SPIE - Education Data Fusion and Kalman Filtering for Object Tracking with Multiple Radar Sensors SC1243

This course describes target tracking and state estimation methods commonly associated with multisensor data fusion. The process usually begins with a decision to apply a sensor-data driven or target-track driven approach for estimating a target’s true state, followed by selection of data or track correlation and association techniques. Several techniques for data and track association are introduced including the deterministic Nearest Neighbor (NN) and Global NN algorithms, and the probabilistic procedures consisting of Joint Probabilistic Data Association, Deferred Decision Multiple Hypothesis Tracking, Track Splitting, and Maximum Likelihood. Position, kinematic, and attribute estimation are discussed for combining measurement data to improve estimates of position, velocity, acceleration, and to initiate target tracks. Subsequent sections of the course introduce radar tracking system functions and design constraints; attributes of radar detections, measurements, and tracks; state space and coordinate conversion procedures required in multisensor tracking systems; multiple-sensor registration and its impact on tracking accuracy; and the sequential probability ratio test for track initiation. The next units define Kalman filtering as an exceptional case of the Bayes filter that estimates the target’s true state at the predicted time of the next observation using a linear combination of a prior state estimate and a weighted difference between an actual noisy measurement and a measurement prediction. The Kalman filter equations, filtering process, filter initialization procedures, and the error covariance and Kalman filter recursive equations are derived. A discussion of the need and methods for maintaining the Kalman gain at a sufficiently large value is provided and models for the process noise are introduced. Alternatives to the Kalman filter are noted for application to nonlinear systems. When a tracked object engages in a maneuver, it is often necessary to introduce additional kinematic models that account for the possible maneuvers. Thus, Interacting Multiple Models are discussed as a method to treat this occurrence. The concluding sections of the course address data fusion and track management options, maturity of data fusion systems, and continuing challenges in fusion system assessment.