Modified Gaussian inverse Wishart PHD filter for tracking multiple non-ellipsoidal extended targets

Use of the Gaussian inverse Wishart probability hypothesis density (GIW-PHD) filter has demonstrated promise as an approach used to track an unknown number of ellipsoidal extended targets. However, based on the assumption that the shape of a target is not ellipsoidal, the performance of the GIW-PHD filter will degrade. This study presents an improved GIW-PHD filter, called a non-ellipsoidal model GIW-PHD (NEM-GIW-PHD) filter, to solve the problem of tracking multiple non-ellipsoidal extended targets. First, an additional target shape estimating approach using B-spline curve is presented for use in the GIW-PHD filter to provide non-ellipsoidal shape parameters. Second, these shape parameters can be used to improve the likelihood function of the GIW-PHD filter. Thus, the identifiability of closely spaced targets with different shapes can be improved. Third, we present an improved shape selection partitioning approach to partition the measurements sets generated by the non-ellipsoidal targets. The simulation results show that the NEM-GIW-PHD filter can achieve higher accuracy than that with the standard GIW-PHD filter in tracking non-ellipsoidal extended targets. In addition, it can provide target shape estimations using the B-spline curve.