Target state estimation with Doppler-only sensors has attracted a lot of attention due to its wide potential applications in target localization and tracking. While existing Doppleronly tracking methods rely on the assumption that Doppler sensors have been correctly registered, in many practical cases there can be significant registration errors which imply measurement biases and thus performance degradation in target state estimation. Motivated by this issue, the present paper addresses the problem of jointly estimating target state and sensor biases based on Doppler-only measurements. The proposed method consists of two phases, i.e., (1) raw estimation of the target state without considering sensor biases, followed by (2) a bias compensation step that relies on linearization of the measurement function and joint estimation of target state-sensor biases via a least square method. The Cramer-Rao lower bound (CRLB) in estimating sensor biases is evaluated and the performance of the proposed method is also assessed via simulations.

Joint bias and target state estimation based on Doppler sensors / Matteo Tesori, Giorgio Battistelli, Luigi Chisci. - ELETTRONICO. - (2023), pp. 1-6. (Intervento presentato al convegno 2023 26th International Conference on Information Fusion (FUSION)).

Joint bias and target state estimation based on Doppler sensors

Matteo Tesori;Giorgio Battistelli;Luigi Chisci
2023

Abstract

Target state estimation with Doppler-only sensors has attracted a lot of attention due to its wide potential applications in target localization and tracking. While existing Doppleronly tracking methods rely on the assumption that Doppler sensors have been correctly registered, in many practical cases there can be significant registration errors which imply measurement biases and thus performance degradation in target state estimation. Motivated by this issue, the present paper addresses the problem of jointly estimating target state and sensor biases based on Doppler-only measurements. The proposed method consists of two phases, i.e., (1) raw estimation of the target state without considering sensor biases, followed by (2) a bias compensation step that relies on linearization of the measurement function and joint estimation of target state-sensor biases via a least square method. The Cramer-Rao lower bound (CRLB) in estimating sensor biases is evaluated and the performance of the proposed method is also assessed via simulations.
2023
FUSION 2023
2023 26th International Conference on Information Fusion (FUSION)
Matteo Tesori, Giorgio Battistelli, Luigi Chisci
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1329653
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