Novel post-Doppler STAP with a priori knowledge information for traffic monitoring applications

Kurzfassung

The road traffic has worsened over time in most cities, and the methods employed for monitoring and counting the vehicles on the roads (e.g., cameras, induction loops, or even people manually counting) are expensive and limited in spatial coverage. Synthetic aperture radars (SAR) provide an effective solution for this problem due to the wide-area coverage and the independence from daylight and weather conditions. Special attention is given in case of large scale events or catastrophes, when mobile internet is unavailable and phone communication is impossible. In this particular scenario, the traffic monitoring with real-time information ensures the safety of the road users and can even save lives. For that reason, this paper presents a novel a priori knowledge-based algorithm for traffic monitoring, where the powerful post-Doppler space-time adaptive processing (PD STAP) is combined with a road network obtained from the freely available OpenStreetMap (OSM) database. The incorporation of a known road network into the processing chain presents great potential for real-time processing, since only the acquired data related to the roads need to be processed. As a result, decreased processing hardware complexity and low costs compared to state-of-the-art systems can be achieved. In addition, it is a promising solution for detecting effectively the road vehicles and estimating their positions, velocities and moving directions with high accuracy. The PD STAP is well-known for its very good clutter suppression, its sensitivity also to low vehicle velocities, and its accurate target position estimation capabilities. The road information is applied after the PD STAP, where the OSM database fused with a digital elevation model (DEM) is applied in order to recognize and to reject false detections, and moreover, to reposition the vehicles detected in the vicinity of the roads. In other words, the distance between the estimated position of the target and its closest road point is measured and compared to a relocation threshold for deciding whether the target corresponds to a true road vehicle or to a false detection. If the first condition is fulfilled, the target is repositioned to its closest road point; otherwise it is discarded. The relocation threshold is computed adaptively for each detection by using an appropriate performance model. The proposed algorithm was tested using real 4-channel aperture switching data acquired by DLR’s airborne system F-SAR. In the radar data takes examined so far, the PD STAP detected vehicles as slow as 7 km/h, with an overall position estimation accuracy better than 10 m. Besides, the estimated velocities of the vehicles were in very good agreement with the differential GPS reference data. To sum up, the experimental results revealed a powerful algorithm that detects even slow vehicles and discards most of the false detections, being suitable for many traffic monitoring applications. We will not limit our further investigations to the data takes whose results are shown in this paper. We have a large pool of multi-channel F-SAR data takes containing real highway traffic scenarios with dozens or even hundreds of vehicles.