dimension-reduced sample covariance matrix inversion STAP filter can be obtained.
  • the first acquisition algorithm is as follows:
  • w v, r is the weight vector of mN v ⁇ 1-dimensional reduced-dimensional STAP filter
  • R v, r is the covariance matrix of mN v ⁇ mN v -dimensional
  • v v, r is mN v ⁇ mN v -dimensional Guidance matrix of virtual dimensionality reduction space-time snapshot.
  • the weight vector of the virtual dimensionality-reduced principal component analysis STAP filter can be obtained according to a second acquisition algorithm, where the second acquisition algorithm is specifically as follows:
  • w r, pc is the weight vector of the principal component-based virtual STAP filter of mN v ⁇ 1 dimension
  • r r, pc is the covariance matrix of the principal component-based virtual dimension reduction snapshot based on mN v ⁇ mN v dimension.
  • ⁇ i and u i are respectively p largest eigenvalues in the matrix R v, r and mN v ⁇ 1-dimensional eigenvectors corresponding to the eigenvalues.
  • the weight vectors of two different virtual STAP filters calculated according to two different algorithms are independent, and the virtual dimension reduction sample covariance inversion algorithm and virtual dimension reduction used by the two different virtual STAP filters are independent.
  • the principal component analysis algorithm filters the virtual dimensionality reduced space-time snapshot signal
  • the computational complexity of the two algorithms is the same and both are O ((NM) 3).
  • the virtual dimensionality reduction sample covariance inversion algorithm and the virtual dimensionality reduction principal component analysis algorithm reduce the parameter dimensions to reduce the algorithm complexity. Therefore, the virtual dimensionality reduction sampling matrix inversion sum and the virtual dimensionality reduction principal component algorithm are complicated. The degree is lower than the complexity of the virtual sampling matrix inversion sum and virtual principal component algorithm.
  • the output signal-to-interference and noise ratios of the two dimensionality reduction filters are as follows:
  • SINR is the signal-to-interference and noise ratio of the output of the virtual dimension reduction sample covariance matrix STAP filter
  • SINR pc is the signal to interference and noise ratio of the output of the virtual dimension reduction principal component analysis STAP filter. Is the power of the target echo signal.
  • the virtual STAP filter based on the principal component has better noise suppression performance.
  • Figure 2 is a schematic diagram of the relationship between different dimensionality-reduced Doppler channels and the output signal-to-interference-to-noise ratio.
  • the signal-to-interference and noise-to-noise ratio is the ratio of space-time snapshot signal energy to interference plus noise energy. High, indicating that the noise suppression performance of the signal output by the dimensionality reduction filter is better.
  • the number of samples is set to 300.
  • the output signal-to-interference and noise ratio increases.
  • the leakage of clutter through Doppler side lobes is very poor, and it can hardly be used.
  • the output signal to noise ratio of the five channels is increased compared with the three channels after the dimensionality reduction.
  • the 7-channel signal-to-interference and noise ratio is increased by 3 decibels.
  • the choice of the number of Doppler channels after dimensionality reduction affects the output signal to interference and noise ratio. Considering moderate computing pressure, the number of Doppler channels is selected to be 5.
  • Figure 3 is a schematic diagram of the relationship between the output signal-to-interference and noise ratio and the number of snapshots. Seen from Figure 3 It turns out that the virtual dimension reduction sampling matrix inversion algorithm and virtual dimension reduction principal component analysis algorithm have better output signal-to-interference and noise ratios than the direct sample covariance inversion algorithm and direct principal component analysis algorithm, so the virtual dimension reduction sampling matrix inversion The algorithm and the virtual dimensionality reduction principal component analysis algorithm have better clutter suppression performance than the direct sample covariance inversion algorithm and the direct principal component analysis algorithm.
  • the signal-to-interference and noise ratio output by the virtual dimensionality reduction principal component analysis algorithm is slightly higher than that of the virtual dimensionality reduction sampling matrix inversion algorithm, that is, the clutter suppression output by the virtual dimensionality reduction principal component analysis algorithm
  • the performance is slightly higher than the clutter suppression performance of the virtual dimension reduction sampling matrix inversion algorithm.
  • the number of samples is changed, the number of samples is set to 80, and the number of reduced-dimensional Doppler channels is 5, as shown in FIG. 4, which is a graph of the output signal-to-interference and noise ratio and different Doppler frequencies. Schematic of the relationship, under the same Doppler frequency, the performance of the virtual dimensionality reduction sampling matrix inversion algorithm and virtual dimensionality reduction principal component analysis algorithm is better than the direct principal component analysis algorithm, direct sampling matrix inversion algorithm and virtual sampling matrix. Inversion algorithm.
  • the method uses a dimensionality reduction transformation matrix to perform dimensionality reduction processing on the virtual space-time snapshot signal, and the dimensionality reduction transformation matrix reduces the parameter dimension in the snapshot signal, thereby reducing
  • the computational complexity while reducing the number of independent and identically distributed training samples, improves the convergence of the algorithm; on the other hand, the method finally obtains a virtual dimensionality reduction STAP filter, which can reduce the computational complexity It also improves the clutter suppression performance of the airborne radar when the number of samples is small.
  • FIG. 5 is a schematic structural diagram of a system for improving clutter suppression performance of an airborne radar according to an embodiment of the present invention.
  • the system includes:
  • the obtaining module 201 is configured to obtain a space-time snapshot signal.
  • FIG. 6 is a schematic structural diagram of a refinement module of the acquisition module.
  • the acquisition module 201 includes:
  • a first acquisition module 301 configured to acquire the space-time snapshot signal of an echo coprime array of a distance unit to be measured in a coprime array airborne radar;
  • the second obtaining module 302 is configured to obtain a covariance matrix of the space-time snapshot signal of the coprime array according to the space-time snapshot signal of the coprime array.
  • the system is specifically used in a coprime array airborne radar.
  • the coprime array airborne radar antenna is a coprime array.
  • the coprime array airborne radar antenna includes N receiving elements and N receiving elements. Consists of two uniform linear arrays, one linear array with 2N 1 sensors, the sensor spacing is N 2 d 0 , and the other linear array with N 2 sensors, the sensor spacing is N 1 d 0 .
  • the radar transmits M pulses in a coherent processing unit.
  • the first acquisition module 301 acquires a radar echo signal of a distance unit to be measured.
  • the signal is a space-time snapshot signal.
  • a coprime array space-time snapshot signal xu without a target signal can be specifically expressed by the following formula:
  • N c is the number of static independent clutter plates in a given range.
  • the vector s [r c, 1 , r c, 2 , ..., r c, Nc ] T is the vector of the Nc ⁇ 1-dimensional clutter signal, and r c, i is the complex amplitude of each clutter.
  • n is thermal noise.
  • the second acquisition module 302 obtains the covariance matrix of the space-time snapshot signal of the coprime array.
  • the second obtaining module 302 obtains the covariance matrix of the space-time snapshot signal of the coprime array according to the covariance matrix obtaining algorithm.
  • the algorithm for obtaining the covariance matrix is as follows:
  • R is the covariance matrix of the co-prime array space-time snapshot signal x u
  • p [r 2 c, 1 , r 2 c, 2 , ..., r 2 c, Nc ]
  • T is Nc ⁇ 1 dimension
  • p is the diagonal matrix after p diagonalization
  • Is the power of thermal noise
  • I NM is the identity matrix of NM ⁇ NM dimension.
  • the interprime array space-time snapshot signal x u obtained by the acquisition module 201 is a space-time snapshot signal without a target signal, and in the case of a target signal, the NM ⁇ 1-dimensional space-time snapshot
  • the signal x can be specifically expressed by the following formula:
  • a is the complex amplitude
  • f d is the regularized Doppler frequency
  • f s is the spatial frequency of the target echo signal
  • b (f d ) is a M ⁇ 1-dimensional time-domain steering vector
  • a (f s ) is an N ⁇ 1-dimensional space-oriented vector
  • x u can be expressed as an NM ⁇ 1-dimensional interference signal in the presence of a target signal.
  • a building module 202 is configured to construct a virtual space-time snapshot signal according to the space-time snapshot signal.
  • the construction module 202 constructs a virtual space-time snapshot signal in the virtual domain according to the interprime array space-time snapshot signal obtained by the acquisition module 201.
  • the virtual domain dimension of the constructing module 202 for constructing the virtual space-time snapshot signal is N v M v ⁇ 1 dimension, and the constructing module 202 constructs the virtual domain according to the acquired interprime array space-time snapshot signal x u .
