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We present a particle filter for multi-object tracking that is based on the ideas of the Approximate Bayesian Computation (ABC) paradigm. The main idea is to avoid the explicit computation of the likelihood function by means of simulation. For this purpose, a large amount of particles in the state space is simulated from the prior, transformed into measurement space, and then compared to the real...
This paper focuses on addressing the data fusion problems in asynchronous sensor networks using distribute particle filter (DPF). Generally, the type of the local information communicated between sensors and the time synchronization of the local information are two major issues for DPF algorithms, which have significant influence on fusion accuracy and communication requirements. To address these...
An efficient subspace-based two-step direction finding method is proposed for uniform linear arrays. It improves the estimation accuracy for small sample size and coherent sources by diminishing the undesirable terms and utilizing the Toeplitz structure of the sample covariance matrix. Furthermore, it works well even using single snapshot, therefore, it is a good candidate to track the direction-of-arrival...
In this paper, a new centralized algorithm is developed to estimate the registration error and target states jointly based on the generalized labeled multi-Bernoulli (GLMB) filter. The bias pseudo-measurements are calculated with the tracks generated by the GLMB filter. Then, the bias estimates are computed to compensate the measurements for multi-target tracking. Since the estimates of the sensor...
The paper deals with the nonlinear state estimation of stochastic dynamic systems with a special focus on coping with outliers appearing in the system. A new stochastic integration Student's-t filter is developed based on the generic Student's-t filter and assuming the density of random variables present in the model and the conditional density of the state be Student's-t distributed. For evaluation...
The distributed detection fusion is investigated for conditionally dependent sensor networks with channel errors. When the joint probability density functions of the sensor observations are dependent and high dimensional, it is known to be a challenging problem. This paper deals with this problem under Monte Carlo framework. The Bayesian cost function is approximated by Monte Carlo importance sampling...
The paper introduces a novel approach to an estimator design, the cooperative filter design, for state estimation of nonlinear systems. The approach is based on the idea of combining estimates of several different approximate (and thus sub-optimal) nonlinear filters, which are configured to perform the same task. Within the concept, two strategies are proposed, namely the cooperative estimation and...
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