PROJECT TITLE :
This paper presents a Bayesian algorithm for joint detection and tracking in a very multitarget setting. Raw measurements are processed using the track-before-detect (TBD) framework. We initial establish a Bayesian recursion, which propagates a probability of target existence together with a target state likelihood density per delay/Doppler bin. In order to handle the nonlinearity of the observation model obtained for orthogonal frequency division multiplexing (OFDM)-based passive radar, a appropriate Gaussian mixture implementation is proposed.
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