  • the space-time snapshot signal, N v M v ⁇ 1-dimensional virtual space-time snapshot signal is as follows:
  • x u represents a virtual space-time snapshot signal of N v M v ⁇ 1 dimension
  • n v is a thermal noise of a virtual domain
  • a vector s is a vector of a clutter signal
  • Is a steering matrix of a virtual space-time snapshot signal of N v M v ⁇ N c dimension
  • v v (f d , f s ) is a steering vector of a virtual space-time snapshot of N v M v ⁇ 1 dimension.
  • the virtual time-domain steering vector b v (f d, i ) is the same as the actual value b (f d ), Is a time-domain steering vector of M v ⁇ 1 dimension, N v ⁇ 1-dimensional spatial steering vector.
  • the building module 202 further obtains a covariance matrix of the virtual space-time snapshot signal according to the virtual space-time snapshot signal.
  • the building module 202 obtains the covariance matrix of the virtual space-time snapshot signal according to the covariance matrix acquisition algorithm of the virtual space-time snapshot signal.
  • the algorithm for obtaining the covariance matrix of the virtual space-time snapshot signal is as follows:
  • R v is the covariance matrix of the virtual space-time snapshot signal x v
  • V v is the steering matrix of the virtual space-time snapshot signal of N v M v ⁇ N c dimension
  • p [r 2 c, 1 , r 2 c, 2 , ..., r 2 c, Nc ]
  • T is a vector of N c ⁇ 1 dimension
  • P is a diagonal matrix after p diagonalization
  • I NvMv is an N v M v ⁇ N v M v dimensional identity matrix.
  • the dimensionality reduction processing module 203 is configured to perform dimensionality reduction processing on the virtual space-time snapshot signal according to the dimensionality reduction transformation matrix to obtain a virtual dimensionality reduction space-time snapshot signal.
  • the dimensionality reduction processing module 203 uses the dimensionality reduction transformation matrix in the dimensionality reduction algorithm, and then uses the dimensionality reduction algorithm to perform the dimensionality reduction processing on the virtual space-time snapshot signal to obtain a filtered output virtual dimensionality reduction space-time snapshot signal.
  • the dimensionality reduction algorithm is as follows:
  • x v, r represents mN v ⁇ 1-dimensional virtual dimensionality reduced space-time snapshot signal
  • n v is thermal noise of the virtual domain
  • T r is a signal dimensionality reduction transformation matrix in the virtual domain of MN v ⁇ mN v -dimensional, among them, Is an N v ⁇ N v dimensional identity matrix
  • T t is the dimensionality reduction transformation matrix of the M ⁇ m Doppler domain.
  • n (m-1) / 2
  • m is an odd number
  • m is the number of selected Doppler channels.
  • v v, r (f d , f s ) is a steering vector of mN v ⁇ 1-dimensional virtual dimensionality reduction space-time snapshot.
  • the calculation module 204 is configured to calculate a covariance matrix of the virtual dimensionality reduced space-time snapshot signal.
  • the calculation module 204 may obtain a covariance matrix of the virtual dimension-reduced space-time snapshot signal according to a covariance matrix acquisition algorithm of the virtual dimension-reduced space-time snapshot signal, and a covariance matrix of the virtual dimension-reduced space-time snapshot signal.
  • the acquisition algorithm is as follows:
  • R v, r is the covariance matrix of mN v ⁇ mN v dimension, Is the identity matrix of mN v ⁇ mN v dimension, Is the power of thermal noise, T r is the transformation matrix of signal dimensionality reduction, and V v is the steering matrix of virtual dimensionality reduction space-time snapshot signal.
  • the conversion module 205 is configured to obtain a virtual dimension reduction STAP filter according to a covariance matrix of the virtual dimension reduction space-time snapshot signal, so that the virtual dimension reduction STAP filter outputs a signal after clutter suppression.
  • FIG. 7 is a schematic structural diagram of a refinement module of the conversion module.
  • the conversion module 205 includes:
  • a first calculation module 401 configured to calculate a weight vector of a virtual reduced-dimensional STAP filter according to a filter calculation method
  • the second calculation module 402 is configured to calculate a virtual dimension reduction space-time snapshot signal output by the virtual dimension reduction STAP filter according to the weight vector, so that the virtual dimension reduction STAP filter outputs a signal with high clutter suppression performance.
  • the weight vector of the virtual dimension reduction STAP filter filters the virtual dimension reduction space-time snapshot signal, and can obtain the effective signal in the virtual dimension reduction space-time snapshot signal so that the clutter signal is removed, so that the virtual dimension reduction STAP
  • the filter outputs a virtual dimensionality-reduced space-time snapshot signal after clutter is suppressed.
  • the virtual dimension reduction STAP filter is any one of a virtual dimension reduction sample covariance matrix inversion STAP filter and a virtual dimension reduction principal component analysis STAP filter.
  • the virtual dimension reduction STAP filter is a virtual dimension reduction sample covariance
  • matrix inversion STAP filter the virtual dimension reduction sample covariance matrix inversion STAP filter filters the virtual dimension reduction space-time snapshot signal according to the virtual dimension reduction sample covariance inversion algorithm;
  • the virtual dimension reduction STAP filter is When the virtual dimensionality-reduced principal component analysis (STAP) filter is used, the virtual dimensionality-reduced principal component analysis (STAP) filter filters the virtual dimensionality-reduced space-time snapshot signal according to the virtual dimensionality-reduced principal component analysis algorithm.
  • STAP virtual dimensionality-reduced principal component analysis
  • a virtual dimensionality-reduced sample covariance matrix can be used to obtain the weight vector of the inverse STAP filter.
  • the first acquisition algorithm is as follows:
  • w v, r is the weight vector of mN v ⁇ 1-dimensional reduced-dimensional STAP filter
  • R v, r is the covariance matrix of mN v ⁇ mN v -dimensional
  • v v, r is mN v ⁇ mN v -dimensional Guidance matrix of virtual dimensionality reduction space-time snapshot.
  • the weight vector of the virtual dimensionality-reduced principal component analysis STAP filter can be obtained according to a second acquisition algorithm, where the second acquisition algorithm is specifically as follows:
  • w r, pc is the weight vector of the principal component-based virtual STAP filter of mN v ⁇ 1 dimension
  • r r, pc is the covariance matrix of the principal component-based virtual dimension reduction snapshot based on mN v ⁇ mN v dimension.
  • ⁇ i and u i are respectively p largest eigenvalues in the matrix R v, r and mN v ⁇ 1-dimensional eigenvectors corresponding to the eigenvalues.
  • the weight vectors of two different virtual STAP filters calculated according to two different algorithms are independent, and the virtual dimension reduction sample covariance inversion algorithm and virtual dimension reduction used by the two different virtual STAP filters are independent.
  • the principal component analysis algorithm filters the virtual dimensionality reduced space-time snapshot signal
  • the computational complexity of the two algorithms is the same and both are O ((NM) 3).
  • the virtual dimensionality reduction sample covariance inversion algorithm and the virtual dimensionality reduction principal component analysis algorithm reduce the parameter dimensions to reduce the algorithm complexity. Therefore, the virtual dimensionality reduction sampling matrix inversion sum and the virtual dimensionality reduction principal component algorithm are complicated. The degree is lower than the complexity of the virtual sampling matrix inversion sum and virtual principal component algorithm.
  • the output signal-to-interference and noise ratios of the two dimensionality reduction filters are as follows:
  • SINR is the signal-to-interference and noise ratio of the output of the virtual dimension reduction sample covariance matrix STAP filter
  • SINR pc is the signal to interference and noise ratio of the output of the virtual dimension reduction principal component analysis STAP filter. Is the power of the target echo signal.
  • the virtual STAP filter based on the principal component has better noise suppression performance.
  • the system is used in the field of ground clutter suppression of airborne radar on moving platforms, which can improve the level of ground clutter suppression and target detection capability of the radar system.
  • Figure 2 is a schematic diagram of the relationship between different dimensionality-reduced Doppler channels and the output signal-to-interference-to-noise ratio.
  • the signal-to-interference and noise-to-noise ratio is the ratio of space-time snapshot signal energy to interference plus noise energy. High, indicating that the noise suppression performance of the signal output by the dimensionality reduction filter is better.
  • the number of samples is set to 300.
  • the output signal-to-interference and noise ratio increases.
  • the leakage of clutter through Doppler side lobes is very poor, and it can hardly be used.
  • the output signal to noise ratio of the five channels is increased compared with the three channels after the dimensionality reduction.
