具有高稳健性的浅海目标方位估计方法研究
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摘要
声纳系统作为对水下目标进行检测、定位及跟踪的重要设备已被广泛应用。在复杂的海洋环境下，对水下目标实现准确而稳健的高分辨方位估计，是新一代声纳迫切需要解决的关键技术。环境及声纳系统参数的失配影响了浅海高分辨方位估计方法的性能。本论文将海洋声传播物理模型与稳健自适应波束形成方法相结合，以克服参数失配对方位估计性能的影响。
首先，本文利用联合对角化这一数学工具，分别构造空时相关矩阵组和高阶累积量矩阵组的联合对角化结构，通过统一的Jacobi旋转正交联合对角化方法，重新获得联合特征值并对空间谱进行修正，以提高高分辨算法在噪声环境下的方位估计性能。
其次，本文考虑优化准则和多种失配因素的影响，提出矢量最优化稳健波束形成方法，有效地改善算法的方位估计性能，通过理论分析可证明该方法可将主流的稳健自适应波束形成算法整合至矢量最优化的整体框架下。文中推导了利用二阶锥规划求解矢量最优化约束问题的表达式，进一步研究了求解矢量最优化稳健波束形成最优权系数的Lagrange快速算法，得到权矢量的近似解析式，并讨论影响最优对角加载因子的各个因素之间的关系，证明了该算法广义上属于对角加载类方法。数值仿真及实验研究表明该方法在约束参数的选取上具有更强的宽容性，在存在相同方位估计偏差的条件下，具有不低于其他主流对角加载类算法的输出信干噪比性能。同时，在相同的信噪比、快拍数以及导向矢量误差条件下，该算法具有更高的谱峰与最大旁瓣比，更窄的-3dB波束宽度，可明显改善算法的分辨能力及对噪声干扰的抑制能力。
再次，本文针对高频近程声纳系统的应用情况，利用虚源理论建立了基阵信号模型并分析了多途效应对方位估计的影响，结合矢量最优化稳健波束形成在约束参数选取宽容性好及稳健性高等方面的优点，提出了一种基于射线理论的矢量最优化浅海稳健方位估计方法。该方法在充分考虑多途信道特点的基础上，进一步考虑实际模型失配的影响，对实际源矢量施加约束条件，以提高高分辨方位估计方法在失配条件下的稳健性。仿真及实验结果表明，该算法在沉积层声学参数不确定、海水深度不确定以及存在导向矢量误差等因素的条件下，具有更为尖锐的谱峰及更低的旁瓣水平，更高的空间分辨能力及更强的噪声干扰抑制能力。
最后，本文针对浅海远程探测声纳系统的应用情况，利用简正波理论，建立了符合浅海声传播特点的基阵信号模型，应用矢量最优化方法对阵列源矢量施加约束条件，以提高高分辨方位估计方法在海水跃变层深度、海水深度及声速梯度等环境参数失配下的稳健性。文中对该算法的空间谱结构、PSR、-3dB波束宽度、成功概率及方位估计偏差等进行了详细的讨论和分析。仿真结果表明，该算法在不同信噪比、入射方位角以及海水跃变层深度、海水深度及声速梯度等不确定条件下，均具有更为尖锐的谱峰及更低的旁瓣水平，并可保持较高的成功概率和方位估计精度。
本文将信道模型及稳健自适应方位估计方法相结合，提出了具有高稳健性的浅海方位估计方法，明显提高了在模型失配下的高分辨方位估计性能。
Sonar systems have been widely used as important equipments for underwater targetdetection, localization and tracking. In the complex ocean environment, the accurate androbust high-resolution estimation on direction of arrival is a key technology for newgeneration of sonar system.The mismatch of environment and sonar system parametersdegrades the performance of high-resolution beamforming in shallow water. In this thesis, theunderwater acoustic propagation model and the robust adaptive beamforming technology arecombined to overcome the influence of parameter mismatch on the DOA estimationperformance.
Firstly, the mathematical tool, joint diagonalization, is used to deal with the spatial-timecorrelation matrix groups and the cumulant matrix groups to construct their jointdiagonalization structures. Through the uniform Jacobi rotation orthogonal jointdiagonalization, the corresponding eigenvalues are obtained and the spatial spectrum isredefined. After that, the performance of high-resolution DOA estimation algorithms can beimproved in the noise environment.
