Risk-Sensitive Filters for Recursive Estimation of Motion From Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Target tracking with bearings — only measurements
Signal Processing
On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
Sigma point Kalman filter for bearing only tracking
Signal Processing - Special section: Multimodal human-computer interfaces
Posterior Cramer-Rao bounds for discrete-time nonlinear filtering
IEEE Transactions on Signal Processing
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A novel particle implementation of risk-sensitive filters (RSF) for nonlinear, non-Gaussian state-space models is presented. Though the formulation of RSFs and its properties like robustness in the presence of parametric uncertainties are known for sometime, closed-form expressions for such filters are available only for a very limited class of models including finite state-space Markov chains and linear Gaussian models. The proposed particle filter-based implementations are based on a probabilistic re-interpretation of the RSF recursions. Accuracy of these filtering algorithms can be enhanced by choosing adequate number of random sample points called particles. These algorithms significantly extend the range of practical applications of risk-sensitive techniques and may also be used to benchmark other approximate filters, whose generic limitations are discussed. Appropriate choice of proposal density is suggested. Simulation results demonstrate the performance of the proposed algorithms.