This paper presents a novel approach to path planning for multi-platform bearings-only tracking of a target in the presence of epistemic detection uncertainty. Instead of using the traditional probabilistic framework, we propose a solution with a coordinated intelligent sensor platform motion control strategy in the possibilistic framework, offering a viable and robust alternative for improved tracking performance. We use track fusion of possibilistic Bernoulli filter implemented with Gaussian-max models to integrate information gathered by multiple platforms. The reward function for intelligent platform motion control is constructed using the concept of possibilistic entropy. The tracking performance of the proposed solution is evaluated using selected metrics via simulations.

Consider the problem of maritime surveillance using a high-resolution airborne radar for the detection and tracking of small surface targets. This is a challenging problem as the sea clutter is spiky with a non-Gaussian amplitude distribution and contains both temporally and spatially varying characteristics. As a possible solution, we have recently proposed a Bayesian track-before-detect algorithm, which assumes a compound K-distributed clutter model with Swerling 1 target fluctuations. This paper considers a suitable modification of this algorithm to work in the range-Doppler domain and evaluates its performance on real datasets collected by the Defence Science and Technology Group’s (DSTG) Ingara X-band radar.

The paper formulates the solution to area search for targets in the framework of possibility theory. The rationale is that the required measurement model parameters, such as the probability of detection and/or the probability of false alarm, are rarely known as precise values. Possibility theory was developed for quantitative modelling of and reasoning with epistemic uncertainty. It provides an elegant Bayesian like solution to target area search. A reward function is proposed as an uncertainty measure which takes into account the epistemic uncertainty. The robustness of the proposed search algorithm is demonstrated by numerical results.

This paper proposes a robust multi-target tracking algorithm for uncertainty in dynamic motion modeling. To address this issue, the multi-target tracking problem is formulated under random finite set (RFS) framework with finite length memory filtering called receding horizon estimation (RHE). The proposed algorithm is based on the generalized labeled multi-Bernoulli (GLMB) filter which enables RHE for multi-target tracking. The proposed algorithm, a Receding Horizon GLMB (RH-GLMB) filter, is evaluated through a numerical example and visual tracking datasets where dynamic modeling uncertainty exists.