Algorithm for unknown SNR estimation based on sequential Monte Carlo method in cluttered environment

In target tracking, radar or sonar sensors provide amplitude information as well as kinematic information, e.g., range and bearing. Considering the amplitude information with kinematic information, tracker can more effectively distinguish measurement origin in cluttered environment. The tracker utilizes the amplitude information in the form of signal to noise ratio (SNR). However, a major challenge comes from the fact that the SNR is often fluctuated according to the target's aspect and effective radar cross section. So the certain level of uncertainty of SNR should be reduced. Focused on the point, we propose a novel SNR estimation algorithm based on sequential Monte Carlo method. Finally, estimated SNR is applied to the probability data association filter with amplitude information. Simulation results demonstrate the effectiveness and high accuracy of the idea of exploiting SNR estimation in heavy cluttered environments.