Wideband target tracking by using SVR-based sequential Monte Carlo method

In this work, a support vector regression (SVR) based sequential Monte Carlo method is presented to track wideband moving sources using a linear and passive sensor array for a signal model based on buffered data. The SVR method is employed together with a particle filter (PF) method to improve the PF tracker performance when a small sample set is available. SVR is used as a sample producing scheme for the current state vector. To provide a good approximation of the posterior density by means of improving the sample diversity, samples (particles) are drawn from an importance density function whose mean and covariance are calculated by using the pre-estimating state vector and the state vector's previous estimate. Thus, a better posterior density than the classical one can be obtained. Simulation results show that the method proposed in this work performs better than the classical one when a small sample set is available. Moreover, the results also show that a modified signal model that utilizes buffering data is superior to the signal model in Ng et al. [Application of particle filters for tracking moving receivers in wireless communication systems, in: IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC), Rome, Italy, June 2003, pp. 575–579].