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The focus of this paper is the problem of recursive estimation for uncertain multisensor linear discrete-time systems. We herein propose a new suboptimal filtering algorithm. The basis of the proposed algorithm is the fusion formula for an arbitrary number of local Kalman filters. The proposed suboptimal filter fuses each local Kalman filter by weighted sum with scalar weights. This filter can be implemented in real time because the scalar weights do not depend on current observations in distinction to the optimal adaptive filter. The examples given, demonstrate the effectiveness and high precision of proposed filter. [ABSTRACT FROM AUTHOR]

The problem of estimating the intensity process of a doubly stochastic Poisson process is analyzed. Using covariance information, a recursive linear minimum mean-square error estimate is designed. Moreover, an efficient procedure for the computation of its associated error covariance is shown. The proposed solution becomes an alternative approach to the Kalman filter which is applicable under the only structural assumption that the intensity process to be estimated has a finite-dimensional covariance function. [ABSTRACT FROM AUTHOR]

In this paper, we present an interesting filtering algorithm to perform accurate estimation in jump Markov nonlinear systems, in case of multi-target tracking. With this paper, we aim to contribute in solving the problem of model based body motion estimation by using data coming from visual sensors. The Interacting Multiple Model (IMM) algorithm is specially designed to track accurately targets whose state and/or measurement (assumed to be linear) models changes during motion transition. However, when these models are nonlinear, the IMM algorithm must be modified in order to guarantee an accurate track. In order to deal with this problem, the IMM algorithm was combined with the Unscented Kalman Filter (UKF) [6]. Even if the later algorithm proved its efficacy in nonlinear model case; it presents a serious drawback in case of non Gaussian noise. To deal with this problem we propose to substitute the UKF with the Particle Filter (PF). To overcome the problem of data association, we propose the use of the JPDA approach, [12]. To reduce the computational burden of the latter technique, we choose firstly the most likely feasible events by applying a Genetic Algorithm; finally the derived algorithm from the combination of the IMM-PF algorithm and the GA-JPDA approach is noted GA-JPDA-IMM-PF. [ABSTRACT FROM AUTHOR]