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In this paper, we present a new approach for onods [5], [6], [7], for tackling the target tracking and data line joint detection and tracking for multiple targets. We association problems. The former approaches essentially combine a deterministic clustering algorithm for target deadopt the classical methods like the extended Kalman Filter tection with a sequential Monte Carlo method for multiple[8] (EKF) for multitarget state estimation, whose tracking target tracking. The proposed approach continuously monperformance is known to be limited by the linearity of the itors the appearance and disappearance of a set of regions data models. On the contrary, the latter approaches are able of interest for target detection within the surveillance reto perform well even when the data models are nonlinear gion. No computational effort for target tracking will be and non-Gaussian. However, almost all of these methods expended unless these regions of interest are persistently assume that the knowledge of the true targets, including detected. In addition, we also integrate a very efficient 2-D when and where they appear and disappear, is given. Acdata assignment algorithm into the sampling method for the cordingly, methods relying on this unrealistic assumption data association problem. The proposed approach is appliare not practical for real-life applications.

This paper addresses the application of sequential importance sampling SIS schemes to tracking DOAs of an unknown number of sources, using a passive array of sensors. This proposed technique has signi cant advantages in this application, including the ability to detect a changing number of signals at arbitrary times throughout the observation period, and that the requirement for quasi-stationarity o ver a limited interval may be relaxed.We propose the use of a reversible jump MCMC 1 step to enhance the statistical diversity of the particles. This step also enables us to introduce two n o vel moves which signi cantly enhance the performance of the algorithm when the DOA tracks cross. The superior performance of the method is demonstrated by examples of application of the particle lter to sequential tracking of the DOAs of an unknown and non-stationary number of sources, and to a scenario where the targets cross. Our results are compared to the PASTd method 2 . *Permission to publish abstract separately is granted. J. Reilly, corresponding author: ph: 905 525 9140 x22895, fax: 905 521 2922,

In this paper, we present a simulation-based method for multitarget tracking and detection using sequential Monte Carlo (SMC), or particle filtering (PF) methods. The proposed approach is applicable to nonlinear and nonGaussian models for the target dynamics and measurement likelihood, where the environment is characterised by high clutter rate and low detection probability. The number of targets is estimated by continuously monitoring the events being represented by the regions of interest (ROIs) in the surveillance region. It follows that the proposed approach utilises the sequential importance sampling filter for recursive target state estimation, in conjunction with a 2-D data assignment method for measurement-to-target association. Computer simulations are also included to demonstrate and evaluate the performance of the proposed approach.

In this paper, we present a new approach for online joint detection and tracking for multiple targets, using sequential Monte Carlo methods. We first use an observation clustering algorithm to find some regions of interest (ROIs), and then propose to initiate a new target or remove an existing track, based on the persistence information of these ROIs over time. In addition, we also integrate a very efficient 2-D data assignment algorithm into the sampling method for the data association problem. Computer simulations demonstrate that the proposed approach is robust in performing joint detection and tracking for multiple targets even though the environment is hostile in terms of a high clutter rate and a low target detection probability.