Detection and tracking targets under low SNR
Naima Amrouche, Ali Khenchaf, Daoud Berkani · 2017
Target tracking in the time is a difficult problem of dynamic state estimation; the problem is more complex in a low signal to noise ratio (SNR) or in high clutter conditions. An efficient approach for target detection and tracking under low signal to noise ratio (SNR) conditions is the Track-Before-Detect (TBDF) filter. This paper proposes to use the TBD method based on particle filtering includes unthresholded data and a binary variable of the existence of the target in the estimating target state for two motion model. Using simulations it is shown that with this method, it is possible to detect and track the target in low signal to noise ratio.