Target aided online sensor localisation in bearing only clusters

In this work, we consider a network of bearing only sensors in a surveillance scenario. The processing of target measurements follow a two-tier architecture: The first tier is composed of centralised processing clusters whereas in the second tier, cluster heads perform decentralised processing. We are interested in the first tier problem of locating peripheral sensors relative to their cluster head. We mainly exploit target measurements received by the cluster head in a parameter estimation setting which involves Sequential Monte Carlo methods, and is known to have many difficulties in practice, including particle deficiency, sensitivity to initialisation, and high computational complexity. These difficulties are exacerbated by the bearing-only modality which provides a relatively poor target observability. We propose an online solution through Bayesian recursions on Junction Tree models of the posterior which partition the problem into fixed size subproblems and hence provides scalability with the number of sensors. We use the received signal strength as noisy range measurements to improve the robustness and accuracy of our algorithm. We demonstrate its efficacy with an example.