Abstract
In previous work, we discussed an efficient form of predetection fusion for use as a preprocessing step before tracking on data sets with large sensor networks of low-quality sensors, with particular eye to application in multistatic sonar. This 2-D version (position measurements only) was compared against an optimal (slow) technique. In this work, we present the 4-D (using position and Doppler measurements) and 5-D versions [using position, Doppler, and also aspect-dependent signal-to-noise ratio (SNR) measurements] of predetection fusion. We demonstrate that improved results, in the sense of root mean square error (RMSE) and number of declared targets-and consequently better tracking results-are possible when Doppler measurements and SNR information are incorporated into our algorithm.
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