Particle filters are being used in a number of state estimation applications because of their capability to effectively solve nonlinear and non-Gaussian problems. However, they have high computational requirements and this becomes even more so in the case of multi target tracking, where data association is the bottleneck. In order to perform data association and estimation jointly, typically an augmented state vector, whose dimensions depend on the number of targets, is used in particle filters. As the number of targets increases, the corresponding computational load increases exponentially. In this case, parallelization is a possibility for achieving real-time feasibility in large-scale multitarget tracking applications. In this paper, we present an optimization-based scheduling algorithm that minimizes the total computation time for the bus-connected heterogeneous primary-secondary architecture. This scheduler is capable of selecting the optimal number of processors from a large pool of secondary processors and mapping the particles among the selected ones. A new distributed resampling algorithm suitable for parallel computing is also proposed. Furthermore, a less communication intensive parallel implementation of the particle filter without sacrificing tracking accuracy using an efficient load balancing technique, in which optimal particle migration among secondary processors is ensured, is presented. Simulation results demonstrate the tracking effectiveness of the new parallel particle filter and the speedup achieved using parallelization. 111To my mother, who sacrificed so much for my well-being, and to my brother and sister

Particle filters are being used in a number of state estimation applications because of their capability to effectively solve nonlinear and non-Gaussian problems. However, they have high computational requirements and this becomes even more so in the case of multi target tracking, where data association is the bottleneck. In order to perform data association and estimation jointly, typically an augmented state vector, whose dimensions depend on the number of targets, is used in particle filters. As the number of targets increases, the corresponding computational load increases exponentially. In this case, parallelization is a possibility for achieving real-time feasibility in large-scale multitarget tracking applications. In this paper, we present an optimization-based scheduling algorithm that minimizes the total computation time for the bus-connected heterogeneous primary-secondary architecture. This scheduler is capable of selecting the optimal number of processors from a large pool of secondary processors and mapping the particles among the selected ones. A new distributed resampling algorithm suitable for parallel computing is also proposed. Furthermore, a less communication intensive parallel implementation of the particle filter without sacrificing tracking accuracy using an efficient load balancing technique, in which optimal particle migration among secondary processors is ensured, is presented. Simulation results demonstrate the tracking effectiveness of the new parallel particle filter and the speedup achieved using parallelization. 111To my mother, who sacrificed so much for my well-being, and to my brother and sister

The joint target tracking and classification using target-to-sensor aspect-dependent Radar Cross Section (RCS) and kinematic data for multistatic sonar network is presented in this paper. The scattered signals measured from different orientations of a target may vary due to aspect-dependant RCS. A complex target may contain several dozen significant scattering centers and dozens of other less significant scatterers. Because of this multiplicity of scatterers, the net RCS pattern exhibits high variation with aspect angle. Thus, radar cross sections from multiple aspects of a target, which are obtained via multiple sensors, will help in accurately determining the target class. By modeling the deterministic relationship that exits between RCS and target aspect, both the target class information and the target orientation can be estimated. Kinematic data are also very helpful in determining the target class as it describes the target motion pattern and its orientation. The proposed algorithm exploits the inter-dependency of target state and the target class using aspect-dependent RCS and kinematic information in order to improve both the state estimates and classification of each target. The simulation studies demonstrate the merits of the proposed joint target tracking and classification algorithm based on aspect-dependant RCS and kinematic information.

In this paper, a new joint target tracking and classification technique based on Observable Operator Models (OOM) is considered. The OOM approach, which has been proposed as a better alternative to the Hidden Markov Model (HMM), is used to model the stochastic process of target classification. These OOMs afford both mathematical simplicity and algorithmic efficiency compared to HMM. Conventional classification techniques use only the feature information from target signatures. The proposed OOM based classification technique incorporates the target-to-sensor orientation together with a sequence of feature information from multiple sensors. The target-to-sensor orientation evolves over time and the target aspect is important in determining the target classes. The multi-aspect classification is modeled using OOM to handle unknown target orientation. This algorithm exploits the inter-dependency of target state and the target class, which improves both the state estimates and classification of each target. Measurement ambiguity is present in both kinematic and feature measurement and therefore, the OOM based classifier is integrated into the multiframe data association framework that is used to resolve measurement origin uncertainties. This technique enables one to overcome ambiguity in feature measurements while improving track purity. A two dimensional example demonstrates the merits of the proposed OOM based joint target tracking and classification algorithm.

The particle filter is an effective technique for target tracking in the presence of nonlinear system model, nonlinear measurement model or non-Gaussian noise in the system and/or measurement processes. However, the current particle filtering algorithms for multitarget tracking suffer from high computational requirements. In this paper, we present a new implementation of the particle filter, called the tagged particle filtering (TPF) algorithm, to handle multitarget tracking problems in an efficient manner. The TPF uses a separate sets of particles for each track. Here, each particle is associated with the closest (in terms of likelihoods) measurement. The particles for a particular track may form separate groups in terms of the measurements associated with them and they evolve independently in groups till two or more groups of particles are separated by a distance large enough to be called separate tracks. A decision is made as to which of the groups is to be retained. Since this algorithm keeps a separate set of particles for each track, the state estimation for individual tracks does not require any additional computation. Also, this algorithm is association free and target class information can be added to the state for feature aided tracking. Simulation results are obtained by applying this tracking filter to a spawning target scenario.