阵列天线快速自适应波束形成技术研究
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摘要
数字波束形成(Digital Beamforming, DBF)天线系统以其在抗干扰、多波束等方面的独特优点在雷达、通信、声纳等领域得到越来越广泛的应用。本文针对大阵列天线自适应数字波束形成处理中算法运算量大、传输数据量大及系统性能受阵列非理想因素影响的问题,围绕分块并行、子阵、降秩和稳健处理等几个方面,对阵列天线快速自适应波束形成技术进行了研究。本文主要工作如下：
1、采用分块并行的方法来解决大阵列天线自适应数字波束形成中计算量和数据传输的瓶颈问题。首先,把分块并行的线性约束最小方差(Partitioned-Parallel Linearly Constrained Minimum Variance, PLCMV)算法推广到二维平面阵列,给出了权矢量方程的构造方法,推导了适用于二维平面天线阵列的PLCMV自适应波束形成算法,并给出了该算法在分布式并行处理系统的实现方案图。另外,提出了基于最小均方的分块并行(Partitioned-Parallel Least Mean Square, PLMS)自适应波束形成算法,并在该算法中加入了切比雪夫约束来获得低的波束旁瓣性能。该算法具有与传统LMS(Least Mean Square)相同的波束形成性能,且可以应用于分布式并行处理系统来实现实时处理。PLMS具有比PLCMV更低的运算量,和更简单的算法实现流程。
2、对子阵级自适应波束形成算法进行了研究。首先,针对大型的二维平面阵列天线,将阵元级不规则错位行的思想应用于子阵布阵中,研究了基于子阵的平面阵列不规则错位布阵方式。并利用二进制粒子群优化算法对错位量的不规则方式进行了优化设计。采用优化后的不规则错位子阵结构的面阵,可以有效降低天线扫描范围内的栅瓣电平。然后,将二维面阵分块并行LCMV(Linearly Constrained Minimum Variance)算法应用于优化后的不规则错位子阵结构,在保证主瓣指向的同时,既控制了栅瓣电平,又可以自适应抑制干扰。利用子阵降低自适应处理维数的同时,通过分块并行算法进一步加快自适应波束形成处理速度。
3、传统的降秩自适应波束形成算法降秩矩阵大多数需通过特征分解获得,而特征值分解会带来新的大运算量,大大限制了算法的工程实现。本文提出了基于最小方差波束形成器(Minimum Variance Beamformer, MVB)的快速降秩自适应波束形成算法(Fast Reduced Rank MVB, FRRMVB)。FRRMVB利用阵列接收的快拍数据来快速构造降秩矩阵中的干扰子空间,具有更优的实时性,适用于大规模阵列雷达或是空时处理雷达系统。该算法在小快拍数下仍有很好波束形成性能,且FRRMVB波束形成性能优于HTP(Hung-Turner Projection)算法。
FRRMVB适用于存在间歇期的雷达系统,用来构造干扰子空间的快拍数据中不能含有期望信号。而连续波体制雷达,接收信号中一直存在期望信号。针对连续波体制雷达,本文还提出了一种基于广义旁瓣相消器(Generalized Sidelobe Canceler, GSC)的快速降秩(Fast Reduced Rank Generalized Sidelobe Canceler, FRRGSC)自适应波束形成算法。该算法可以应用于接收信号中含有期望信号的情况。利用GSC下支路的中间快拍数据来构造降秩矩阵,并在此基础上对快速估计干扰空间的方法进行了改进,利用所有可以利用的快拍数来构造降秩矩阵,只增加了少量的运算量,使得所提出的快速降秩波束形成算法具有很好的稳健性。FRRGSC算法构造降秩矩阵只需要一次复数乘法和少量复数加法,所需运算量远小于传统的基于GSC的降秩算法,在实际应用中具有更优的实时性,有利于算法的工程实现。
4、针对大阵列自适应波束形成算法的稳健性问题,提出了一种适用于大型阵列的分块并行的稳健递归LCMV算法(Partitioned-Parallel Robust Recursive LCMV, PRRLCMV),并给出了PRRLCMV算法应用于分布式并行处理系统的方案流程图。PRRLCMV算法可以有效地校正估计期望信号方向性矢量误差,避免期望信号方向性矢量误差所带来的算法波束形成性能下降。PRRLCMV算法的运算量远远小于传统的基于梯度优化的稳健LCMV算法,且在达到稳健性的同时,该算法的大部分步骤可以进行分块并行处理。
5、对传统的基于投影的稳健自适应波束形成算法进行了改进,首先通过最小均方误差准则求得校正后的估计协方差矩阵,然后从校正协方差矩阵的特征向量中估计出期望信号子空间,最后把估计期望信号方向性矢量向期望信号子空间投影得到校正的期望信号方向性矢量。与传统的基于投影的稳健算法相比,改进后算法不需要估计信源个数,且在低信噪比下仍具有很好的波束形成性能。
Digital beamforming(DBF) technique can greatly improve the capability of interference suppression, and can conveniently form multi-beam. It is applied for many areas such as radar, communication sonar. For a large-scale adaptive array, heavy computational load and high-rate data transmission are two challenges in the implementation of an adaptive DBF system. Moreover, the large-scale array becomes extremely sensitive to array imperfections. In this dissertation, some research works have been made on fast digital beamforming technique:Partioned-Parallel DBF algorithm, DBF algorithm based on subarray, reduced rank DBF algorithm and robust DBF algorithm. The main contributions are illustrated as follows:
1. The efficient Partitioned-Parallel DBF method has been used to deal with the bottleneck of high-rate data transmission and reduce the computational cost. First, the Partitioned-Parallel Linearly Constrained Minimum Variance (PLCMV) is extended to adaptive 2-D antenna array processing. An implementation scheme of the 2-D PLCMV algorithm based on a distributed-parallel-processing system is also present. Then, an efficient Partioned-Parallel DBF algorithm based on the least mean square algorithm (PLMS) is proposed. Moreover, low sidelobe of the beam pattern is obtained by constraining the static steering vector with Tschebyscheff coefficients. PLMS has the same performance as the conventional LMS algorithm. Also, PLMS requires less computational load than PLCMV. as well as it is easier to be implemented to do real time adaptive array processing. An implementation scheme of the PLMS algorithm based on a distributed-parallel-processing system is also present.
