Bayesian multi-object filtering with amplitude feature likelihood for unknown object SNR

In many tracking scenarios, the amplitude of target returns are stronger than those coming from false alarms. This information can be used to improve the multi-target state estimation by obtaining more accurate target and false alarm likelihoods. Target amplitude feature is well known to improve data association in conventional tracking filters, such as the Probabilistic Data Association (PDA) and Multiple Hypothesis Tracking (MHT), and results in better tracking performance of low SNR targets. The advantage of using the target amplitude approach is that targets can be identified earlier through the enhanced discrimination between target and false alarms. One of the limitations of this approach is that it is usually assumed that the SNR of the target is known. We show that the reliable estimation of the SNR requires a significant number of measurements and so we propose an alternative approach for situations where the SNR is unknown. We illustrate this approach in the context of multip...

• Type: Journal
• Year: 2010
• Where: TSP
• Authors: Daniel Clark, Branko Ristic, Ba-Ngu Vo, Ba-Tuong Vo
• Keywords: Amplitude, Artificial Intelligence, False Alarms, Target Amplitude Approach, TSP 2010