Track maneuvering targets using various tracking filters. The example shows the difference between filters that use a single motion model and multiple motion models.

Illustrates the use of particle filters and Gaussian-sum filters to track a single object using range-only measurements.

Track objects when measurements wrap in angle, range, or range rate.

Illustrates how to track targets using passive angle-only measurements from a single sensor. Passive angle-only measurements contain azimuth and elevation of a target with respect to the sensor. The absence of range measurements makes the problem challenging as the targets to be tracked are fully observable only under certain conditions.

Handle out-of-sequence measurements using the retrodiction technique at the filter level.

Smooth state estimates of a target using the smooth object function. Smoothing is a technique to refine previous state estimates using the up-to-date measurements and the state estimate information. In this example, you will learn how to improve previously corrected estimates from an Interacting Multi-Model (IMM) filter by running a backward recursion, which produces smoothed and more accurate state estimates. In the first section, you implement a smooth algorithm to smooth the trajectory of a turning car. In the remainder of this example, you perform smoothing on several highly maneuvering aircraft trajectories, taken from the Benchmark Trajectories for Multi-Object Tracking example.

Tune process noise and measurement noise of a constant velocity Kalman filter

Tune a tracking filter and improve the tracking performance of the tracker.

Tune a tracking filter to track maneuvering targets.