机载/星载雷达地面运动目标检测稀疏处理方法研究
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摘要
机载/星载雷达地面运动目标检测(GMTI)系统在军事领域具有重要的应用价值。机载/星载雷达处于下视工作状态，由于平台的高速运动导致杂波多普勒展宽，从而会淹没慢速目标，因此需要进行杂波抑制。目前空时自适应处理(STAP)和多通道SAR/GMTI技术是针对机载/星载雷达的两种最基本的杂波抑制技术。但是由于实际非理想因素的影响，空时自适应处理性能改善受限于独立同分布样本的数目。对于多通道SAR/GMTI方法，随着雷达成像分辨率的提高和广域监视的需要，其采样数据量成倍增加，给数据传输和存储系统带来严峻挑战。本文紧密围绕少量数据样本条件下的GMTI方法展开研究，主要工作包括：
1、针对实际非均匀环境下独立同分布样本不足的问题，基于压缩感知理论提出一种针对单距离门数据处理的运动目标检测方法，该方法首先将接收回波数据变换至二维波束-多普勒域进行信号能量积累，并相应在二维频谱域构造冗余字典，利用贝叶斯压缩感知技术在待检测距离门内提取杂波谱的主要分量，然后估计杂波能量的支撑区域，以此作为先验知识，建立基于加权的最小l1范数优化模型实现运动目标检测。本文所提方法无需估计协方差矩阵，直接对待检测距离门数据进行处理，实验结果表明所提方法具有良好的动目标检测性能。
2、针对目前双通道SAR地面运动目标检测方法采样数据量过大的问题，提出一种基于稀疏采样条件下的双通道SAR/GMTI方法。该方法针对方位降采样的双通道回波数据，利用压缩感知技术实现SAR成像，并通过DPCA技术进行杂波抑制，从而最终实现地面运动目标检测。首先详细分析了压缩感知成像算法的运动目标重构模型，从理论上分析了算法利用双通道降采样数据进行杂波抑制的可行性。并通过公式推导和仿真实验证明了目标运动参数(距离向速度/加速度，方位向速度)对基于压缩感知算法的目标成像的影响。仿真与实测数据实验证明，在信杂比较高(场景散射系数近似稀疏)的情况下，本文算法具有良好的杂波抑制性能。
3、针对观测场景散射系数的空间分布稀疏性较差情况，提出一种基于联合稀疏处理和加权l1范数优化的双通道SAR/GMTI方法。考虑到多通道雷达对同一地面场景进行观测，因此图像对之间具有很强的相关性，该方法对两个通道的SAR回波数据进行联合处理，通过构造的变换矩阵将目标能量支撑区进行分离，将重构两幅SAR图像问题转换为重构一幅SAR图像和运动目标的问题，然后利用重构的SAR图像得到加权位置信息，通过构建加权l1范数优化模型实现杂波抑制与地面运动目标成像。分别将该方法应用至一维稀疏采样和二维稀疏采样情况，实验结果表明该方法只需少量样本数据即可有效实现地面运动目标检测，即使在信杂比较低条件下仍可取得较好的动目标检测结果。
4、针对观测场景不稀疏情况，提出一种稀疏采样下基于先验知识的杂波抑制与GMTI算法。在该算法中，雷达采用一发多收体制，其中一个通道按照奈奎斯特采样定理周期发射与接收脉冲信号，其他通道随机稀疏接收脉冲信号。首先对满采样的通道数据进行SAR成像，并以此作为先验知识对稀疏采样的通道数据进行稀疏表示，在其距离脉压域进行杂波抑制，此时通道中只包含运动目标信号。由于运动目标的空间分布具有很强的稀疏性，因此可以有效实现稀疏重构。该方法对通道间的幅度相位误差具有良好的稳健性。实验结果表明该方法可以极大程度降低所需的原始采样数据，同时能够较好地获得地面静止场景和运动目标信息。
5、传统扫描式合成孔径雷达(ScanSAR)成像算法方位分辨率低，并存在严重的“扇贝”效应，针对该问题，提出一种ScanSAR模式下基于稀疏重构理论的成像算法。该算法对同一子条带内的所有脉冲串统一处理，利用压缩感知理论重构方位图像。实验结果表明该算法可以获得较高的方位分辨率，并有效克服了“扇贝效应”。将方法应用于多通道SAR/GMTI实测数据处理，也获得较好的杂波抑制性能。
Airborne/spaceborne ground moving target indication (GMTI) radar system playsan important role in military applications. Since airborne/spaceborne radar usuallyworks in the downward-looking status thus causing a spreading clutter Dopplerbandwidth because of the high speed of the platform, the moving targets may besubmerged in clutter. At present, space time adapting processing (STAP) andmulti-channel SAR/GMTI techniques are the two fundamental clutter suppressionmethods for high-speed platform radar. In practical applications, it is hard to acquirethe sufficient Independent and Identically Distributed (IID) sample supports toestimate the covariance matrix due to the heterogeneity of the clutter, in the endcausing a dramatic degreadation of the STAP processor performance. For themulti-channel SAR/GMTI system, the increasing demands for high resolution of SARimages and wide swath coverage have resulted in a huge SAR raw data, which posesmajor constraints in the operation of SAR to transmit the data to ground, or to storethem onboard. In this thesis, novel GMTI methods with limited samples are studied.The main contributions of this thesis are as follows:
1、For conventional Space-Time Adaptive Processing (STAP), sufficient IIDsample supports are hard to obtain. To mitigate this problem, a Compressive Sensing(CS)-based Ground Moving Target Indication (GMTI) method is proposed. In theproposed method, firstly the space-time data of the interested range bin is transformedto spatial-temporal frequency fields to accumulate the signal energy and thecorresponding redundant dictionary is constructed; Then several primary clutterspectrum peaks are extracted by Bayesian compressive sensing technique to estimatethe clutter ridge; Finally, the weighted l1minimization optimization model is used torealize the ground moving target indication(GMTI) without estimating the covariancematrix. Simulation results demonstrate the excellent GMTI performance of theproposed method.
