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In a variety of problems, the number and state of multiple moving targets areunknown and are subject to be inferred from their measurements obtained by asensor with limited sensing ability. This type of problems is raised in avariety of applications, including monitoring of endangered species, cleaning,and surveillance. Particle filters are widely used to estimate target statefrom its prior information and its measurements that recently become available,especially for the cases when the measurement model and the prior distributionof state of interest are non-Gaussian. However, the problem of estimatingnumber of total targets and their state becomes intractable when the number oftotal targets and the measurement-target association are unknown. This paperpresents a novel Gaussian particle filter technique that combines Kalman filterand particle filter for estimating the number and state of total targets basedon the measurement obtained online. The estimation is represented by a set ofweighted particles, different from classical particle filter, where eachparticle is a Gaussian distribution instead of a point mass.

A Gaussian Particle Filter Approach for Sensors to Track Multiple Moving Targets