2. Subarray technique for DBF system is investigated. First, for the large plane antenna arrays with subarrays, the randomly staggered structure is applied to decrease the grating lobes. Binary particle swarm optimization (BPSO) algorithm is used to find the optimal structure of the randomly staggered subarrays. Simulated results show that the plane antenna arrays with subarrays of this new structure can get low side lobe in the scanning range. Then, PLCMV based on the optimal staggered structure is proposed, wich can adaptive cancel the interferences. Combine subarray to reduce the adaptive dimension and the Partioned-Paralle algorithm to achieve fast processing.
3. In conventional reduced rank beamformer based on Generalized Sidelobe Canceller (GSC) or Minimum Variance beamformer (MVB). reducing rank matrix is usually obtained from estimated sample covariance matrix by eigendecomposition. To alleviate the computational burden and achieve real-time processing, a fast reduced rank adaptive beamforming based on minimum variance beamformer(FRRMVB) is proposed. FRRMVB takes a set of data vectors as a rough and fast estimate of the interference subspace. And the rank reducing matrix is obtained as the augmentation of the estimated interference subspace and the steering vector of the desired signal. The FRRMVB is very simpler in design and programming, and requires less computational load than the conventional RRMVB adaptive beamformer. Moreover, it has good performance even with small sample size, and has better performance than HTP algorithm. FRRMVB can be used to deal with the real-time adaptive array processing for uncontinuous wave radar. For continuous wave radar, a new fast reduced rank adaptive beamforming algorithm based on GSC(FRRGSC) is proposed. It uses a set of intermediate data vectors in the below branch of GSC to construct the rank reducing matrix. Furthermore, in order to achieve the robustness, all available snapshots have been used to obtain the rank reducing matrix. The FRRGSC method is very simpler in design and programming, and requires less computational load than the conventional reduced rank adaptive beamformer. Simulation results demonstrate the validity of this new method.
4. In practical array systems, traditional adaptive beamforming algorithms are known to degrade if some of exploited assumptions on the environment, sources or antenna array become wrong or imprecise. A new efficient partitioned-parallel robust recursive linearly constrained minimum variance (PRRLCMV) algorithm is proposed. An implementation scheme of the PRRLCMV algorithm based on a distributed-parallel-processing system is also present. It can be easily executed in a distributed-parallel-processing fashion, sequentially and in parallel. As a result, the PRRLCMV algorithm provides an effective solution that can alleviate the bottleneck of high-rate data transmission and reduce the computational cost. Moreover, PRRLCMV can significantly reduce the degradation due to various array errors.
