A direction tracking algorithm based on the repulsive force model with low SNR in MIMO HF sky-wave radar

Abstract

In MIMO HF sky-wave radar, the spatial spectrum estimation technology is used to obtain the estimates of the Direction-Of-Departure (DOD) and the Direction-Of-Arrival (DOA) of signals of interest and the “undesired” signals for suppressing the multipath clutter of the instability ionosphere layer. However, in the case of low Signal-to-Noise Ratio (SNR), the biases of the estimates of the direction of the signals and clutter are relatively large. And the biases could not be calibrated by the conventional Particle Filter (PF) algorithm. Therefore, in this paper, a direction tracking algorithm based on the repulsive force model with low SNR in MIMO HF sky-wave radar is proposed to improve the accuracy of the direction tracking. Firstly, the MUSIC algorithm based on the Toeplitz approximation is used to obtain the directions of the signals, which are most often taken as the coherent sources for better resolution performance. Secondly, we develop the repulsive force model with Stochastic Differential Equations (SDE), and add it to the Markov Chain Monte Carlo Particle Filter (MCMC-PF) algorithm. We compare the azimuth estimates obtained by the MUSIC algorithm based on the Toeplitz approximation method using the PF algorithm with the repulsive force model and the PF algorithm without the repulsive force model. The simulation results demonstrate the effectiveness of this algorithm. The algorithm can be applied to DOD and DOA estimates of the azimuth and the elevation with the proper antenna array.