Showing papers by "Simon Maskell published in 2012"

Maneuvering target tracking using an unbiased nearly constant heading model

Abstract: This paper addresses the problem of modeling maneuvering target motion in tracking applications. Moving targets typically follow deterministic straight-line or curved trajectories, with minor deviations due to random disturbances. As a result, modeling target motion typically involves the derivation of state transition functions based on the laws of kinematics, with the addition of uncertainty terms in the form of random noise to compensate for model mismatch. Although it is possible to construct quite accurate models, there is a trade-off between model simplicity (and, thus, ease of implementation) and model accuracy. In this paper, we present a model for target motion that is based on a Brownian description of the target's speed and heading, which allows the derivation of closed form expressions for the exact first two moments of the propagated probability density function of the target's state vector. We outline the design of tracking algorithms based on this model, and demonstrate its effectiveness in dealing with maneuvering targets based on simulations.

An application of Sequential Monte Carlo samplers: An alternative to particle filters for non-linear non-Gaussian sequential inference with zero process noise

Abstract: Particle filters are not applicable in sequential parameter estimation scenarios, ie scenarios involving zero process noise. Sequential Monte Carlo (SMC) samplers provide an alternative sequential Monte-carlo approximation to particle filters that can address this issue. This paper aims to provide a description of SMC samplers that is accessible to an engineering audience and illustrate the utility of SMC samplers through their application to a specific problem. The problem involves processing a stream of bearings-only measurements to perform localisation of a stationary tar get. The SMC sampler solution is shown to outperform an Extended and Unscented Kalman filter in nonlinear scenarios (as defined by a novel metric for nonlinearity that this paper describes). The SMC sampler offers a computational cost that is near-constant over time on average. Future work aims to investigate the utility of Approximate Bayesian Computation and apply the technique within a Simultaneous Localisation and Mapping context. (8 pages)