  • the 7-channel signal-to-interference and noise ratio is increased by 3 decibels.
  • the choice of the number of Doppler channels after dimensionality reduction affects the output signal to interference and noise ratio. Considering the medium-level calculation pressure, the number of Doppler channels is selected to be 5.
  • Figure 3 is a schematic diagram of the relationship between the output signal-to-interference and noise ratio and the number of snapshots. From Figure 3, It can be seen that the virtual dimension reduction sampling matrix inversion algorithm and virtual dimension reduction principal component analysis algorithm have better output signal-to-interference and noise ratios than the direct sample covariance inversion algorithm and direct principal component analysis algorithm. The inverse algorithm and virtual dimensionality reduction principal component analysis algorithm have better clutter suppression performance than the direct sample covariance inversion algorithm and direct principal component analysis algorithm.
  • the signal-to-interference and noise ratio output by the virtual dimensionality reduction principal component analysis algorithm is slightly higher than that of the virtual dimensionality reduction sampling matrix inversion algorithm, that is, the clutter suppression output by the virtual dimensionality reduction principal component analysis algorithm
  • the performance is slightly higher than the clutter suppression performance of the virtual dimension reduction sampling matrix inversion algorithm.
  • the number of samples is changed, the number of samples is set to 80, and the number of reduced-dimensional Doppler channels is 5, as shown in FIG. 4, which is a graph of the output signal-to-interference and noise ratio and different Doppler frequencies. Schematic diagram of the relationship. Under the same Doppler frequency, the performance of the virtual dimensionality reduction sampling matrix inversion algorithm and virtual dimensionality reduction principal component analysis algorithm is better than the direct principal component analysis algorithm, direct sampling matrix inversion algorithm and virtual sampling. Matrix inversion algorithm.
  • the dimensionality reduction processing module of the system uses a dimensionality reduction transformation matrix to perform dimensionality reduction processing on the virtual space-time snapshot signal, and the dimensionality reduction transformation matrix performs parameter dimensions in the snapshot signal.
  • the system finally obtains a virtual reduced-dimensional STAP filter. It can reduce the computational complexity and improve the clutter suppression performance of airborne radar when the number of samples is small.
  • FIG. 8 is a schematic structural diagram of a computing device according to another embodiment of the present invention.
  • the computing device 5 of this embodiment includes a processor 501, a memory 502, and a computer program 503 stored in the memory 502 and executable on the processor 501, for example, to improve airborne radar clutter suppression performance.
  • Method of Procedure When the processor 501 executes the computer program 503, the steps in the embodiment of the method for improving the airborne radar clutter suppression performance are implemented, for example, steps 101 to 105 shown in FIG. 1.
  • the processor 501 executes the computer program 503 the functions of the modules / units in the foregoing device embodiments are realized, such as the acquisition module 201, the construction module 202, the dimensionality reduction processing module 203, the calculation module 204, and the conversion module 205 shown in FIG. Functions.
  • the computer program 503 of the method for improving the clutter suppression performance of an airborne radar mainly includes: acquiring a space-time snapshot signal; constructing a virtual space-time snapshot signal according to the space-time snapshot signal; The time-resolved signal is subjected to dimensionality reduction processing to obtain a virtual reduced-dimensional space-time snapshot signal; a covariance matrix of the virtual-dimensional reduced-space-time snapshot signal is calculated; a virtual reduction is obtained according to the covariance matrix of the virtual-dimensional reduced-space-time snapshot signal Dimensional STAP filter, so that the virtual reduced-dimensional STAP filter outputs a signal after clutter suppression.
  • the computer program 503 may be divided into one or more modules / units, and one or more modules / units are stored in the memory 502 and executed by the processor 501 to complete the present invention.
  • One or more modules / units may be a series of computer program instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of the computer program 503 in the computing device 5.
  • the computer program 503 can be divided into functions of an acquisition module 201, a construction module 202, a dimensionality reduction processing module 203, a calculation module 204, and a conversion module 205 (modules in a virtual device).
  • the specific functions of each module are as follows: the acquisition module 201, It is used to obtain the space-time snapshot signal.
  • the building module 202 is configured to construct a virtual space-time snapshot signal based on the space-time snapshot signal.
  • the dimension reduction processing module 203 is configured to perform a virtual space-time snapshot signal according to the dimensionality reduction transformation matrix. Dimensionality reduction processing to obtain a virtual dimensionality reduction space-time snapshot signal; a calculation module 204 for calculating a covariance matrix of the virtual dimensionality reduction space-time snapshot signal; a conversion module 205 for calculating The covariance matrix obtains a virtual reduced-dimensional STAP filter, so that the virtual reduced-dimensional STAP filter outputs a signal after clutter suppression.
  • the computing device 5 may include, but is not limited to, a processor 501 and a memory 502. Those skilled in the art can understand that FIG. 8 is only an example of the computing device 5 and does not constitute a limitation on the computing device 5. It may include more or less components than shown in the figure, or combine some components, or different components. For example, computing devices may also include input and output devices, network access devices, and buses.
  • the so-called processor 501 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), Ready-made programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.
  • a general-purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
  • the memory 502 may be an internal storage unit of the computing device 5, such as a hard disk or a memory of the computing device 5.
  • the memory 502 may also be an external storage device of the computing device 5, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, and a flash memory card (Flash) provided on the computing device 5. Card) and so on.
  • the memory 502 may also include both an internal storage unit of the computing device 5 and an external storage device.
  • the memory 502 is used to store computer programs and other programs and data required by the computing device.
  • the memory 502 may also be used to temporarily store data that has been output or is to be output.
  • the disclosed apparatus / computing device and method may be implemented in other ways.
  • the device / computing device embodiments described above are only schematic.
  • the division of modules or units is only a logical function division.
  • the displayed or discussed mutual coupling or direct coupling or communication connection may be indirect coupling or communication connection through some interfaces, devices or units, which may be electrical, mechanical or other forms.
  • the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, which may be located in one place, or may be distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the objective of the solution of this embodiment.
  • each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist separately physically, or two or more units may be integrated into one unit.
  • the above integrated unit may be implemented in the form of hardware or in the form of software functional unit.
  • integrated modules / units When integrated modules / units are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the method of the above embodiment, and can also be completed by a computer program instructing related hardware.
  • the computer program of the method for improving the airborne radar clutter suppression performance can be stored in a computer In a readable storage medium, when the computer program is executed by a processor, the steps of the foregoing method embodiments can be implemented, that is, acquiring a space-time snapshot signal; constructing a virtual space-time snapshot signal according to the space-time snapshot signal; The dimensionality reduction transformation matrix performs dimensionality reduction processing on the virtual space-time snapshot signal to obtain a virtual dimensionality reduction space-time snapshot signal; calculates a covariance matrix of the virtual dimensionality reduction space-time snapshot signal; according to the virtual dimensionality reduction space-time snapshot signal To obtain a virtual reduced-dimensional STAP filter so that the virtual reduced-dimensional STAP filter outputs a clutter-suppressed signal.
  • the computer program includes computer program code, and the computer program code may be in a source code form, an object code form, an executable file, or some intermediate form.
  • the computer-readable medium may include: any entity or device capable of carrying computer program code, a recording medium, a U disk, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM, Read-Only Memory), and random access Memory (RAM, Random Access Memory), electric carrier signals, telecommunication signals, and software distribution media. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of the legislation and patent practice in the jurisdiction.
  • the computer-readable medium does not include Electric carrier signals and telecommunication signals.
  • the above embodiments are only used to illustrate the technical solutions of the present invention, but not limited to them.
  • the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still apply the foregoing embodiments. Modifications to the recorded technical solutions, or equivalent replacements of some of the technical features thereof; and these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the present invention Within the scope of protection.

