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báo cáo hóa học: " Track-before-detect in distributed sensor applications"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Track-before-detect in distributed sensor applications | Govaers et al. EURASIP Journal on Advances in Signal Processing 2011 2011 20 http asp.eurasipjournals.eom content 2011 1 20 o EURASIP Journal on Advances in Signal Processing a SpringerOpen Journal RESEARCH Open Access Track-before-detect in distributed sensor applications Felix Govaers 1 Yang Rong2 Lai Hoe Chee2 Wolfgang Koch1 Teow Loo Nin2 and Ng Gee Wah2 Abstract In this article we propose a new extension to a Dynamic Programming Algorithm DPA approach for Track-before-Detect challenges. This extension enables the DPA to process time-delayed sensor data directly. Such delay might appear because of delays in communication networks. The extended DPA is identical to the recursive standard DPA in case of all sensor data appear in the timely correct order. Furthermore an intense evaluation of the Accumulated State Density ASD filter is given on simulation data. Last but not least we apply a combination of DPA and ASD on data of a real radar system and present the resulting tracks. Our experience concerning this combination is a seamless cooperation between the track initialization by DPA and a track maintenance by ASD filter. Keywords Track-before-detect Out-of-sequence Real data application Dynamic programming approach Accumulated state density TBD OOSM DPA ASD 1. Introduction Since many years security applications employing radar sensors for surveillance objectives are increasingly important. In situations where targets with a low signal-to-noise ratio SNR appear it is convenient to apply tests on track existence utilizing raw sensor data instead of using thresholded measurements. This approach is generally called Track-before-Detect TBD . It enables a radar system to search for low-observable targets LOTs i.e. objects with a low SNR. These targets can be invisible to conventional methodologies as most of the information about them might be cut off by the applied threshold. The gain of a TBD algorithm is often paid by high computational costs. Even today when .