Particle filtering has a great potential for solving highly nonlinear and non-Gaussian estimation problems, generally intractable within a standard linear Kalman filtering based framework. However; the implementation of particle filters (PFs) is rather computationally involved, which nowadaysprevents them frompractical real-world application. A natural idea to make PFs feasible for "real-time" data processing is IO implement them on distributed multiprocessor computer systems. This paper presents three schemes for distributing the computations of generic particle filters, including resampling and. optionally, a Metropolis-Hastings (MH) step. Simulation results based on a maneuvering target tracking scenario show that distributed implementations can provide a promising solution 10 the steep computational burden incurred when using a large number of particles.

A new variable-structure (VS) Augmented Interacting Multiple Model (AIMM) technique is developed in the paper. Fixed-structure (FS) and VS AIMM algorithms using augmented constant velocity and augmented coordinated turn (ACT) models, are proposed. The ACT model includes the difference between the unknown current turn rate and its value assumed in the IMM models. Due to the estimated turn rate, significant self-adjusting abilities are provided to the designed AIMM algorithms, which give very good overall accuracy and consistency. Both AIMM algorithms are compared to a particular VS adaptive grid IMM algorithm. It is shown that the VS IMM algorithms possess better mobility, while the FS AIMM algorithm possesses better consistency. The VS AIMM algorithm provides the best estimation of the turn rate.

Many problems involve both decision and estimation where the performance of decision and estimation affects each other. They are usually solved by a two-stage strategy: decision-then-estimation or estimation-then-decision, which suffers from several serious drawbacks. A more integrated solution is preferred. Such an approach was proposed in [14]. It is based on a new Bayes risk as a generalization of those for decision and estimation, respectively. It is Bayes optimal and can be applied to a wide spectrum of joint decision and estimation (JDE) problems. In this paper, we apply that approach to the important problem of joint tracking and classification of targets, which has received a great deal of attention in recent years. A simple yet representative example is given and the performance of the JDE solution is compared with the traditional methods. Issues with design of parameters needed for the new approach are addressed.

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