Abstract

一种提高机载雷达杂波抑制性能的方法及系统，方法包括：获取空时快拍信号（101），根据空时快拍信号构造虚拟空时快拍信号（102），根据降维转换矩阵对该虚拟空时快拍信号进行降维处理，得到虚拟降维空时快拍信号（103），计算虚拟降维空时快拍信号的协方差矩阵（104），根据虚拟降维空时快拍信号的协方差矩阵获取虚拟降维STAP滤波器，以使虚拟降维STAP滤波器输出杂波抑制后的信号（105）。方法使用降维转换矩阵将虚拟空时快拍信号进行降维处理，降维转换矩阵将空时快拍信号中的参数维度进行下降，从而降低了计算复杂度，同时降低了独立同分布训练样本个数，提高了算法收敛性。

Description

一种提高机载雷达杂波抑制性能的方法及系统 技术领域
本发明涉及雷达信号处理领域，尤其涉及一种提高机载雷达杂波抑制性能的方法及系统。
背景技术
空时自适应处理(Space-Time Adaptive Processing，STAP)是机载雷达杂波抑制的关键技术，在提高机载杂波抑制质量过程中，关键在于提高空时自适应处理效率。
传统的空时自适应处理技术均是利用在均匀的线性阵列机载雷达中，但是均匀线性阵列情形下，通过增加阵元数实现雷达系统的高分辨率与准确率，需大量的硬件支持和较高的成本，而稀疏阵列通过稀疏配置阵元可实现较大的孔径而不用增加额外的阵元，即稀疏阵列不用增加相应的成本可实现较好的性能。因此，稀疏阵列在雷达中的运用也越来越广泛，然而稀疏阵列的欠采样特性又会导致基于采样矩阵求逆算法用于机载雷达时杂波抑制性能严重下降。
为了解决稀疏阵列的欠采样问题，现有技术中采用基于差集的相关算法，如阵列-脉冲联合互质的角度-多普勒估计算法，嵌套阵列的全维空时自适应处理算法，最小冗余阵列空时自适应处理算法，基于杂波频谱稀疏性的空时自适应处理算法等。但上述算法中存在收敛缓慢和计算复杂度高的技术问题。
技术问题
本发明的主要目的在于提供一种提高机载雷达杂波抑制性能的方法及系统，用于解决现有技术在抑制机载雷达杂波时存在收敛慢和计算复杂度高的技术问题。
技术解决方案
本发明第一方面提供一种提高机载雷达杂波抑制性能的方法，所述方法包括：
获取空时快拍信号；
根据所述空时快拍信号构造虚拟空时快拍信号；
根据降维转换矩阵对所述虚拟空时快拍信号进行降维处理，得到虚拟降维空时快拍信号；
计算所述虚拟降维空时快拍信号的协方差矩阵；根据所述虚拟降维空时快拍信号的协方差矩阵获取虚拟降维空时自适应处理STAP滤波器，以使所述虚拟降维STAP滤波器输出杂波抑制后的信号。
本发明第二方面提供一种提高机载雷达杂波抑制性能的系统，所述系统包括：
获取模块，用于获取空时快拍信号；
构建模块，用于根据所述空时快拍信号构造虚拟空时快拍信号；
降维处理模块，用于根据降维转换矩阵对所述虚拟空时快拍信号进行降维处理，得到虚拟降维空时快拍信号；
计算模块，用于计算所述虚拟降维空时快拍信号的协方差矩阵；
转换模块，用于根据所述虚拟降维空时快拍信号的协方差矩阵获取虚拟降维空时自适应处理 STAP滤波器，以使所述虚拟降维STAP滤波器输出杂波抑制后的信号。
有益效果
从上述技术方案可知，一方面，该方法使用降维转换矩阵将虚拟空时快拍信号进行降维处理，降维转换矩阵将快拍信号中的参数维度进行下降，从而降低了计算复杂度，同时降低了独立同分布训练样本个数，提高了算法收敛性；另一方面，该技术方案最后获取到虚拟降维STAP滤波器，该虚拟降维STAP滤波器降低计算复杂度，也提高了机载雷达在样本数较少情况下的杂波的抑制性能。
附图说明
图1为本发明实施例提供的一种提高机载雷达杂波抑制性能的方法的流程示意图；
图2为不同降维多普勒通道与输出信干噪比的关系示意图；
图3为输出信干噪比与快拍数的关系示意图；
图4为输出信干噪比与不同多普勒频率的关系示意图；
图5为本发明实施例提供的一种提高机载雷达杂波抑制性能的系统的结构示意图；
图6为获取模块的细化模块的结构示意图；
图7为转换模块的细化模块的结构示意图；
图8为本发明另一实施例提供的计算设备的结构示意图。
本发明的实施方式
为使得本发明的发明目的、特征、优点能够更加的明显和易懂，下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本发明一部分实施例，而非全部实施例。基于本发明中的实施例，本领域技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。
由于现有技术中在抑制机载雷达杂波时存在收敛慢、计算成本高和计算复杂度高的技术问题。为了解决上述技术问题，本发明提出一种在样本数较少情况下提高机载雷达杂波抑制性能的方法。
请参阅图1，为本发明实施例提供的一种提高机载雷达杂波抑制性能的方法的流程示意图，该方法包括：
步骤101、获取空时快拍信号。
具体地，获取互质阵列机载雷达中的待测距离单元的回波互质阵列空时快拍信号，根据该互质阵列空时快拍信号获取互质阵列空时快拍信号的协方差矩阵。
在本发明实施例中，该方法具体用在互质阵列机载雷达中，互质阵列机载雷达天线为互质阵列，互质阵列机载雷达天线包括N个接收阵元，N个接收元由两个均匀线性阵列组成，一个线性阵列带有2N 1传感器，传感器间距为N 2d 0，另一个线性阵列带有N 2传感器，传感器间距为N 1d 0。该雷达在一个相干处理单元内发射M个脉冲。在M个脉冲内雷达获取的某个待测距离单元的雷达回波信号，该信号为空时快拍信号，在无目标信号情况下的互质阵列空时快拍信号x u具体可以用下式表示：
Figure PCTCN2018101995-appb-000001
其中，矩阵
Figure PCTCN2018101995-appb-000002
是NM×Nc维理想的空时快拍信号的导 向矩阵，
Figure PCTCN2018101995-appb-000003
为空时快拍的导向矢量，N c为给定范围的静态独立杂波片个数。向量s＝[r c,1,r c,2,...,r c,Nc] T是Nc×1维的杂波信号的矢量，r c,i是每一个杂波片的复幅度，n为热噪声。
在获取了互质阵列空时快拍信号之后，还获取该互质阵列空时快拍信号的协方差矩阵。
具体地，根据协方差矩阵获取算法获取该互质阵列空时快拍信号的协方差矩阵，该协方差矩阵获取算法具体如下：
Figure PCTCN2018101995-appb-000004
其中，R为互质阵列空时快拍信号x u的协方差矩阵，p＝[r 2 c,1,r 2 c,2,...,r 2 c,Nc] T是Nc×1维的向量，P为p对角化后的对角矩阵，
Figure PCTCN2018101995-appb-000005
是热噪声的功率，I NM是NM×NM维的单位矩阵。
需要说明的是，获取的互质阵列空时快拍信号x u是在无目标信号情况下的空时快拍信号，在有目标信号情况下，NM×1维的空时快拍信号x具体可以用下式表示：
x＝av(f d,f s)+x u
其中，a是复幅度，f d为正则化多普勒频率，f s为目标回波信号空间频率，NM×1维向量
Figure PCTCN2018101995-appb-000006
为空时快拍导向矢量，b(f d)为M×1维的时域导向矢量，a(f s)为N×1维的空域导向矢量，其中，b(f d)和a(f s)可用下式表示：
Figure PCTCN2018101995-appb-000007
此时，x u可以表示为有目标信号情况下的NM×1维的干扰信号。
步骤102、根据空时快拍信号构造虚拟空时快拍信号。
具体地，根据上一步骤获取的互质阵列空时快拍信号在虚拟域构建虚拟空时快拍信号。
在本发明实施例中，构建虚拟空时快拍信号的虚拟域维数为N vM v×1维，根据获取的互质阵列空时快拍信号x u构建虚拟域的空时快拍信号，N vM v×1维虚拟空时快拍信号具体如下式：
x v＝V vs+n v
其中，x u表示N vM v×1维的虚拟空时快拍信号，n v是虚拟域的热噪声，向量s是杂波信号的矢量，
Figure PCTCN2018101995-appb-000008
是N vM v×N c维的虚拟空时快拍信号的导向矩阵，v v(f d,f s)是N vM v×1维的虚拟空时快拍的导向矢量。
其中，
Figure PCTCN2018101995-appb-000009
需要说明的是，对于固定的脉冲重复周期，虚拟时域导向矢量b v(f d,i)与实际值b(f d)相同，