5. A novel modified projection method is proposed, which is robust against the signal steering vector mismatch and covariance matrix uncertainty. First, an enhanced covariance matrix estimate based on a shrinkage method is obtained. Then, the desired signal subspace is estimated from the eigenvectors of the enhanced covariance matrix, and a new calibrated steering vector of the desired signal is obtained in sequence by projecting the presumed one onto the new estimated desired signal subspace. Compared with the traditional projection method, it does not need to estimate the number of sources, and works well even at low signal-to-noise ratio.
1、采用分块并行的方法来解决大阵列天线自适应数字波束形成中计算量和数据传输的瓶颈问题。首先,把分块并行的线性约束最小方差(Partitioned-Parallel Linearly Constrained Minimum Variance, PLCMV)算法推广到二维平面阵列,给出了权矢量方程的构造方法,推导了适用于二维平面天线阵列的PLCMV自适应波束形成算法,并给出了该算法在分布式并行处理系统的实现方案图。另外,提出了基于最小均方的分块并行(Partitioned-Parallel Least Mean Square, PLMS)自适应波束形成算法,并在该算法中加入了切比雪夫约束来获得低的波束旁瓣性能。该算法具有与传统LMS(Least Mean Square)相同的波束形成性能,且可以应用于分布式并行处理系统来实现实时处理。PLMS具有比PLCMV更低的运算量,和更简单的算法实现流程。
2、对子阵级自适应波束形成算法进行了研究。首先,针对大型的二维平面阵列天线,将阵元级不规则错位行的思想应用于子阵布阵中,研究了基于子阵的平面阵列不规则错位布阵方式。并利用二进制粒子群优化算法对错位量的不规则方式进行了优化设计。采用优化后的不规则错位子阵结构的面阵,可以有效降低天线扫描范围内的栅瓣电平。然后,将二维面阵分块并行LCMV(Linearly Constrained Minimum Variance)算法应用于优化后的不规则错位子阵结构,在保证主瓣指向的同时,既控制了栅瓣电平,又可以自适应抑制干扰。利用子阵降低自适应处理维数的同时,通过分块并行算法进一步加快自适应波束形成处理速度。
3、传统的降秩自适应波束形成算法降秩矩阵大多数需通过特征分解获得,而特征值分解会带来新的大运算量,大大限制了算法的工程实现。本文提出了基于最小方差波束形成器(Minimum Variance Beamformer, MVB)的快速降秩自适应波束形成算法(Fast Reduced Rank MVB, FRRMVB)。FRRMVB利用阵列接收的快拍数据来快速构造降秩矩阵中的干扰子空间,具有更优的实时性,适用于大规模阵列雷达或是空时处理雷达系统。该算法在小快拍数下仍有很好波束形成性能,且FRRMVB波束形成性能优于HTP(Hung-Turner Projection)算法。
FRRMVB适用于存在间歇期的雷达系统,用来构造干扰子空间的快拍数据中不能含有期望信号。而连续波体制雷达,接收信号中一直存在期望信号。针对连续波体制雷达,本文还提出了一种基于广义旁瓣相消器(Generalized Sidelobe Canceler, GSC)的快速降秩(Fast Reduced Rank Generalized Sidelobe Canceler, FRRGSC)自适应波束形成算法。该算法可以应用于接收信号中含有期望信号的情况。利用GSC下支路的中间快拍数据来构造降秩矩阵,并在此基础上对快速估计干扰空间的方法进行了改进,利用所有可以利用的快拍数来构造降秩矩阵,只增加了少量的运算量,使得所提出的快速降秩波束形成算法具有很好的稳健性。FRRGSC算法构造降秩矩阵只需要一次复数乘法和少量复数加法,所需运算量远小于传统的基于GSC的降秩算法,在实际应用中具有更优的实时性,有利于算法的工程实现。
4、针对大阵列自适应波束形成算法的稳健性问题,提出了一种适用于大型阵列的分块并行的稳健递归LCMV算法(Partitioned-Parallel Robust Recursive LCMV, PRRLCMV),并给出了PRRLCMV算法应用于分布式并行处理系统的方案流程图。PRRLCMV算法可以有效地校正估计期望信号方向性矢量误差,避免期望信号方向性矢量误差所带来的算法波束形成性能下降。PRRLCMV算法的运算量远远小于传统的基于梯度优化的稳健LCMV算法,且在达到稳健性的同时,该算法的大部分步骤可以进行分块并行处理。
5、对传统的基于投影的稳健自适应波束形成算法进行了改进,首先通过最小均方误差准则求得校正后的估计协方差矩阵,然后从校正协方差矩阵的特征向量中估计出期望信号子空间,最后把估计期望信号方向性矢量向期望信号子空间投影得到校正的期望信号方向性矢量。与传统的基于投影的稳健算法相比,改进后算法不需要估计信源个数,且在低信噪比下仍具有很好的波束形成性能。
Digital beamforming(DBF) technique can greatly improve the capability of interference suppression, and can conveniently form multi-beam. It is applied for many areas such as radar, communication sonar. For a large-scale adaptive array, heavy computational load and high-rate data transmission are two challenges in the implementation of an adaptive DBF system. Moreover, the large-scale array becomes extremely sensitive to array imperfections. In this dissertation, some research works have been made on fast digital beamforming technique:Partioned-Parallel DBF algorithm, DBF algorithm based on subarray, reduced rank DBF algorithm and robust DBF algorithm. The main contributions are illustrated as follows:
1. The efficient Partitioned-Parallel DBF method has been used to deal with the bottleneck of high-rate data transmission and reduce the computational cost. First, the Partitioned-Parallel Linearly Constrained Minimum Variance (PLCMV) is extended to adaptive 2-D antenna array processing. An implementation scheme of the 2-D PLCMV algorithm based on a distributed-parallel-processing system is also present. Then, an efficient Partioned-Parallel DBF algorithm based on the least mean square algorithm (PLMS) is proposed. Moreover, low sidelobe of the beam pattern is obtained by constraining the static steering vector with Tschebyscheff coefficients. PLMS has the same performance as the conventional LMS algorithm. Also, PLMS requires less computational load than PLCMV. as well as it is easier to be implemented to do real time adaptive array processing. An implementation scheme of the PLMS algorithm based on a distributed-parallel-processing system is also present.