Secondly, the influences of the optimization criterions and different mismatch factors areconsidered, the vector optimization robust adaptive beamforming algorithm is proposed toimprove the performance on DOA estimation. It can be proved that the existing robustadaptive beamforming algorithms can be unified into this vector optimization framework.Thispaper presents the process to solve the vector optimization problem by the second order coneprogram (SOCP) and an approximate analytic formula for the optimization weight vectorcalculation is obtained according to the Lagrange fast algorithm.Furthermore, this paperanalyzed the relationships between various factors for the optimal diagonal loading factor, andproved that the proposed algorithm is a generalized diagonal loading method. The numericalsimulation and experimental researches show that the proposed algorithm has a wideparameter selection range, and its output signal interference noise ratio (SINR) is not lowerthan other algorithms under the same bearing estimation bias. At the same time, the proposedalgorithm has higher peak to maximum sidelobe ratio (PSR) and narrower-3dB beamwidth,under the same signal noise ratio (SNR), snapshots, or steering vector errors. So it cansignificantly improve the DOA estimation resolution and the suppression ability for the noiseand interference.
Thirdly, we established the array signal model according to the theory of image and analyzed the multipath effect on the bearing estimation for the situation of high-frequencyshort-range sonar system application. Then, combine the advantages of the vectoroptimization robust adaptive beamforming, such as a good tolerance to the constraintparameter selection, higher stability, and so on, we propose a vector optimization robustbearing estimation method in shallow water based on ray theory. Taking fully account of themultipath channel characteristics, we further consider the model mismatch influence, andimpose constraint conditions on the actual source vectors to improve the robustness for thehigh-resolution bearing estimation methods under the mismatch conditions. The simulationand experimental results show that the proposed method has higher peaks and lower sidelobelevel under the same conditions of the sediment parameter uncertainties, water depthuncertainties, the steering vector errors and other factors. So it has better DOA estimationresolution and noise interference suppression ability.
Finally, for the situation of the shallow-water remote detecting sonar system application,we established the array signal model according to the normal mode theory and applied avector optimization method to impose constraint conditions on the actual source vectors inorder to improve the robustness for the high-resolution bearing estimation with theuncertainty of water thermocline depth、water depth, sound velocity gradient and other factorsin shallow water.In this paper, we discussed and analyzed the spatial spectrum structure, PSR,-3dB beamwidth, success probability, bearing estimation bias, and so on in detail. Thesimulation results show that the proposed algorithm has higher spectral peaks and lowersidelobe level under the conditions of different SNRs, incidence angles, water thermoclinedepth、water depth and sound velocity gradient uncertainties. At the same time, it keeps bettersuccess probability and bearing estimation accuracy.
In this paper, the underwater channel model and the excellent robust adaptivebeamforming technique are combined to propose the highly robust bearing estimation methodin shallow water. It has been shown that the algorithm performance can be improvedsignificantly when the model parameters are mismatched.
首先，本文利用联合对角化这一数学工具，分别构造空时相关矩阵组和高阶累积量矩阵组的联合对角化结构，通过统一的Jacobi旋转正交联合对角化方法，重新获得联合特征值并对空间谱进行修正，以提高高分辨算法在噪声环境下的方位估计性能。
其次，本文考虑优化准则和多种失配因素的影响，提出矢量最优化稳健波束形成方法，有效地改善算法的方位估计性能，通过理论分析可证明该方法可将主流的稳健自适应波束形成算法整合至矢量最优化的整体框架下。文中推导了利用二阶锥规划求解矢量最优化约束问题的表达式，进一步研究了求解矢量最优化稳健波束形成最优权系数的Lagrange快速算法，得到权矢量的近似解析式，并讨论影响最优对角加载因子的各个因素之间的关系，证明了该算法广义上属于对角加载类方法。数值仿真及实验研究表明该方法在约束参数的选取上具有更强的宽容性，在存在相同方位估计偏差的条件下，具有不低于其他主流对角加载类算法的输出信干噪比性能。同时，在相同的信噪比、快拍数以及导向矢量误差条件下，该算法具有更高的谱峰与最大旁瓣比，更窄的-3dB波束宽度，可明显改善算法的分辨能力及对噪声干扰的抑制能力。
再次，本文针对高频近程声纳系统的应用情况，利用虚源理论建立了基阵信号模型并分析了多途效应对方位估计的影响，结合矢量最优化稳健波束形成在约束参数选取宽容性好及稳健性高等方面的优点，提出了一种基于射线理论的矢量最优化浅海稳健方位估计方法。该方法在充分考虑多途信道特点的基础上，进一步考虑实际模型失配的影响，对实际源矢量施加约束条件，以提高高分辨方位估计方法在失配条件下的稳健性。仿真及实验结果表明，该算法在沉积层声学参数不确定、海水深度不确定以及存在导向矢量误差等因素的条件下，具有更为尖锐的谱峰及更低的旁瓣水平，更高的空间分辨能力及更强的噪声干扰抑制能力。
最后，本文针对浅海远程探测声纳系统的应用情况，利用简正波理论，建立了符合浅海声传播特点的基阵信号模型，应用矢量最优化方法对阵列源矢量施加约束条件，以提高高分辨方位估计方法在海水跃变层深度、海水深度及声速梯度等环境参数失配下的稳健性。文中对该算法的空间谱结构、PSR、-3dB波束宽度、成功概率及方位估计偏差等进行了详细的讨论和分析。仿真结果表明，该算法在不同信噪比、入射方位角以及海水跃变层深度、海水深度及声速梯度等不确定条件下，均具有更为尖锐的谱峰及更低的旁瓣水平，并可保持较高的成功概率和方位估计精度。
本文将信道模型及稳健自适应方位估计方法相结合，提出了具有高稳健性的浅海方位估计方法，明显提高了在模型失配下的高分辨方位估计性能。
Sonar systems have been widely used as important equipments for underwater targetdetection, localization and tracking. In the complex ocean environment, the accurate androbust high-resolution estimation on direction of arrival is a key technology for newgeneration of sonar system.The mismatch of environment and sonar system parametersdegrades the performance of high-resolution beamforming in shallow water. In this thesis, theunderwater acoustic propagation model and the robust adaptive beamforming technology arecombined to overcome the influence of parameter mismatch on the DOA estimationperformance.
