Maneuvering Target Parameter Estimation Based on Sparse Bayesian Dictionary Learning in Space-Time Adaptive Processing
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摘要: 针对基于稀疏恢复的空时自适应处理(STAP)目标参数估计方法中字典失配导致估计性能下降的问题，该文提出一种基于稀疏贝叶斯字典学习的高精度目标参数估计方法。该方法首先通过目标方位信息补偿多个阵元数据构建联合稀疏恢复数据，然后对补偿后的每个阵元数据利用双线性变换进行加速度和速度项分离。最后构建速度参数和加速度参数的泰勒级数动态字典，对机动目标参数进行高精度贝叶斯字典学习稀疏恢复。仿真实验证明，该方法能有效提高字典失配情况下目标参数估计精度，估计性能优于已有字典固定离散化的稀疏恢复空时目标参数估计方法。Abstract: A sparse Bayesian dictionary learning-based parameter estimation method is proposed to overcome the performance degradation in presence of dictionary mismatch in Space-Time Adaptive Processing (STAP). First, multiple measurements are constructed by using direction compensated space samples. Second, the bilinear transformation is utilized to separate the velocity and acceleration of the maneuvering target. Finally, the dynamic dictionaries of velocity and acceleration are established by the Taylor’s series, and then the maneuvering target parameters are estimated by sparse Bayesian dictionary learning. Numerical results show that the proposed method can obtain better accuracy in parameter estimation, and can provide an improved performance to the sparse recovery methods with pre-discretized dictionary in STAP parameter estimation.
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