Figure PCTCN2018101995-appb-000010
是M v×1维的时域导向矢量，
Figure PCTCN2018101995-appb-000011
为N v×1维的空域导向矢量。
进一步地，在获取了虚拟空时快拍信号后，可根据虚拟空时快拍信号获取该虚拟空时快拍信号的协方差矩阵。
具体地，根据虚拟空时快拍信号的协方差矩阵获取算法获取该虚拟空时快拍信号的协方差矩阵，该虚拟空时快拍信号的协方差矩阵获取算法具体如下：
Figure PCTCN2018101995-appb-000012
其中，R v是虚拟空时快拍信号x v的协方差矩阵，V v是N vM v×N c维的虚拟空时快拍信号的导向矩阵，p＝[r 2 c,1,r 2 c,2,...,r 2 c,Nc] T是N c×1维的向量，P为p对角化后的对角矩阵，
Figure PCTCN2018101995-appb-000013
是热噪声的功率，I NvMv是N vM v×N vM v维单位矩阵。
步骤103、根据降维转换矩阵对虚拟空时快拍信号进行降维处理，得到虚拟降维空时快拍信号。
具体地，将降维转换矩阵用于降维算法中，再利用降维算法将虚拟空时快拍信号进行降维处理，得到滤波输出后的虚拟降维空时快拍信号，其中，降维算法具体如下所示：
Figure PCTCN2018101995-appb-000014
其中，x v,r表示mN v×1维的虚拟降维空时快拍信号，n v是虚拟域的热噪声，T r是MN v×mN v维的虚拟域内的信号降维转换矩阵，其中，
Figure PCTCN2018101995-appb-000015
Figure PCTCN2018101995-appb-000016
是一个N v×N v维的单位矩阵，T t是M×m的多普勒域的降维转换矩阵，T t具体如下：
Figure PCTCN2018101995-appb-000017
其中，n＝(m-1)/2，m为奇数，且m是选中的多普勒通道的数量。
因此，得到的降维目标导向矢量如下所示：
Figure PCTCN2018101995-appb-000018
其中，v v,r(f d,f s)为mN v×1维的虚拟降维空时快拍的导向矢量。
步骤104、计算虚拟降维空时快拍信号的协方差矩阵。
具体地，可根据虚拟降维空时快拍信号的协方差矩阵获取算法获取该虚拟降维空时快拍信号的协方差矩阵，该虚拟降维空时快拍信号的协方差矩阵获取算法具体如下：
Figure PCTCN2018101995-appb-000019
其中，R v,r为mN v×mN v维的协方差矩阵，
Figure PCTCN2018101995-appb-000020
是mN v×mN v维的单位矩阵，
Figure PCTCN2018101995-appb-000021
是热噪声的功率，T r是信号降维的转换矩阵，V v是虚拟降维空时快拍信号的导向矩阵。
步骤105、根据该虚拟降维空时快拍信号的协方差矩阵获取虚拟降维STAP滤波器，以使该虚拟降维STAP滤波器输出杂波抑制后的信号。
具体地，根据滤波器计算方法计算虚拟降维STAP滤波器的权矢量，根据该权矢量计算虚拟降维STAP滤波器输出的虚拟降维空时快拍信号，以使虚拟降维STAP滤波器输出高杂波抑制性能的信号。
其中，虚拟降维STAP滤波器的权矢量对虚拟降维空时快拍信号进行滤波，可获取虚拟降维空时快拍信号中的有效信号从而使得杂波信号被除去，使得虚拟降维STAP滤波器输出杂波被抑制后的虚拟降维空时快拍信号。
进一步地，虚拟降维STAP滤波器为虚拟降维样本协方差矩阵求逆STAP滤波器和虚拟降维主分量分析STAP滤波器中的任意一个，当虚拟降维STAP滤波器为虚拟降维样本协方差矩阵求逆STAP滤波器时，虚拟降维样本协方差矩阵求逆STAP滤波器根据虚拟降维样本协方差求逆算法对虚拟降维空时快拍信号进行滤波；当虚拟降维STAP滤波器为虚拟降维主分量分析STAP滤波器时，虚拟降维主分量分析STAP滤波器根据虚拟降维主分量分析算法对虚拟降维空时快拍信号进行滤波。
具体地，可选的滤波器计算方法有两种，根据第一获取算法可获取虚拟降维样本协方差矩阵求逆STAP滤波器的权矢量，第一获取算法具体如下：
Figure PCTCN2018101995-appb-000022
其中，w v,r为mN v×1维的降维STAP滤波器的权矢量，R v,r为mN v×mN v维的协方差矩阵，v v,r为mN v×mN v维的虚拟降维空时快拍的导向矩阵。
根据第二获取算法可获取虚拟降维主分量分析STAP滤波器的权矢量，其中，第二获取算法具体如下：
Figure PCTCN2018101995-appb-000023
其中，w r,pc为mN v×1维的基于主分量的虚拟STAP滤波器的权矢量，R r,pc为mN v×mN v维的基于主分量的虚拟降维快拍的协方差矩阵，具体大小如下：
Figure PCTCN2018101995-appb-000024
其中，λ i和u i，分别是矩阵R v,r中p个最大的特征值和与特征值对应的mN v×1维的特征向量。
需要说明的是，根据两个不同的算法计算得到两个不同虚拟STAP滤波器的权矢量是独立的，两个不同虚拟STAP滤波器分别使用的虚拟降维样本协方差求逆算法和虚拟降维主分量分析算法对虚拟降维空时快拍信号进行滤波时，两个算法的计算复杂度是相同且均为O((NM)3)。虚拟降维样本协方差求逆算法和虚拟降维主分量分析算法对参数维度进行降维，降低了算法复杂度，因此虚拟降维采样矩阵求逆和和虚拟降维主成分两个算法的复杂度低于虚拟采样矩阵求逆和和虚拟主成分两个算法的复杂度。
在本发明实施例中，两个降维滤波器的输出信干噪比具体如下：
Figure PCTCN2018101995-appb-000025
Figure PCTCN2018101995-appb-000026
其中，SINR为虚拟降维样本协方差矩阵求逆STAP滤波器输出信干噪比，SINR pc为虚拟降维主分量分析STAP滤波器的输出信干噪比，
Figure PCTCN2018101995-appb-000027
是目标回波信号的功率。
需要说明的是，在低样本下基于主分量的虚拟STAP滤波器有更好的抑制杂波性能。
将该系统用于运动平台机载雷达地面杂波抑制领域，可提高雷达系统地面杂波抑制水平与目标检测能力。
如图2所示，图2为不同降维多普勒通道与输出信干噪比的关系示意图，信干噪比为空时快拍信号能量与干扰加噪声能量的比值，信干噪比越高，说明降维滤波器输出的信号的杂波抑制性能越好。
在本发明实施例中，设置样本数为300，从图2中看出，随着通道个数的增加，输出的信干噪比也在增加，虚拟降维采样矩阵求逆算法因主波束杂波通过多普勒旁瓣发生泄露杂波滤除性能非常差，几乎不能使用，在单个多普勒通道的情况下与降维后的三个通道相比，五通道的输出信干噪比增加了2分贝，七通道信干噪比增加3分贝,降维后的多普勒通道个数选择影响输出信干噪比。在中等程度计算压力下考虑，选择多普勒通道个数为5。
设置目标正则化多普勒频率为0.25，快拍数的取值范围为50到5000，如图3所示，图3为输出信干噪比与快拍数的关系示意图，从图3中看出，虚拟降维采样矩阵求逆算法和虚拟降维主分量分析算法具有比直接样本协方差求逆算法和直接主分量分析算法更好的输出信干噪比，因此虚拟降维采样矩阵求逆算法和虚拟降维主分量分析算法具有比直接样本协方差求逆算法和直接主分量分析算法更好的杂波抑制性能。另，虽虚拟降维主分量分析算法的输出信干噪比只比虚拟主分量分析算法的输出信干噪比小1.5分贝，但将输出信干噪比提高1.5分贝会造成大量的计算量。
同时也可以看出，虚拟降维主分量分析算法输出的信干噪比稍高于虚拟降维采样矩阵求逆算法输出的信干噪比，即虚拟降维主分量分析算法输出的杂波抑制性能稍高于虚拟降维采样矩阵求逆算法杂波抑制性能。
在本实施例中，改变样本数量，将样本数量设置为80，降维多普勒通道的个数为5,如图4所示，图4为输出信干噪比与不同多普勒频率的关系示意图，在同一多普勒频率的条件下，虚拟降维采样矩阵求逆算法和虚拟降维主分量分析算法的性能优于直接主分量分析算法、直接采样矩阵求逆算法和虚拟采样矩阵求逆算法。