Firstly, the mathematical tool, joint diagonalization, is used to deal with the spatial-timecorrelation matrix groups and the cumulant matrix groups to construct their jointdiagonalization structures. Through the uniform Jacobi rotation orthogonal jointdiagonalization, the corresponding eigenvalues are obtained and the spatial spectrum isredefined. After that, the performance of high-resolution DOA estimation algorithms can beimproved in the noise environment.
Secondly, the influences of the optimization criterions and different mismatch factors areconsidered, the vector optimization robust adaptive beamforming algorithm is proposed toimprove the performance on DOA estimation. It can be proved that the existing robustadaptive beamforming algorithms can be unified into this vector optimization framework.Thispaper presents the process to solve the vector optimization problem by the second order coneprogram (SOCP) and an approximate analytic formula for the optimization weight vectorcalculation is obtained according to the Lagrange fast algorithm.Furthermore, this paperanalyzed the relationships between various factors for the optimal diagonal loading factor, andproved that the proposed algorithm is a generalized diagonal loading method. The numericalsimulation and experimental researches show that the proposed algorithm has a wideparameter selection range, and its output signal interference noise ratio (SINR) is not lowerthan other algorithms under the same bearing estimation bias. At the same time, the proposedalgorithm has higher peak to maximum sidelobe ratio (PSR) and narrower-3dB beamwidth,under the same signal noise ratio (SNR), snapshots, or steering vector errors. So it cansignificantly improve the DOA estimation resolution and the suppression ability for the noiseand interference.
Thirdly, we established the array signal model according to the theory of image and analyzed the multipath effect on the bearing estimation for the situation of high-frequencyshort-range sonar system application. Then, combine the advantages of the vectoroptimization robust adaptive beamforming, such as a good tolerance to the constraintparameter selection, higher stability, and so on, we propose a vector optimization robustbearing estimation method in shallow water based on ray theory. Taking fully account of themultipath channel characteristics, we further consider the model mismatch influence, andimpose constraint conditions on the actual source vectors to improve the robustness for thehigh-resolution bearing estimation methods under the mismatch conditions. The simulationand experimental results show that the proposed method has higher peaks and lower sidelobelevel under the same conditions of the sediment parameter uncertainties, water depthuncertainties, the steering vector errors and other factors. So it has better DOA estimationresolution and noise interference suppression ability.
Finally, for the situation of the shallow-water remote detecting sonar system application,we established the array signal model according to the normal mode theory and applied avector optimization method to impose constraint conditions on the actual source vectors inorder to improve the robustness for the high-resolution bearing estimation with theuncertainty of water thermocline depth、water depth, sound velocity gradient and other factorsin shallow water.In this paper, we discussed and analyzed the spatial spectrum structure, PSR,-3dB beamwidth, success probability, bearing estimation bias, and so on in detail. Thesimulation results show that the proposed algorithm has higher spectral peaks and lowersidelobe level under the conditions of different SNRs, incidence angles, water thermoclinedepth、water depth and sound velocity gradient uncertainties. At the same time, it keeps bettersuccess probability and bearing estimation accuracy.
In this paper, the underwater channel model and the excellent robust adaptivebeamforming technique are combined to propose the highly robust bearing estimation methodin shallow water. It has been shown that the algorithm performance can be improvedsignificantly when the model parameters are mismatched.
引文
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