A model-based joint tracking and classification (JTC) method is proposed for narrowband radar with kinematic and radar cross section (RCS) measurements. The method is derived from the 3D scattering center model (3DSCM), which can construct an explicit relation between the aspect angle and the predicted RCS. To deal with the numerical problem in observation model, a modified likelihood function for RCS measurement is adopted under the assumption of additive Gaussian observation noise. The JTC processing is realized by sequential Monte Carlo (SMC) technique. Specifically, a bank of particle filters are used to obtain type-dependent target state and type estimates. Compared with the traditional JTC methods using low resolution sensor, the proposed method is free from the constraint that target classification has to rely on different maneuvering modes. Simulation results validate the effectiveness of the proposed method with maritime application scenario.
An effective target classification algorithm based on feature aided tracking (FAT) is proposed, using the length of target (target extent) as the classification information. To implement the algorithm, the Rao-Blackwellised unscented Kalman filter (RBUKF) is used to jointly estimate the kinematic state and target extent; meanwhile the joint probability data association (JPDA) algorithm is exploited to implement multi-target data association aided by target down-range extent. Simulation results under different condition show the presented algorithm is both accurate and robust, and it is suitable for the application of near spaced targets tracking and classification under the environment of dense clutters.