Sensor array signal tracking using a data-driven window approach

In many practical source tracking applications, the interval of source stat ionarity may severely vary with time, so that array observations may contai n both almost stationary data blocks and nonstationary data intervals with rapidly moving sources. Moreover, typical situations may occur where some s ources move rapidly within the window exploited, whereas the motion of the other sources is weak. In such scenarios, the traditional fixed-window appr oach appears to be nonoptimal because it may lead to a very poor tracking p erformance. Below, we address the narrowband direction of arrival (DOA) tra cking problem using a new adaptive-window approach. In our technique, a sep arate data-driven window is used for each source of interest. The optimizat ion of window lengths is based on the bias-to-variance tradeoff. The compar ison of our approach with conventional fixed-window algorithms is presented showing that the underlying idea has an evident potential in nonstationary scenarios with rapidly moving sources. A natural price for the improved tr acking performance is a higher computational cost and the restriction of ou r approach by the scenarios with 'well-separated' sources. (C) 2000 Elsevie r Science B.V. All rights reserved.