Kalman filtering: theory and practice
Kalman filtering: theory and practice
Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
Bearings-only tracking of manoeuvring targets using particle filters
EURASIP Journal on Applied Signal Processing
Posterior Cramer-Rao bounds for discrete-time nonlinear filtering
IEEE Transactions on Signal Processing
Least squares algorithms for time-of-arrival-based mobile location
IEEE Transactions on Signal Processing
Brief paper: Detection and estimation for abruptly changing systems
Automatica (Journal of IFAC)
EURASIP Journal on Advances in Signal Processing - Special issue on advances in multidimensional synthetic aperture radar signal processing
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This paper investigates the problem of manoeuvring target tracking, using multiple bistatic range and bistatic range-rate measurements from a number of bistatic radars, consisting a multistatic radar. The contribution of the paper is twofold. First, a theoretical lower bound of the performance error is derived and analyzed. Second, four filtering algorithms are proposed, presented and compared to the theoretical bound. The proposed algorithms include: (i) sequential iterated extended Kalman filter (SI-EKF), (ii) iterated unscented Kalman filter (I-UKF), (iii) interactive multiple model algorithm combined with sequential iterated extended Kalman filter (IMM-SI-EKF), and (iv) interactive multiple model algorithm combined with iterated unscented Kalman filter (IMM-I-UKF). Monte Carlo simulation demonstrates the track accuracy performance and computational complexity of each algorithm for manoeuvring targets.