2. Subarray technique for DBF system is investigated. First, for the large plane antenna arrays with subarrays, the randomly staggered structure is applied to decrease the grating lobes. Binary particle swarm optimization (BPSO) algorithm is used to find the optimal structure of the randomly staggered subarrays. Simulated results show that the plane antenna arrays with subarrays of this new structure can get low side lobe in the scanning range. Then, PLCMV based on the optimal staggered structure is proposed, wich can adaptive cancel the interferences. Combine subarray to reduce the adaptive dimension and the Partioned-Paralle algorithm to achieve fast processing.
3. In conventional reduced rank beamformer based on Generalized Sidelobe Canceller (GSC) or Minimum Variance beamformer (MVB). reducing rank matrix is usually obtained from estimated sample covariance matrix by eigendecomposition. To alleviate the computational burden and achieve real-time processing, a fast reduced rank adaptive beamforming based on minimum variance beamformer(FRRMVB) is proposed. FRRMVB takes a set of data vectors as a rough and fast estimate of the interference subspace. And the rank reducing matrix is obtained as the augmentation of the estimated interference subspace and the steering vector of the desired signal. The FRRMVB is very simpler in design and programming, and requires less computational load than the conventional RRMVB adaptive beamformer. Moreover, it has good performance even with small sample size, and has better performance than HTP algorithm. FRRMVB can be used to deal with the real-time adaptive array processing for uncontinuous wave radar. For continuous wave radar, a new fast reduced rank adaptive beamforming algorithm based on GSC(FRRGSC) is proposed. It uses a set of intermediate data vectors in the below branch of GSC to construct the rank reducing matrix. Furthermore, in order to achieve the robustness, all available snapshots have been used to obtain the rank reducing matrix. The FRRGSC method is very simpler in design and programming, and requires less computational load than the conventional reduced rank adaptive beamformer. Simulation results demonstrate the validity of this new method.
4. In practical array systems, traditional adaptive beamforming algorithms are known to degrade if some of exploited assumptions on the environment, sources or antenna array become wrong or imprecise. A new efficient partitioned-parallel robust recursive linearly constrained minimum variance (PRRLCMV) algorithm is proposed. An implementation scheme of the PRRLCMV algorithm based on a distributed-parallel-processing system is also present. It can be easily executed in a distributed-parallel-processing fashion, sequentially and in parallel. As a result, the PRRLCMV algorithm provides an effective solution that can alleviate the bottleneck of high-rate data transmission and reduce the computational cost. Moreover, PRRLCMV can significantly reduce the degradation due to various array errors.
5. A novel modified projection method is proposed, which is robust against the signal steering vector mismatch and covariance matrix uncertainty. First, an enhanced covariance matrix estimate based on a shrinkage method is obtained. Then, the desired signal subspace is estimated from the eigenvectors of the enhanced covariance matrix, and a new calibrated steering vector of the desired signal is obtained in sequence by projecting the presumed one onto the new estimated desired signal subspace. Compared with the traditional projection method, it does not need to estimate the number of sources, and works well even at low signal-to-noise ratio.
引文
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