Particle filter based detection for tracking

TLDR: A new method to perform detection and tracking of a possible target in noise based on the basis of the standard measurements but on the raw radar video data, better suited for tracking weak targets in noise than the classical method.

Abstract: We present a new method to perform detection and tracking of a possible target in noise. We perform tracking not on the basis of the standard measurements but on the raw radar video data. Detection then is based upon the a posteriori information, i.e., the probability density of the state given these past measurements (in this case video data). This way of data processing/tracking is also referred to as track before detect (TBD) for obvious reasons. An advantage of this method over classical tracking is that in this TBD approach the decision whether a target is present or not is based on integrated and kinematically correlated energy. This method is better suited for tracking weak targets in noise than the classical method. As this problem statement leads to a nonlinear non-Gaussian filtering problem classical filtering methods (e.g. Kalman filtering) will result in poor performance. A particle filter is used to deal with the nonlinearities and the non-Gaussian nature of the noise. The same particle filter output is also used to perform detection based on a likelihood ratio test.