2、In the conventional SAR ground moving targets indication (GMTI) method, thesample number is heavily large, which greatly increase the data transmission andstorage load. To mitigate this problem, a SAR/GMTI method based on compressivesensing is proposed in this paper. In the proposed method, compressive sensing theoryis used to perform the azimuth direction focus with sparse sampled raw data. Theconventional displaced phase center antenna (DPCA) technique is adopted to suppress clutter. Theoretical analysis shows that the proposed method can be applied to cluttersuppression of the sparse sampling data of dual channels and the influences of motionparameters (range velocity/acceleration, azimuth velocity) on target imaging areanalysed in detail. The results of simulated and real data processing verify that theproposed method has excellent clutter suppression performance in the case of highsignal to clutter ratios (SCNR).
3、For the SAR-based GMTI system, the clutter scattering centers of surveillancearea are usually non-sparse. In this case, the sampled raw data of single channel cannot be significantly reduced. Considering the fact that the correlations among themulti-channel SAR images are high, a SAR/GMTI method based on jointly sparse andweighted l1optimization model is proposed. In the proposed method, dual channelSAR raw data are jointly processed. Firstly, a transform matrix is constructed toseparate the energy support areas of moving targets from that of all scattering centers,and then we convert dual-channel SAR imaging to single-channel imaging and movingtargets reconstruction. Then, we can roughly obtain the energy support areas of allscattering centers via CS. Finally, based on the acquired energy support areas above,clutter suppression and GMTI is achieved by solving a weighted l1optimization model.The proposed method is applied to the SAR raw data sparsely sampled in1-dimensiondomain (azimuth) and in2-dimension domains (range and azimuth), respectively.Simulated and real data experiments demonstrate that the proposed method performswell with sparse sampled raw data, even if clutter scattering centers have a low sparselevel.
4、For the cases of clutter scattering centers are non-sparse,we propose a cluttersuppression and GMTI method with sparse sampled data for dual-channel SAR. In theproposed method, one channel periodically transmits and receives pulses at Nyquistsampling rate, and other channels sparely and randomly receive pulses. Firstly, rawdata of the channel with full sampling are used to perform SAR imaging. Thenutilizing the acquired SAR image above to be prior-knowledge, the clutter included inother channels data is suppressed. Since the moving targets are sparse in space afterclutter suppression, the moving targets image can be accurately recovered bycompressive sensing. The proposed method is robust to amplitude and phase errorsbetween the channels. The experiment results demonstrate that the static and dynamicinformation loss of the interest area is slight, even if the sampled data are significantlyreduced.
