Novel post-Doppler STAP with a priori knowledge information for traffic monitoring applications: basic idea and first results

Abstract

Abstract. This paper presents a novel a priori knowledgebased algorithm for traffic monitoring applications. The powerful post-Doppler space-time adaptive processing (PD STAP) is combined with a known road network obtained from the freely available OpenStreetMap (OSM) database. The road information is applied after the PD STAP for recognizing and rejecting false detections, and moreover, for repositioning the vehicles detected in the vicinity of the roads. The algorithm presents great potential for real-time processing, decreased hardware complexity and low costs compared to state-of-the-art systems. The processor was tested using real multi-channel data acquired by DLR's airborne system F-SAR. The experimental results are shown and discussed, and the novelties are highlighted (e.g., the benefits of using a priori knowledge information).

Range-Doppler Based CFAR Ship Detection with Automatic Training Data Selection

“…For smaller line-of-sight velocities a detection may be prevented by the presence of strong clutter. Previous studies have shown that effective clutter suppression generally is only possible when multiple receiving channels are available [17,18]. For instance, techniques like post-Doppler space-time adaptive processing (STAP) [19] effectively suppress the clutter and normalize or whiten the Doppler spectrum so that directly a single CFAR threshold can be applied once the clutter statistics are estimated.…”

Fast post-Doppler STAP with road map for traffic monitoring: overview and first results

“…The quality of the training data impacts directly on the PD STAP performance since the CCM is used for clutter cancellation, so that this operation is of great importance. Some strategies for training data selection and update are presented and discussed in detail in da Silva et al (2019).…”

“…The Performance Model block provides the GMTI performance of the optimum PD STAP processor, including theoretical predictions of the detection performance, blind velocities, minimum detectable velocity (MDV), azimuth positioning error, among others. Some of the performance results were already presented in da Silva and Baumgartner (2017a) based on the framework found in Ender et al (2008).…”

“…1b. A decision has to be made for each PD STAP detection in order to verify whether the detection is actually a vehicle moving with high probability on the road or not. This decision is carried out by the Error Models and Decision block, which combines two positioning error models: one for the moving targets (Cerutti-Maori et al, 2008) and one for the OSM road points (da Silva and Baumgartner, 2017b). If the detection is true, then the target is assigned to its closest OSM road point and its absolute velocity on the road and moving direction are estimated (Baumgartner and Krieger, 2012).…”

“…As a comparison, our former PD STAP processor with a priori knowledge information (Silva and Baumgartner, 2017a) was applied on the same data set, where all the available SAR data were processed. The former processor was also able to detect the cars 1 to 5, whereas the processing time was nearly 37 times longer than the fast processor proposed in this paper.…”