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Proceedings Paper

A minimalist approach to bias estimation for passive sensor measurements with targets of opportunity
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Paper Abstract

In order to carry out data fusion, registration error correction is crucial in multisensor systems. This requires estimation of the sensor measurement biases. It is important to correct for these bias errors so that the multiple sensor measurements and/or tracks can be referenced as accurately as possible to a common tracking coordinate system. This paper provides a solution for bias estimation for the minimum number of passive sensors (two), when only targets of opportunity are available. The sensor measurements are assumed time-coincident (synchronous) and perfectly associated. Since these sensors provide only line of sight (LOS) measurements, the formation of a single composite Cartesian measurement obtained from fusing the LOS measurements from different sensors is needed to avoid the need for nonlinear filtering. We evaluate the Cramer-Rao Lower Bound (CRLB) on the covariance of the bias estimate, i.e., the quantification of the available information about the biases. Statistical tests on the results of simulations show that this method is statistically efficient, even for small sample sizes (as few as two sensors and six points on the trajectory of a single target of opportunity). We also show that the RMS position error is significantly improved with bias estimation compared with the target position estimation using the original biased measurements.

Paper Details

Date Published: 30 September 2013
PDF: 10 pages
Proc. SPIE 8857, Signal and Data Processing of Small Targets 2013, 88570D (30 September 2013); doi: 10.1117/12.2026897
Show Author Affiliations
Djedjiga Belfadel, Univ. of Connecticut (United States)
Richard W. Osborne, Univ. of Connecticut (United States)
Yaakov Bar-Shalom, Univ. of Connecticut (United States)

Published in SPIE Proceedings Vol. 8857:
Signal and Data Processing of Small Targets 2013
Oliver E. Drummond; Richard D. Teichgraeber, Editor(s)

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