5、The conventional ScanSAR imaging method acquires wide swath coverage at acost of severe azimuth resolution loss. Moreover, scalloping is also a major ScanSARdrawback. To overcome the aforementioned problem, a compressing sensing-basedimaging and GMTI method is proposed for ScanSAR mode. In the proposed method,CS is utilized to perform azimuth imaging by processing jointly sparse aperture data ofeach subswath after range compression and range cell migration correction (RCMC).The proposed method has high azimuth resolution. Moreover, it can overcomescalloping problem without resolution loss. An excellent clutter suppressionperformance using the proposed method can be achieved in the application ofmulti-channel SAR raw data processing.
1、针对实际非均匀环境下独立同分布样本不足的问题，基于压缩感知理论提出一种针对单距离门数据处理的运动目标检测方法，该方法首先将接收回波数据变换至二维波束-多普勒域进行信号能量积累，并相应在二维频谱域构造冗余字典，利用贝叶斯压缩感知技术在待检测距离门内提取杂波谱的主要分量，然后估计杂波能量的支撑区域，以此作为先验知识，建立基于加权的最小l1范数优化模型实现运动目标检测。本文所提方法无需估计协方差矩阵，直接对待检测距离门数据进行处理，实验结果表明所提方法具有良好的动目标检测性能。
2、针对目前双通道SAR地面运动目标检测方法采样数据量过大的问题，提出一种基于稀疏采样条件下的双通道SAR/GMTI方法。该方法针对方位降采样的双通道回波数据，利用压缩感知技术实现SAR成像，并通过DPCA技术进行杂波抑制，从而最终实现地面运动目标检测。首先详细分析了压缩感知成像算法的运动目标重构模型，从理论上分析了算法利用双通道降采样数据进行杂波抑制的可行性。并通过公式推导和仿真实验证明了目标运动参数(距离向速度/加速度，方位向速度)对基于压缩感知算法的目标成像的影响。仿真与实测数据实验证明，在信杂比较高(场景散射系数近似稀疏)的情况下，本文算法具有良好的杂波抑制性能。
3、针对观测场景散射系数的空间分布稀疏性较差情况，提出一种基于联合稀疏处理和加权l1范数优化的双通道SAR/GMTI方法。考虑到多通道雷达对同一地面场景进行观测，因此图像对之间具有很强的相关性，该方法对两个通道的SAR回波数据进行联合处理，通过构造的变换矩阵将目标能量支撑区进行分离，将重构两幅SAR图像问题转换为重构一幅SAR图像和运动目标的问题，然后利用重构的SAR图像得到加权位置信息，通过构建加权l1范数优化模型实现杂波抑制与地面运动目标成像。分别将该方法应用至一维稀疏采样和二维稀疏采样情况，实验结果表明该方法只需少量样本数据即可有效实现地面运动目标检测，即使在信杂比较低条件下仍可取得较好的动目标检测结果。
4、针对观测场景不稀疏情况，提出一种稀疏采样下基于先验知识的杂波抑制与GMTI算法。在该算法中，雷达采用一发多收体制，其中一个通道按照奈奎斯特采样定理周期发射与接收脉冲信号，其他通道随机稀疏接收脉冲信号。首先对满采样的通道数据进行SAR成像，并以此作为先验知识对稀疏采样的通道数据进行稀疏表示，在其距离脉压域进行杂波抑制，此时通道中只包含运动目标信号。由于运动目标的空间分布具有很强的稀疏性，因此可以有效实现稀疏重构。该方法对通道间的幅度相位误差具有良好的稳健性。实验结果表明该方法可以极大程度降低所需的原始采样数据，同时能够较好地获得地面静止场景和运动目标信息。
5、传统扫描式合成孔径雷达(ScanSAR)成像算法方位分辨率低，并存在严重的“扇贝”效应，针对该问题，提出一种ScanSAR模式下基于稀疏重构理论的成像算法。该算法对同一子条带内的所有脉冲串统一处理，利用压缩感知理论重构方位图像。实验结果表明该算法可以获得较高的方位分辨率，并有效克服了“扇贝效应”。将方法应用于多通道SAR/GMTI实测数据处理，也获得较好的杂波抑制性能。
Airborne/spaceborne ground moving target indication (GMTI) radar system playsan important role in military applications. Since airborne/spaceborne radar usuallyworks in the downward-looking status thus causing a spreading clutter Dopplerbandwidth because of the high speed of the platform, the moving targets may besubmerged in clutter. At present, space time adapting processing (STAP) andmulti-channel SAR/GMTI techniques are the two fundamental clutter suppressionmethods for high-speed platform radar. In practical applications, it is hard to acquirethe sufficient Independent and Identically Distributed (IID) sample supports toestimate the covariance matrix due to the heterogeneity of the clutter, in the endcausing a dramatic degreadation of the STAP processor performance. For themulti-channel SAR/GMTI system, the increasing demands for high resolution of SARimages and wide swath coverage have resulted in a huge SAR raw data, which posesmajor constraints in the operation of SAR to transmit the data to ground, or to storethem onboard. In this thesis, novel GMTI methods with limited samples are studied.The main contributions of this thesis are as follows:
1、For conventional Space-Time Adaptive Processing (STAP), sufficient IIDsample supports are hard to obtain. To mitigate this problem, a Compressive Sensing(CS)-based Ground Moving Target Indication (GMTI) method is proposed. In theproposed method, firstly the space-time data of the interested range bin is transformedto spatial-temporal frequency fields to accumulate the signal energy and thecorresponding redundant dictionary is constructed; Then several primary clutterspectrum peaks are extracted by Bayesian compressive sensing technique to estimatethe clutter ridge; Finally, the weighted l1minimization optimization model is used torealize the ground moving target indication(GMTI) without estimating the covariancematrix. Simulation results demonstrate the excellent GMTI performance of theproposed method.
