targettracking

Target tracking is the process of estimating the evolving state of a moving target over time using observations from one or more sensors. The state typically includes position, velocity, and sometimes acceleration or other attributes, while the measurements may be noisy and incomplete due to clutter or missed detections. Tracking aims to produce a consistent sequence of state estimates that explain the observed data and support tasks such as prediction, control, or alerting. Applications span radar, sonar, lidar, video surveillance, autonomous vehicles, and robotics.

Core elements of target tracking include a state-space model, a measurement model, and an inference algorithm. The state evolves according to a dynamical model, and measurements provide noisy evidence about the current state. Because noise, nonlinearities, and ambiguities are common, probabilistic filters are widely used. Kalman filters handle linear-Gaussian problems, while extended Kalman filters and unscented Kalman filters address nonlinearities. Particle filters support highly nonlinear and non-Gaussian scenarios. Data association techniques, such as multiple hypothesis tracking (MHT) and joint probabilistic data association (JPDA), help assign measurements to existing tracks and manage track continuity.

Track management involves initializing, maintaining, updating, and deleting tracks, as well as handling occlusions and clutter. In computer vision, tracking can also involve appearance models and dedicated trackers that operate on image data.

Challenges in target tracking include clutter, false alarms, occlusions, measurement dropouts, and the need for real-time performance. Evaluation focuses on accuracy, robustness, latency, and the ability to preserve track identities across time.