Coherent long-time integration and Bayesian detection with Bernoulli
track-before-detect
Abstract
We consider the problem of detecting small and manoeuvring objects with
staring array radars. Coherent processing and long-time integration are
key to addressing the undesirably low signal-to-noise/background
conditions in this scenario and are complicated by the object
manoeuvres. We propose a Bayesian solution that builds upon a Bernoulli
state space model equipped with the likelihood of the radar data cubes
through the radar ambiguity function. Likelihood evaluation in this
model corresponds to coherent long-time integration. The proposed
processing scheme consists of Bernoulli filtering within expectation
maximisation iterations that aims at approximately finding complex
reflection coefficients. We demonstrate the efficacy of our approach in
a simulation example.