Neural net based variable structure multiple model reducing mode set jump delay

Variable structure multiple model (VSMM) is one of the most powerful algorithms for effectively tracking a single maneuvering target. Although VSMM is developed specifically to improve the interactive multiple model (MM) method focused to reducing computational cost and improving tracking performance, it presents an inherent limitation in the form of the presence of mode set jump delay (MJD). MJD as an undesirable phenomenon in VSMM is described and analyzed. In order to eliminate the MJD, a neural network based VSMM that automatically selects the optimal mode set as achieved by supervised training is proposed. Through representative simulations we show the proposed algorithm outperforming over the conventional digraph switching VSMM in terms of tracking error.