National Laboratory of Radar Signal Processing, Xidian University, Xi'an 710071, China.
Collaborative Innovation Center of Information Sensing and Understanding, Xidian University, Xi'an 710071, China.
Sensors (Basel). 2018 Aug 13;18(8):2650. doi: 10.3390/s18082650.
This paper proposes two novel phase-based algorithms for the passive localization of a single source with a uniform circular array (UCA) under the case of measuring phase ambiguity based on two phase difference observation models, which are defined as the unambiguous-relative phase observation model (UARPOM) and the ambiguous-relative phase observation model (ARPOM). First, by analyzing the varying regularity of the phase differences between the adjacent array elements of a UCA, the corresponding relationship between the phase differences and the azimuth and elevation angle of the signal is derived. Based on the two phase observation models, two corresponding novel algorithms, namely, the phase integral accumulation and the randomized Hough transform (RHT), are addressed to resolve the phase ambiguity. Then, by using the unambiguous phase differences, the closed-form estimates of the azimuth and elevation angles are determined via a least squares (LS) algorithm. Compared with the existing phase-based methods, the proposed algorithms improve the estimation accuracy. Furthermore, our proposed algorithms are more flexible for the selection of an array radius. Such an advantage could be applied more broadly in practice than the previous methods of ambiguity resolution. Simulation results are presented to verify the effectiveness of the proposed algorithm.
本文提出了两种新颖的基于相位的算法，用于在基于两种相位差观测模型（即无模糊相对相位观测模型（UARPOM）和模糊相对相位观测模型（ARPOM））测量相位模糊的情况下，用均匀圆形阵列（UCA）对单个源进行无源定位。首先，通过分析 UCA 相邻阵元之间相位差的变化规律，推导出相位差与信号方位角和俯仰角的对应关系。基于这两种相位观测模型，分别提出了两种相应的新颖算法，即相位积分累加和随机霍夫变换（RHT），以解决相位模糊问题。然后，利用无模糊相位差，通过最小二乘（LS）算法确定方位角和俯仰角的闭式估计。与现有的基于相位的方法相比，所提出的算法提高了估计精度。此外，与之前的解模糊方法相比，所提出的算法在阵元半径的选择上更加灵活。这种优势在实际应用中比以前的解模糊方法具有更广泛的适用性。仿真结果验证了所提出算法的有效性。
