Multisensor Detection in Randomly Arriving Impulse Interference using the Hough Transform

In this chapter, several advanced detection algorithms for Track-Before-Detect (TBD) procedures using the Hough Transform (HT) are proposed and studied. The detection algorithms are based on the scheme described in (Carlson et al., 1994) to use the Hough transform for simultaneous target detection and trajectory estimation. The concept described in (Carlson et al., 1994) accepts that a target moves within a single azimuth resolution cell, and the distance to the target is estimated for several last scans forming the (r-t) data space. The Hough transform maps all points from the (r-t) space into the Hough space of patterns. The association with a particular pattern is done by thresholding the Hough parameter space with a predetermined threshold. In order to enhance the target detectability in conditions of Randomly Arriving Impulse Interference (RAII), a CFAR processor is proposed to be used for signal detection in the (r-t) space instead of the detector with a fixed threshold as it is suggested in (Carlson et al., 1994). The results obtained show that such a Hough detector works successfully in a noise environment. In real-time and realistic applications, however, when the two target parameters (range and azimuth) vary in time, the usage of the Polar Hough transform (PHT) is more suitable for radar applications because the input parameters for the PHT are the output parameters of a search radar system. Such a Polar Hough detector combined with a CFAR processor is proposed for operation in RAII conditions. The results obtained by simulation illustrate the high effectiveness of this detector when operating in strong RAII situations. Finally, the TBDPHT approach is applied to the design of a multi-channel Polar Hough detector for multisensor target detection and trajectory estimation in conditions of RAII. Three different structures of a nonsynchronous multi-sensor Polar Hough detector, decentralized with track association (DTA), decentralized with plot association (DPA) and centralized with signal