Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Data Fusion and Sensor Management: A Decentralized Information-Theoretic Approach
Data Fusion and Sensor Management: A Decentralized Information-Theoretic Approach
Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
Model-driven data acquisition in sensor networks
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Visual Tracking by Continuous Density Propagation in Sequential Bayesian Filtering Framework
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-Camera Tracking with Adaptive Resource Allocation
International Journal of Computer Vision
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
A Bayesian approach to tracking multiple targets using sensorarrays and particle filters
IEEE Transactions on Signal Processing
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We propose a sequential and hierarchical Monte Carlo Bayesian framework for state estimation using multi-modal data. The proposed hierarchical particle filter (HPF) estimates the global filtered posterior density of the unknown state in multiple stages, by partitioning the state space and the measurement space into lower dimensional subspaces. At each stage, we find an estimate of one partition using the measurements from the corresponding partition, and the information from the previous stages. We demonstrate the proposed framework for joint initiation, termination and tracking of multiple targets using multi-modal sensors. Here, the multi-modal data consists of the measurements collected from a radar, an infrared camera and a human scout. We compare the performance of the proposed HPF with the performance of a standard particle filter that uses linear opinion (SPF-LO), independent opinion (SPF-IO), and independent likelihood (SPF-IL) for data fusion. The results show that HPF improves the robustness of the tracking system in handling the initiation and termination of targets and provides a lower mean-squared error (RMSE) in the position estimates of the targets that maintain their tracks. The RMSE in the velocity estimates using the HPF was similar to the RMSE obtained using SPF based methods.