Multi Sensor Target Tracking
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Group-target Tracking
This book describes grouping detection and initiation; group initiation algorithm based on geometry center; data association and track continuity; as well as separate-detection and situation cognition for group-target. It specifies the tracking of the target in different quantities and densities. At the same time, it integrates cognition into the application. Group-target Tracking is designed as a book for advanced-level students and researchers in the area of radar systems, information fusion of multi-sensors and electronic countermeasures. It is also a valuable reference resource for professionals working in this field.
Multi-sensor Target Tracking
"Target tracking is a well-studied research topic with a vast array of applications. The basic idea is to track one or more targets of interest using data collected by one or more sensors. While a single sensor may provide enough data, it is more beneficial to establish a network of sensors that collaborate with each other. In this thesis, we study multi-sensor target tracking and present three manuscripts.In the first manuscript, we present a distributed bearings-only single-target particle filter. Unlike the existing literature, the proposed filter incorporates the Earth's curvature in the measurement model to provide more accurate bearing computation. Furthermore, we derive an approximate joint log-likelihood function to reduce the total communication overhead. In the second manuscript, we extend our work in the first manuscript and present two compression algorithms for distributed particle filters. The proposed algorithms construct a graph over the particles and exploit the resulting graph Laplacian matrix to encode the particle log-likelihoods. The proposed algorithms are not limited to any measurement models and can be incorporated in any generic particle filter. We also derive theoretical results showing that the proposed algorithms outperform existing methods at low communication overhead. In the third manuscript, we study data assignment in multi-target tracking. We propose two heuristic but computationally efficient algorithms for multi-sensor multi-target data assignment that can generate a number of likely target-measurement associations. We also implement these algorithms in a generalized labeled multi-Bernoulli filter to validate their performance." --
Target Tracking with Random Finite Sets
This book focuses on target tracking and information fusion with random finite sets. Both principles and implementations have been addressed, with more weight placed on engineering implementations. This is achieved by providing in-depth study on a number of major topics such as the probability hypothesis density (PHD), cardinalized PHD, multi-Bernoulli (MB), labeled MB (LMB), d-generalized LMB (d-GLMB), marginalized d-GLMB, together with their Gaussian mixture and sequential Monte Carlo implementations. Five extended applications are covered, which are maneuvering target tracking, target tracking for Doppler radars, track-before-detect for dim targets, target tracking with non-standard measurements, and target tracking with multiple distributed sensors. The comprehensive and systematic summarization in target tracking with RFSs is one of the major features of the book, which is particularly suited for readers who are interested to learn solutions in target tracking with RFSs. The book benefits researchers, engineers, and graduate students in the fields of random finite sets, target tracking, sensor fusion/data fusion/information fusion, etc.