Abstract
In this paper, we consider a variant of the extended target tracking (ETT) problem, namely the multiel- lipsoidal ETT problem. In multiellipsoidal ETT, target extent is represented by multiple ellipses, which correspond to the origin of the measurements on the target surface. The problem involves estimating the target’s kinematic state and solving the association problem between the measurements and the ellipses. We cast the problem in a sequential Monte Carlo (SMC) framework and investigate different marginalization strategies to find an efficient particle filter. Under the known extent assumption, we define association variables to find the correct association between the measurements and the ellipses; hence, the posterior involves both discrete and continuous random variables. By expressing the measurement likelihood as a mixture of Gaussians we derive and employ a marginalized particle filter for the independent association variables without sampling the discrete states. We compare the performance of the method with its alternatives and illustrate the gain in nonstandard marginalization.
Highlights
Conventional target tracking methods consider point targets that generate at most one measurement per scan [1, 2]
With the evolution of sensor technology, the focus of the target tracking literature shifted from point target tracking to extended target tracking (ETT) algorithms, which aim at estimating target extent simultaneously with the kinematic state using a set of measurements per scan
In this paper, a multiellipsoidal ETT model is studied in the sequential Monte Carlo (SMC) framework under the assumption of known extent
Summary
Conventional target tracking methods consider point targets that generate at most one measurement per scan [1, 2]. With the evolution of sensor technology, the focus of the target tracking literature shifted from point target tracking to extended target tracking (ETT) algorithms, which aim at estimating target extent simultaneously with the kinematic state using a set of measurements per scan. The most popular ETT model is based on random matrices, which are used to define elliptical target extents [6, 7]. More recent contributions involve detailed representations of complex shapes These algorithms are based on random hypersurface models that can represent so-called star convex shapes [8, 9]. Recent methods use the Gaussian process-based random hypersurface models to represent target extents [10, 11]. The random matrix models [6, 7] can be extended to multiellipsoidal target extents to obtain a more detailed representation of the extent [12]. We will illustrate the advantages of the nonstandard marginalization approach by comparing it with its alternatives in simulations
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
