CRLB for estimation of 3D sensor biases in spherical coordinates

Michael Kowalski; Djedjiga Belfadel; Yaakov Bar-Shalom; Peter Willett

doi:10.1117/12.2319069

27 April 2018 CRLB for estimation of 3D sensor biases in spherical coordinates

Michael Kowalski, Djedjiga Belfadel, Yaakov Bar-Shalom, Peter Willett

Proceedings Volume 10646, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVII; 106461S (2018) https://doi.org/10.1117/12.2319069
Event: SPIE Defense + Security, 2018, Orlando, FL, United States

Abstract

In order to carry out data fusion, it is crucial to account for the imprecision of sensor measurements due to systematic errors. This requires estimation of the sensor measurement biases. In this paper, we consider a 3D multisensor multitarget bias estimation approach for both additive and multiplicative biases in the measurements. Multiplicative biases can more accurately represent real biases in many sensors, however, they increase the complexity of the estimation problem. By converting biased measurements into pseudo-measurements of the biases it is possible to estimate biases separately from target state estimation. The conversion of the spherical measurements to Cartesian measurements, which has to be done using the unbiased conversion, is the key that allows estimation of the sensor biases without having to estimate the states of the targets of opportunity. The measurements provided by these sensors are assumed time-coincident (synchronous) and perfectly associated. We evaluate the Cram´er-Rao Lower Bound (CRLB) on the covariance of the bias estimates, which serves as a quantification of the available information about the biases.

Citation Download Citation

Michael Kowalski, Djedjiga Belfadel, Yaakov Bar-Shalom, and Peter Willett "CRLB for estimation of 3D sensor biases in spherical coordinates", Proc. SPIE 10646, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVII, 106461S (27 April 2018); https://doi.org/10.1117/12.2319069

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