Direct Position Determination Algorithm Toward Coherent Signals in Low SNR Localization Scenarios

The direct position determination (DPD) algorithm, which combines the parameter estimation and position calculation procedures together, has attracted significant attention due to its good performance in low signal-to-noise ratio (SNR) situations. However, the high computing consumption and being only suitable for noncoherent signals make its application limited in real situations. To address these problems, we first propose to use the Kronecker theory to decompose the original signal into signal and noise subspaces, based on which a cost function is constructed, and thus, the localization is transformed into a nonconvex optimization problem. Compared with traditional decomposition algorithms, this processing can improve the freedom degree of linear arrays and address the challenge to identify the direct transmitting path in the case of coherent signals. Then, to solve the nonconvex optimization problem, an adaptive reverse particle swarm optimization (ARPSO) algorithm is proposed, in which the adaptive elite mutation (AEM) strategy is introduced into the traditional particle swarm optimization (PSO) algorithms to avoid obtaining the local optimal solution caused by local convergence. Moreover, we adopt a noninertial velocity (NIV) update scheme to guide the particle flight trajectories. This scheme accelerates the process of convergence, saving the computing overhead a lot. The experimental results show that our proposed decomposition algorithm performs well for coherent and incoherent signals, and the improved optimization algorithm outperforms existing algorithms in both precision and computing consumption.