从本发明实施例提供的方法可知，一方面，该方法使用降维转换矩阵将虚拟空时快拍信号进行降维处理，降维转换矩阵将快拍信号中的参数维度进行下降，从而降低了计算复杂度，同时降低了独立同分布训练样本个数，提高了算法收敛性；另一方面，该方法最后获取到虚拟降维STAP滤波器，该虚拟降维STAP滤波器的可降低计算复杂度，也提高了机载雷达在样本数较少情况下的杂波抑制性能。
请参阅图5，图5为本发明实施例提供的一种提高机载雷达杂波抑制性能的系统的结构示意图，该系统包括：
获取模块201，用于获取空时快拍信号。
其中，如图6所示，图6为获取模块的细化模块的结构示意图，获取模块201包括：
第一获取模块301，用于获取互质阵列机载雷达中的待测距离单元的回波互质阵列空时快拍信号；
第二获取模块302，用于根据该互质阵列空时快拍信号获取互质阵列空时快拍信号的协方差矩阵。
在本发明实施例中，该系统具体用在互质阵列机载雷达中，互质阵列机载雷达天线为互质阵列，互质阵列机载雷达天线包括N个接收阵元，N个接收元由两个均匀线性阵列组成，一个线性阵列带有2N 1传感器，传感器间距为N 2d 0，另一个线性阵列带有N 2传感器，传感器间距为N 1d 0。该雷达在一个相干处理单元内发射M个脉冲。第一获取模块301获取某个待测距离单元的雷达回波信号，该信号为空时快拍信号，在无目标信号情况下的互质阵列空时快拍信号xu具体可以用下式表示：
Figure PCTCN2018101995-appb-000028
向矩阵，
Figure PCTCN2018101995-appb-000029
为空时快拍的导向矢量，N c为给定范围的静态独立杂波片个数。向量s＝[r c,1,r c,2,...,r c,Nc] T是Nc×1维的杂波信号的矢量，r c,i是每一个杂波片的复幅度，n为热噪声。
在第一获取模块301获取了互质阵列空时快拍信号之后，第二获取模块302获取该互质阵列空时快拍信号的协方差矩阵。
具体地，第二获取模块302根据协方差矩阵获取算法获取该互质阵列空时快拍信号的协方差矩 阵，该协方差矩阵获取算法具体如下：
Figure PCTCN2018101995-appb-000030
其中，R为互质阵列空时快拍信号x u的协方差矩阵，p＝[r 2 c,1,r 2 c,2,...,r 2 c,Nc] T是Nc×1维的向量，P为p对角化后的对角矩阵，
Figure PCTCN2018101995-appb-000031
是热噪声的功率，I NM是NM×NM维的单位矩阵。
需要说明的是，获取模块201获取的互质阵列空时快拍信号x u是在无目标信号情况下的空时快拍信号，在有目标信号情况下，NM×1维的空时快拍信号x具体可以用下式表示：
x＝av(f d,f s)+x u
其中，a是复幅度，f d为正则化多普勒频率，f s为目标回波信号空间频率，NM×1维向量
Figure PCTCN2018101995-appb-000032
为空时快拍导向矢量，b(f d)为M×1维的时域导向矢量，a(f s)为N×1维的空域导向矢量，其中，b(f d)和a(f s)可用下式表示：
Figure PCTCN2018101995-appb-000033
此时，x u可以表示为有目标信号情况下的NM×1维的干扰信号。
构建模块202，用于根据空时快拍信号构造虚拟空时快拍信号。
具体地，构建模块202根据获取模块201获取的互质阵列空时快拍信号在虚拟域构建虚拟空时快拍信号。
在本发明实施例中，构建模块202构建虚拟空时快拍信号的虚拟域维数为N vM v×1维，构建模块202根据获取的互质阵列空时快拍信号x u构建虚拟域的空时快拍信号，N vM v×1维虚拟空时快拍信号具体如下式：
x v＝V vs+n v
其中，x u表示N vM v×1维的虚拟空时快拍信号，n v是虚拟域的热噪声，向量s是杂波信号的矢量，
Figure PCTCN2018101995-appb-000034
是N vM v×N c维的虚拟空时快拍信号的导向矩阵，v v(f d,f s)是N vM v×1维的虚拟空时快拍的导向矢量。
其中，
Figure PCTCN2018101995-appb-000035
需要说明的是，对于固定的脉冲重复周期，虚拟时域导向矢量b v(f d,i)与实际值b(f d)相同，
Figure PCTCN2018101995-appb-000036
是M v×1维的时域导向矢量，
Figure PCTCN2018101995-appb-000037
为N v×1维的空域导向矢量。
进一步地，构建模块202在获取了虚拟空时快拍信号后，构建模块202还根据虚拟空时快拍信号获取该虚拟空时快拍信号的协方差矩阵。
具体地，构建模块202根据虚拟空时快拍信号的协方差矩阵获取算法获取该虚拟空时快拍信号的协方差矩阵，该虚拟空时快拍信号的协方差矩阵获取算法具体如下：
Figure PCTCN2018101995-appb-000038
其中，R v是虚拟空时快拍信号x v的协方差矩阵，V v是N vM v×N c维的虚拟空时快拍信号的导向矩阵，p＝[r 2 c,1,r 2 c,2,...,r 2 c,Nc] T是N c×1维的向量，P为p对角化后的对角矩阵，
Figure PCTCN2018101995-appb-000039
是热噪声的功率，I NvMv是N vM v×N vM v维单位矩阵。
降维处理模块203，用于根据降维转换矩阵对虚拟空时快拍信号进行降维处理，得到虚拟降维空 时快拍信号。
具体地，降维处理模块203将降维转换矩阵用于降维算法中，再利用降维算法将虚拟空时快拍信号进行降维处理，得到滤波输出后的虚拟降维空时快拍信号，其中，降维算法具体如下所示：
Figure PCTCN2018101995-appb-000040
其中，x v,r表示mN v×1维的虚拟降维空时快拍信号，n v是虚拟域的热噪声，T r是MN v×mN v维的虚拟域内的信号降维转换矩阵，其中，
Figure PCTCN2018101995-appb-000041
Figure PCTCN2018101995-appb-000042
是一个N v×N v维的单位矩阵，T t是M×m的多普勒域的降维转换矩阵，T t具体如下：
Figure PCTCN2018101995-appb-000043
其中，n＝(m-1)/2，m为奇数，且m是选中的多普勒通道的数量。
因此，得到的降维目标导向矢量如下所示：
Figure PCTCN2018101995-appb-000044
其中，v v,r(f d,f s)为mN v×1维的虚拟降维空时快拍的导向矢量。
计算模块204，用于计算虚拟降维空时快拍信号的协方差矩阵。
具体地，计算模块204可根据虚拟降维空时快拍信号的协方差矩阵获取算法获取该虚拟降维空时快拍信号的协方差矩阵，该虚拟降维空时快拍信号的协方差矩阵获取算法具体如下：
Figure PCTCN2018101995-appb-000045
其中，R v,r为mN v×mN v维的协方差矩阵，
Figure PCTCN2018101995-appb-000046
是mN v×mN v维的单位矩阵，
Figure PCTCN2018101995-appb-000047
是热噪声的功率，T r是信号降维的转换矩阵，V v是虚拟降维空时快拍信号的导向矩阵。
转换模块205，用于根据虚拟降维空时快拍信号的协方差矩阵获取虚拟降维STAP滤波器，以使该虚拟降维STAP滤波器输出杂波抑制后的信号。
其中，如图7所示，图7为转换模块的细化模块的结构示意图，转换模块205包括：
第一计算模块401，用于根据滤波器计算方法计算虚拟降维STAP滤波器的权矢量；
第二计算模块402，用于根据该权矢量计算虚拟降维STAP滤波器输出的虚拟降维空时快拍信号，以使虚拟降维STAP滤波器输出高杂波抑制性能的信号。
其中，虚拟降维STAP滤波器的权矢量对虚拟降维空时快拍信号进行滤波，可获取虚拟降维空时快拍信号中的有效信号从而使得杂波信号被除去，使得虚拟降维STAP滤波器输出杂波被抑制后的虚拟降维空时快拍信号。
其中，虚拟降维STAP滤波器为虚拟降维样本协方差矩阵求逆STAP滤波器和虚拟降维主分量分析STAP滤波器中的任意一个，当虚拟降维STAP滤波器为虚拟降维样本协方差矩阵求逆STAP滤波器时，虚拟降维样本协方差矩阵求逆STAP滤波器根据虚拟降维样本协方差求逆算法对虚拟降维空时快拍信号进行滤波；当虚拟降维STAP滤波器为虚拟降维主分量分析STAP滤波器时，虚拟降维主分量分析STAP滤波器根据虚拟降维主分量分析算法对虚拟降维空时快拍信号进行滤波。
