An efficient algorithm for three-dimensional passive tracking: milestone A
Date
2015
Authors
Badriasl, L.
Arulampalam, S.
Finn, A.
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Report
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In this report, we derive a novel batch Bayesian weighted instrumental variable estimator for the three-dimensional (3D) target motion analysis (TMA) problem using bearing and elevation measurements. Unlike most existing estimators based on instrumental variables, the proposed approach is able to incorporate a priori information in the estimation process. The prior knowledge is specified in terms of a Gaussian distribution with known mean and covariance. The performance of the proposed Bayesian algorithm is compared to that of the other techniques such as the extended Kalman filter, unscented Kalman filter, and shifted Rayleigh filter in different scenarios. Simulations show that the proposed estimator mostly outperforms the compared Bayesian approaches in different geometries, specially in challenging scenarios with high bearing and elevation rates.
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Copyright 2015 the authors