Application of wavelet packets theory in maneuver target tracking

Multiple-model Kalman Filter Bank (KFB) is the conventional method in the area of maneuver target tracking. However, the KFB method is often faced with the dilemma between noise sensitivity and the accuracy of the temporal and spatial location of the maneuver. The difficulty, of dealing with this problem is due to the fact that the maneuver information of a target is often submerged in the measurement noise, especially when the measurements are acquired in low SNR case. With the wavelet packets analysis theory, the primitive measurements can be decomposed into two parts in a resolution level One is the “trend” of the measurements which is related to the true course of the target´s moving track. The other is the “fluctuation” of the measurement which is related to the maneuver information of the target and the measurement noise. In the case of conventional KFB, measurements are processed in a single resolution level, thus, the noise components, the maneuver information components and the target location components of the measurements are weighted as the same to the KFB. With the wavelet packets theory, we can decompose the measurements into their trends and fluctuations in different resolution levels, thus KFB is applied respectively to the trends and the fluctuations So the measurement noise is easily attenuated with little or without attenuation of the location information anti maneuver information. Simulation tests and comparison between conventional KFB method and wavelet packets analysis method are also discussed in the paper to demonstrate practicality and advantage of the wavelet packets method