Coherent Long-Time Integration and Bayesian Detection With Bernoulli Track-Before-Detect

Authors:
Publisher website
Google Scholar
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.
Funding Information
  • Look Out
  • Defence and Security Accelerator
  • The Royal Navy and Defence Innovation Unit

This publication has 17 references indexed in Scilit: