Data compression, data fusion and Kalman filtering in wavelet packet sub-bands of a multisensor tracking system

In a multisensor target tracking system in which centralised Kalman filtering is employed, the amount of data to be transmitted from the sensors to the central processor demands a huge communication cost. In addition, the amount of data received by the central processor further imposes an enormous computational load on the Kalman filter. The authors propose the use of wavelet packet decomposition on the observed data vectors so that the insignificant sub-band components may be suppressed transmitted resulting in the communication cost. Furthermore, optimum fusion is applied to the sub-band components before the data vectors are reconstructed. Two tracking schemes, tracking by reconstructed compressed data (TRCD) and tracking by compressed sub-band data (TSCD), are proposed. In TRCD, Kalman filtering is applied to the reconstructed data vector, whereas in TCSD, Kalman filtering is only applied to the components in the dominant sub-band of the decomposition. Simulation results show that, in terms of communications and/or computation economy, these two schemes offer attractive alternatives without over-sacrificing performance