具体地，可选的滤波器计算方法有两种，根据第一获取算法可获取虚拟降维样本协方差矩阵求 逆STAP滤波器的权矢量，第一获取算法具体如下：
Figure PCTCN2018101995-appb-000048
其中，w v,r为mN v×1维的降维STAP滤波器的权矢量，R v,r为mN v×mN v维的协方差矩阵，v v,r为mN v×mN v维的虚拟降维空时快拍的导向矩阵。
根据第二获取算法可获取虚拟降维主分量分析STAP滤波器的权矢量，其中，第二获取算法具体如下：
Figure PCTCN2018101995-appb-000049
其中，w r,pc为mN v×1维的基于主分量的虚拟STAP滤波器的权矢量，R r,pc为mN v×mN v维的基于主分量的虚拟降维快拍的协方差矩阵，具体大小如下：
Figure PCTCN2018101995-appb-000050
其中，λ i和u i，分别是矩阵R v,r中p个最大的特征值和与特征值对应的mN v×1维的特征向量。
需要说明的是，根据两个不同的算法计算得到两个不同虚拟STAP滤波器的权矢量是独立的，两个不同虚拟STAP滤波器分别使用的虚拟降维样本协方差求逆算法和虚拟降维主分量分析算法对虚拟降维空时快拍信号进行滤波时，两个算法的计算复杂度是相同且均为O((NM)3)。虚拟降维样本协方差求逆算法和虚拟降维主分量分析算法对参数维度进行降维，降低了算法复杂度，因此虚拟降维采样矩阵求逆和和虚拟降维主成分两个算法的复杂度低于虚拟采样矩阵求逆和和虚拟主成分两个算法的复杂度。
在本发明实施例中，两个降维滤波器的输出信干噪比具体如下：
Figure PCTCN2018101995-appb-000051
Figure PCTCN2018101995-appb-000052
其中，SINR为虚拟降维样本协方差矩阵求逆STAP滤波器输出信干噪比，SINR pc为虚拟降维主分量分析STAP滤波器的输出信干噪比，
Figure PCTCN2018101995-appb-000053
是目标回波信号的功率。
需要说明的是，在低样本下基于主分量的虚拟STAP滤波器有更好的抑制杂波性能。
其中，将该系统用于运动平台机载雷达地面杂波抑制领域，可提高雷达系统地面杂波抑制水平与目标检测能力。
如图2所示，图2为不同降维多普勒通道与输出信干噪比的关系示意图，信干噪比为空时快拍信号能量与干扰加噪声能量的比值，信干噪比越高，说明降维滤波器输出的信号的杂波抑制性能越好。
在本发明实施例中，设置样本数为300，从图2中看出，随着通道个数的增加，输出的信干噪比也在增加，虚拟降维采样矩阵求逆算法因主波束杂波通过多普勒旁瓣发生泄露杂波滤除性能非常差，几乎不能使用，在单个多普勒通道的情况下与降维后的三个通道相比，五通道的输出信干噪比增加了2分贝，七通道信干噪比增加3分贝,降维后的多普勒通道个数选择影响输出信干噪比。在中等程 度计算压力下考虑，选择多普勒通道个数为5。
设置目标正则化多普勒频率为0.25，快拍数的取值范围为50到5000，如图3所示，图3为输出信干噪比与快拍数的关系的示意图，从图3中看出，虚拟降维采样矩阵求逆算法和虚拟降维主分量分析算法具有比直接样本协方差求逆算法和直接主分量分析算法更好的输出信干噪比，因此虚拟降维采样矩阵求逆算法和虚拟降维主分量分析算法具有比直接样本协方差求逆算法和直接主分量分析算法更好的杂波抑制性能。另，虽虚拟降维主分量分析算法的输出信干噪比只比虚拟主分量分析算法的输出信干噪比小1.5分贝，但将输出信干噪比提高1.5分贝会造成大量的计算量。
同时也可以看出，虚拟降维主分量分析算法输出的信干噪比稍高于虚拟降维采样矩阵求逆算法输出的信干噪比，即虚拟降维主分量分析算法输出的杂波抑制性能稍高于虚拟降维采样矩阵求逆算法杂波抑制性能。
在本实施例中，改变样本数量，将样本数量设置为80，降维多普勒通道的个数为5,如图4所示，图4为输出信干噪比与不同多普勒频率的关系的示意图，在同一多普勒频率的条件下，虚拟降维采样矩阵求逆算法和虚拟降维主分量分析算法的性能优于直接主分量分析算法、直接采样矩阵求逆算法和虚拟采样矩阵求逆算法。
从本发明实施例提供的系统可知，一方面，该系统的降维处理模块使用降维转换矩阵将虚拟空时快拍信号进行降维处理，降维转换矩阵将快拍信号中的参数维度进行下降，从而降低了计算复杂度，同时降低了独立同分布训练样本个数，提高了算法收敛性；另一方面，该系统最后获取到虚拟降维STAP滤波器，该虚拟降维STAP滤波器的可降低计算复杂度，也提高了机载雷达在样本数较少情况下的杂波的抑制性能。
图8为本发明另一实施例提供的计算设备的结构示意图。如图8所示，该实施例的计算设备5包括：处理器501、存储器502以及存储在存储器502中并可在处理器501上运行的计算机程序503，例如提高机载雷达杂波抑制性能的方法的程序。处理器501执行计算机程序503时实现上述提高机载雷达杂波抑制性能的方法实施例中的步骤，例如图1所示的步骤101至步骤105。或者，处理器501执行计算机程序503时实现上述各装置实施例中各模块/单元的功能，例如图5所示获取模块201、构建模块202、降维处理模块203、计算模块204和转换模块205的功能。
示例性的，提高机载雷达杂波抑制性能的方法的计算机程序503主要包括：获取空时快拍信号；根据空时快拍信号构造虚拟空时快拍信号；根据降维转换矩阵对虚拟空时快拍信号进行降维处理，得到虚拟降维空时快拍信号；计算虚拟降维空时快拍信号的协方差矩阵；根据该虚拟降维空时快拍信号的协方差矩阵获取虚拟降维STAP滤波器，以使该虚拟降维STAP滤波器输出杂波抑制后的信号。计算机程序503可以被分割成一个或多个模块/单元，一个或者多个模块/单元被存储在存储器502中，并由处理器501执行，以完成本发明。一个或多个模块/单元可以是能够完成特定功能的一系列计算机程序指令段，该指令段用于描述计算机程序503在计算设备5中的执行过程。例如，计算机程序503可以被分割成获取模块201、构建模块202、降维处理模块203、计算模块204和转换模块205(虚拟装置中的模块)的功能，各模块具体功能如下：获取模块201，用于获取空时快拍信号；构建模块202，用于根据空时快拍信号构造虚拟空时快拍信号；降维处理模块203，用于根据降维转换矩 阵对虚拟空时快拍信号进行降维处理，得到虚拟降维空时快拍信号；计算模块204，用于计算虚拟降维空时快拍信号的协方差矩阵；转换模块205，用于根据虚拟降维空时快拍信号的协方差矩阵获取虚拟降维STAP滤波器，以使该虚拟降维STAP滤波器输出杂波抑制后的信号。
计算设备5可包括但不仅限于处理器501、存储器502。本领域技术人员可以理解，图8仅仅是计算设备5的示例，并不构成对计算设备5的限定，可以包括比图示更多或更少的部件，或者组合某些部件，或者不同的部件，例如计算设备还可以包括输入输出设备、网络接入设备、总线等。
所称处理器501可以是中央处理单元(Central Processing Unit，CPU)，还可以是其他通用处理器、数字信号处理器(Digital Signal Processor，DSP)、专用集成电路(Application Specific Integrated Circuit，ASIC)、现成可编程门阵列(Field-Programmable Gate Array，FPGA)或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件等。通用处理器可以是微处理器或者该处理器也可以是任何常规的处理器等。
存储器502可以是计算设备5的内部存储单元，例如计算设备5的硬盘或内存。存储器502也可以是计算设备5的外部存储设备，例如计算设备5上配备的插接式硬盘，智能存储卡(Smart Media Card，SMC)，安全数字(Secure Digital，SD)卡，闪存卡(Flash Card)等。进一步地，存储器502还可以既包括计算设备5的内部存储单元也包括外部存储设备。存储器502用于存储计算机程序以及计算设备所需的其他程序和数据。存储器502还可以用于暂时地存储已经输出或者将要输出的数据。
