A classical rotating air surveillance radar system detects target echoes against a background of noise.
The role of the radar tracker is to monitor consecutive updates from the radar system (which typically occur once every few seconds, as the antenna rotates) and to determine those sequences of plots belonging to the same target, whilst rejecting any plots believed to be false alarms.
In addition, the radar tracker is able to use the sequence of plots to estimate the current speed and heading of the target.
A multisensor tracker extends the concept of the multiradar tracker to allow the combination of reports from different types of sensor - typically radars, secondary surveillance radars (SSR), identification friend or foe (IFF) systems and electronic support measures (ESM) data.
A radar track will typically contain the following information: In addition, and depending on the application or tracker sophistication, the track will also include: There are many different mathematical algorithms used for implementing a radar tracker, of varying levels of sophistication.
In the real world, a radar tracker typically faces a combination of all of these effects; this has led to the development of an increasingly sophisticated set of algorithms to resolve the problem.
A variation of JPDA called JIPDA also estimates target existence, therefore handling plot-to-track, initialization, and maintenance.
Common approaches to deciding on whether to terminate a track include: In this important step, the latest track prediction is combined with the associated plot to provide a new, improved estimate of the target state as well as a revised estimate of the errors in this prediction.
The mathematics of the Kalman filter is therefore concerned with propagating these covariance matrices and using them to form the weighted sum of prediction and measurement.
In situations where the target motion conforms well to the underlying model, there is a tendency of the Kalman filter to become "overconfident" of its own predictions and to start to ignore the radar measurements.
It is therefore common practice when implementing the filter to arbitrarily increase the magnitude of the state estimate covariance matrix slightly at each update to prevent this.
The MHT is designed for situations in which the target motion model is very unpredictable, as all potential track updates are considered.
As a result, it might handle multiple parts of the radar tracker algorithm, i.e. the plot-to-track, the smoothing, and the initialization.
IMM uses two or more Kalman filters which run in parallel, each using a different model for target motion or errors.
The IMM forms an optimal weighted sum of the output of all the filters and is able to rapidly adjust to target maneuvers.
Although conceptually simple, the filter can easily diverge (i.e. gradually perform more and more badly) if the state estimate about which the equations are linearised is poor.
The UKF attempts to improve on the EKF by removing the need to linearise the measurement and state equations.
This approach then suffers none of the problems of divergence due to poor linearisation and yet retains the overall computational simplicity of the EKF.