Abstract
This paper proposes a new position tracking algorithm by integrating extended Kalman filter (EKF) and direction-of-arrival (DOA)-based geolocation into one factor graph (FG) framework. A distributed sensor network is assumed for detecting an anonymous target, where the process and observation equations in the state space model (SSM) are unknown. Importantly, the predicted state information can be utilized not only for filtering, but also for enhancing the observation process. To be specific, by taking the prediction into account as the a priori, a new FG scheme is proposed for GEolocation, denoted by FG-GE. The benefits are two-fold, compared with the conventional geolocation scheme which does not rely on the a priori information. First of all, significant performance improvement can be observed, in terms of the root mean square error (RMSE), when severe sensing errors are suddenly encountered. Furthermore, the proposed FG-GE can achieve dramatic reduction of computational complexity. In addition, this paper also proposes the use of a predicted Cramer-Rao lower bound (P-CRLB) to dynamically estimate the observation error variance, which demonstrates more robust tracking performance than that with only fixed average variance approximation.
Highlights
The roles to be played by wireless cellular networks are experiencing a paradigm shift from mobile communications to more dedicated infrastructure-supporting applications
1) A new DOA-based tracking system is proposed by integrating extended Kalman filter (EKF) and geolocation into one factor graph (FG) framework; 2) By utilizing the state prediction as the a priori information, the impact of sudden sensing errors can be eliminated; 3) The proposed FG-GE exhibits much lower computational complexity than the conventional scheme; 4) The robustness of tracking is further enhanced, by estimating the observation error variance for FG-EKF with the proposed predicted Cramer-Rao lower bound (P-Cramer Rao lower bound (CRLB)) in real time
The predicted state information obtained from EKF is used for filtering, and for observation, i.e, as the a priori information of FG-GE
Summary
The roles to be played by wireless cellular networks are experiencing a paradigm shift from mobile communications to more dedicated infrastructure-supporting applications. A predicted CRLB (P-CRLB) is calculated in this paper, where the predicted target position based on the result of previous timing is used With this technique, the proposed FG-EKF is shown to achieve higher robustness than conventional schemes which assume only fixed estimation of the observation noise variance. 1) A new DOA-based tracking system is proposed by integrating EKF and geolocation into one FG framework; 2) By utilizing the state prediction as the a priori information, the impact of sudden sensing errors can be eliminated; 3) The proposed FG-GE exhibits much lower computational complexity than the conventional scheme; 4) The robustness of tracking is further enhanced, by estimating the observation error variance for FG-EKF with the proposed P-CRLB in real time.
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