The purpose of a tracking algorithm is to associate data measured by one or more (moving) sensors to moving objects in the environment. The state of these objects that can be estimated with the tracking process depends on the type of data that is provided by these sensors. It is discussed how the tracking algorithm can adapt itself, depending on the provided data, to improve data association. The core of the tracking algorithm is an extended Kalman filter using multiple hypotheses for contact to track association. Examples of various sensor suites of radars, electro-optic sensors and acoustic sensors are presented.
The use of the Radon transform and the Wigner-Radon power spectrum for ISAR motion compensation is described. It is shown that the local Radon power spectrum is closely related to the Cohen's class of quadratic time-frequency representations in a similar way as the Radon and Fourier transform are related. The peak of the local Radon transform is used as a measure for the velocity towards the radar of a moving target. The velocity estimate can be used to align the range profiles and perform target radial motion correction. Another application of the Radon transformation is the correction for time-variation of Doppler frequency of the signal during the Coherent Processing Interval. The Radon transform of the cross-range time-frequency representation of the signal is used for focusing an ISAR image that has been blurred due to non-uniform target rotation.
Fusion of radar and EO-sensors is investigated for the purpose of surveillance in littoral waters is. All sensors are considered to be co-located with respect to the distance, typically 1 to 10 km, of the area under surveillance. The sensor suite is a coherent polarimetric radar in combination with a set of camera's sensitive to visible light, near infrared, mid infrared and far infrared. Although co-located, the sensors are dissimilar and not necessarily synchronized. A critical aspect for beneficial fusion in this application is correct association of information from these sensors. Various architectures are considered and it will be argued that a fuse while track algorithm is the most suitable algorithm in this case. Discussed is how such an algorithm is designed and applied. To improve association reliability also non-kinematic features of both sensor types are considered. Investigated in particular is, which features from contacts measured with the polarimetric radar and the EO-sensors are correlated. These features and their correlations are incorporated in the tracking process. Preliminary results are shown.
Area surveillance for guarding and intruder detection with a combined camera radar sensor is considered. This specific sensor combination is attractive since complementary information is provided by the respective elements. Thus, a more complete description of objects of interest can be obtained. Several strategies to fuse the data are discussed. Results obtained with live experiments are presented. When compared to camera only, a significant reduction of the number of false tracks is achieved.