Fang Jiaxiong, Liu Yonghong, Jiang Yifang, Lu Yang, Zhang Zehao, Chen Hua, Wang Laihua
School of Faculty of Information Science and Engineering, Ningbo University, Ningbo 315211, China.
Key Laboratory of Intelligent Perception and Advanced Control of State Ethnic Affairs Commission, Dalian 116600, China.
Sensors (Basel). 2020 Apr 12;20(8):2177. doi: 10.3390/s20082177.
In this paper, a joint diagonalization based two dimensional (2D) direction of departure (DOD) and 2D direction of arrival (DOA) estimation method for a mixture of circular and strictly noncircular (NC) sources is proposed based on an L-shaped bistatic multiple input multiple output (MIMO) radar. By making full use of the L-shaped MIMO array structure to obtain an extended virtual array at the receive array, we first combine the received data vector and its conjugated counterpart to construct a new data vector, and then an estimating signal parameter via rotational invariance techniques (ESPRIT)-like method is adopted to estimate the DODs and DOAs by joint diagonalization of the NC-based direction matrices, which can automatically pair the four dimensional (4D) angle parameters and solve the angle ambiguity problem with common one-dimensional (1D) DODs and DOAs. In addition, the asymptotic performance of the proposed algorithm is analyzed and the closed-form stochastic Cramer-Rao bound (CRB) expression is derived. As demonstrated by simulation results, the proposed algorithm has outperformed the existing one, with a result close to the theoretical benchmark.
本文基于L型双基地多输入多输出（MIMO）雷达，提出了一种基于联合对角化的圆信号与严格非圆（NC）信号混合源二维（2D）出发角（DOD）和二维到达角（DOA）估计方法。通过充分利用L型MIMO阵列结构在接收阵列处获得扩展虚拟阵列，我们首先将接收数据向量与其共轭对应向量相结合以构建新的数据向量，然后采用一种类似旋转不变技术估计信号参数（ESPRIT）的方法，通过对基于NC的方向矩阵进行联合对角化来估计DOD和DOA，该方法能够自动对四维（4D）角度参数进行配对，并解决常见一维（1D）DOD和DOA的角度模糊问题。此外，分析了所提算法的渐近性能，并推导了闭式随机克拉美罗界（CRB）表达式。仿真结果表明，所提算法优于现有算法，结果接近理论基准。