所属领域的技术人员可以清楚地了解到，为了描述的方便和简洁，仅以上述各功能单元、模块的划分进行举例说明，实际应用中，可以根据需要而将上述功能分配由不同的功能单元、模块完成，即将装置的内部结构划分成不同的功能单元或模块，以完成以上描述的全部或者部分功能。实施例中的各功能单元、模块可以集成在一个处理单元中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个单元中，上述集成的单元既可以采用硬件的形式实现，也可以采用软件功能单元的形式实现。另外，各功能单元、模块的具体名称也只是为了便于相互区分，并不用于限制本申请的保护范围。上述系统中单元、模块的具体工作过程，可以参考前述方法实施例中的对应过程，在此不再赘述。
在上述实施例中，对各个实施例的描述都各有侧重，某个实施例中没有详述或记载的部分，可以参见其它实施例的相关描述。
本领域普通技术人员可以意识到，结合本文中所公开的实施例描述的各示例的单元及算法步骤，能够以电子硬件、或者计算机软件和电子硬件的结合来实现。这些功能究竟以硬件还是软件方式来执行，取决于技术方案的特定应用和设计约束条件。专业技术人员可以对每个特定的应用来使用不同方法来实现所描述的功能，但是这种实现不应认为超出本发明的范围。
在本发明所提供的实施例中，应该理解到，所揭露的装置/计算设备和方法，可以通过其它的方式实现。例如，以上所描述的装置/计算设备实施例仅仅是示意性的，例如，模块或单元的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，例如多个单元或组件可以结合或者可以集成到另一个系统，或一些特征可以忽略，或不执行。另一点，所显示或讨论的相互之间的耦合 或直接耦合或通讯连接可以是通过一些接口，装置或单元的间接耦合或通讯连接，可以是电性，机械或其它的形式。
作为分离部件说明的单元可以是或者也可以不是物理上分开的，作为单元显示的部件可以是或者也可以不是物理单元，即可以位于一个地方，或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部单元来实现本实施例方案的目的。
另外，在本发明各个实施例中的各功能单元可以集成在一个处理单元中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个单元中。上述集成的单元既可以采用硬件的形式实现，也可以采用软件功能单元的形式实现。
集成的模块/单元如果以软件功能单元的形式实现并作为独立的产品销售或使用时，可以存储在一个计算机可读取存储介质中。基于这样的理解，本发明实现上述实施例方法中的全部或部分流程，也可以通过计算机程序来指令相关的硬件来完成，提高机载雷达杂波抑制性能的方法的计算机程序可存储于一计算机可读存储介质中，该计算机程序在被处理器执行时，可实现上述各个方法实施例的步骤，即，获取空时快拍信号；根据空时快拍信号构造虚拟空时快拍信号；根据降维转换矩阵对虚拟空时快拍信号进行降维处理，得到虚拟降维空时快拍信号；计算虚拟降维空时快拍信号的协方差矩阵；根据该虚拟降维空时快拍信号的协方差矩阵获取虚拟降维STAP滤波器，以使该虚拟降维STAP滤波器输出杂波抑制后的信号。
其中，计算机程序包括计算机程序代码，计算机程序代码可以为源代码形式、对象代码形式、可执行文件或某些中间形式等。计算机可读介质可以包括：能够携带计算机程序代码的任何实体或装置、记录介质、U盘、移动硬盘、磁碟、光盘、计算机存储器、只读存储器(ROM，Read-Only Memory)、随机存取存储器(RAM，Random Access Memory)、电载波信号、电信信号以及软件分发介质等。需要说明的是，计算机可读介质包含的内容可以根据司法管辖区内立法和专利实践的要求进行适当的增减，例如在某些司法管辖区，根据立法和专利实践，计算机可读介质不包括电载波信号和电信信号。以上实施例仅用以说明本发明的技术方案，而非对其限制；尽管参照前述实施例对本发明进行了详细的说明，本领域的普通技术人员应当理解：其依然可以对前述各实施例所记载的技术方案进行修改，或者对其中部分技术特征进行等同替换；而这些修改或者替换，并不使相应技术方案的本质脱离本发明各实施例技术方案的精神和范围，均应包含在本发明的保护范围之内。
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以上为对本发明所提供的一种提高机载雷达杂波抑制性能的方法及系统的描述，对于本领域的技术人员，依据本发明实施例的思想，在具体实施方式及应用范围上均会有改变之处，综上，本说明书内容不应理解为对本发明的限制。

Claims (10)

  1. 一种提高机载雷达杂波抑制性能的方法，其特征在于，所述方法包括：
    获取空时快拍信号；
    根据所述空时快拍信号构造虚拟空时快拍信号；
    根据降维转换矩阵对所述虚拟空时快拍信号进行降维处理，得到虚拟降维空时快拍信号；
    计算所述虚拟降维空时快拍信号的协方差矩阵；
    根据所述虚拟降维空时快拍信号的协方差矩阵获取虚拟降维空时自适应处理STAP滤波器，以使所述虚拟降维STAP滤波器输出杂波抑制后的信号。
  2. 根据权利要求1所述的方法，其特征在于，所述获取空时快拍信号的步骤包括：
    获取互质阵列机载雷达中的待测距离单元的回波互质阵列空时快拍信号；
    根据所述互质阵列空时快拍信号获取互质阵列空时快拍信号的协方差矩阵。
  3. 根据权利要求1所述的方法，其特征在于，所述根据所述空时快拍信号构造虚拟空时快拍信号之后，所述方法还包括：
    根据所述虚拟空时快拍信号获取所述虚拟空时快拍信号的协方差矩阵。
  4. 根据权利要求1所述的方法，其特征在于，所述根据所述虚拟降维空时快拍信号的协方差矩阵获取虚拟降维STAP滤波器的步骤包括：
    根据滤波器计算方法计算所述虚拟降维STAP滤波器的权矢量；
    根据所述权矢量计算所述虚拟降维STAP滤波器的输出空时快拍信号，以使所述虚拟降维STAP滤波器输出高杂波抑制性能的信号。
  5. 根据权利要求4所述的方法，其特征在于，所述虚拟降维STAP滤波器为虚拟降维样本协方差矩阵求逆STAP滤波器和虚拟降维主分量分析STAP滤波器中的任意一个；
    所述虚拟降维样本协方差矩阵求逆STAP滤波器根据虚拟降维样本协方差求逆算法对所述虚拟降维空时快拍信号进行滤波；
    所述虚拟降维主分量分析STAP滤波器根据虚拟降维主分量分析算法对所述虚拟降维空时快拍信号进行滤波。
  6. 一种提高机载雷达杂波抑制性能的系统，其特征在于，所述系统包括：
    获取模块，用于获取空时快拍信号；
    构建模块，用于根据所述空时快拍信号构造虚拟空时快拍信号；
    降维处理模块，用于根据降维转换矩阵对所述虚拟空时快拍信号进行降维处理，得到虚拟降维空时快拍信号；
    计算模块，用于计算所述虚拟降维空时快拍信号的协方差矩阵；
    转换模块，用于根据所述虚拟降维空时快拍信号的协方差矩阵获取虚拟降维空时自适应处理STAP滤波器，以使所述虚拟降维STAP滤波器输出杂波抑制后的信号。
  7. 根据权利要求6所述的系统，其特征在于，所述获取模块包括：
    第一获取模块，用于获取互质阵列机载雷达中的待测距离单元的回波互质阵列空时快拍信号；
    第二获取模块，用于根据所述互质阵列空时快拍信号获取互质阵列空时快拍信号的协方差矩阵。
  8. 根据权利要求6所述的系统，其特征在于，所述转换模块包括：
    第一计算模块，用于根据滤波器计算方法计算所述虚拟降维STAP滤波器的权矢量；
    第二计算模块，用于根据所述权矢量计算所述虚拟降维STAP滤波器的输出空时快拍信号，以使所述虚拟降维STAP滤波器输出高杂波抑制性能的信号。
  9. 一种计算设备，包括存储器、处理器以及存储在所述存储器中并可在所述处理器上运行的计算机程序，其特征在于，所述处理器执行所述计算机程序时实现如权利要求1至5任意一项所述方法的步骤。
  10. 一种计算机可读存储介质，所述计算机可读存储介质存储有计算机程序，其特征在于，所述计算机程序被处理器执行时实现如权利要求1至5任意一项所述方法的步骤。
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