2、In the conventional SAR ground moving targets indication (GMTI) method, thesample number is heavily large, which greatly increase the data transmission andstorage load. To mitigate this problem, a SAR/GMTI method based on compressivesensing is proposed in this paper. In the proposed method, compressive sensing theoryis used to perform the azimuth direction focus with sparse sampled raw data. Theconventional displaced phase center antenna (DPCA) technique is adopted to suppress clutter. Theoretical analysis shows that the proposed method can be applied to cluttersuppression of the sparse sampling data of dual channels and the influences of motionparameters (range velocity/acceleration, azimuth velocity) on target imaging areanalysed in detail. The results of simulated and real data processing verify that theproposed method has excellent clutter suppression performance in the case of highsignal to clutter ratios (SCNR).
3、For the SAR-based GMTI system, the clutter scattering centers of surveillancearea are usually non-sparse. In this case, the sampled raw data of single channel cannot be significantly reduced. Considering the fact that the correlations among themulti-channel SAR images are high, a SAR/GMTI method based on jointly sparse andweighted l1optimization model is proposed. In the proposed method, dual channelSAR raw data are jointly processed. Firstly, a transform matrix is constructed toseparate the energy support areas of moving targets from that of all scattering centers,and then we convert dual-channel SAR imaging to single-channel imaging and movingtargets reconstruction. Then, we can roughly obtain the energy support areas of allscattering centers via CS. Finally, based on the acquired energy support areas above,clutter suppression and GMTI is achieved by solving a weighted l1optimization model.The proposed method is applied to the SAR raw data sparsely sampled in1-dimensiondomain (azimuth) and in2-dimension domains (range and azimuth), respectively.Simulated and real data experiments demonstrate that the proposed method performswell with sparse sampled raw data, even if clutter scattering centers have a low sparselevel.
4、For the cases of clutter scattering centers are non-sparse,we propose a cluttersuppression and GMTI method with sparse sampled data for dual-channel SAR. In theproposed method, one channel periodically transmits and receives pulses at Nyquistsampling rate, and other channels sparely and randomly receive pulses. Firstly, rawdata of the channel with full sampling are used to perform SAR imaging. Thenutilizing the acquired SAR image above to be prior-knowledge, the clutter included inother channels data is suppressed. Since the moving targets are sparse in space afterclutter suppression, the moving targets image can be accurately recovered bycompressive sensing. The proposed method is robust to amplitude and phase errorsbetween the channels. The experiment results demonstrate that the static and dynamicinformation loss of the interest area is slight, even if the sampled data are significantlyreduced.
5、The conventional ScanSAR imaging method acquires wide swath coverage at acost of severe azimuth resolution loss. Moreover, scalloping is also a major ScanSARdrawback. To overcome the aforementioned problem, a compressing sensing-basedimaging and GMTI method is proposed for ScanSAR mode. In the proposed method,CS is utilized to perform azimuth imaging by processing jointly sparse aperture data ofeach subswath after range compression and range cell migration correction (RCMC).The proposed method has high azimuth resolution. Moreover, it can overcomescalloping problem without resolution loss. An excellent clutter suppressionperformance using the proposed method can be achieved in the application ofmulti-channel SAR raw data processing.
引文
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