Tracking systems

The tutorial is an overview of tracking and data fusion for surveillance systems with applications both to defense and civilian systems. It is divided into four parts: Part 1 - Filtering: Covers the topics related to state estimation for stochastic dynamic systems: optimal Bayesian estimator, Kalman filter, nonlinear filters (extended and unscented Kalman filter, Gaussian sum filter, particle filter); filters for maneuvering motion (Interactive multiple-model filter); Crame-Rao lower bounds for filtering. Part 2 - Data association: Due to the imperfections of a detector, the input to a tracking system may lack the target originated detections and often contains false detections (due to clutter). The data association component of a tracking system determines the origin of each input detection. The tutorial will cover techniques such as: gating, (global) nearest neighbor algorithm, (joint) probabilistic data association, multiple hypotheses tracking. In addition, the idea of using random sets for multi-target tracking will be introduced with a review of the PHD filter. Part 3 - Distributed multi-sensor tracking: Modern surveillance systems typically consist of multiple sensors connected by a communication network for a data exchange. While the network surveillance offers potentially more accurate and reliable performance, there are many practical issues that need to be resolved beforehand. The tutorial will provide answers to problems: how to choose the multi-sensor architecture, how to avoid a repeated use of the same information, how to perform distributed track association and track fusion, how to ensure proper multi-sensor alignment in time and space. Part 4 - Selected applications: The last part of the tutorial will cover several practical applications: ballistic missile tracking, GMTI radar tracking, tracking with hard constraints, angle-only tracking, tracking using TDoA measurements, track-before-detect, video tracking, tracking using an acoustic sensor networ- k, etc.