Classification of radar data by detecting and identifying spatial and temporal anomalies
Tutkimustuotos › › vertaisarvioitu
|Otsikko||Automatic Target Recognition XX; Acquisition, Tracking, Pointing, and Laser Systems Technologies XXIV; and Optical Pattern Recognition XXI; Defence, Security, and Sensing 2010: Sensor Data and Signal, Image and Neural Processing, Orlando, FL, USA, April 5-9, 2010. Proceedings of SPIE|
|DOI - pysyväislinkit|
|Tila||Julkaistu - 2010|
|OKM-julkaisutyyppi||A4 Artikkeli konferenssijulkaisussa|
For some time, applying the theory of pattern recognition and classification to radar signal processing has been a topic of interest in the field of remote sensing. Efficient operation and target indication is often hindered by the signal background, which can have similar properties with the interesting signal. Because noise and clutter may constitute most part of the response of surveillance radar, aircraft and other interesting targets can be seen as anomalies in the data. We propose an algorithm for detecting these anomalies on a heterogeneous clutter background in each range-Doppler cell, the basic unit in the radar data deﬁned by the resolution in range, angle and Doppler. The analysis is based on the time history of the response in a cell and its correlation to the spatial surroundings. If the newest time window of response in a resolution cell differs statistically from the time history of the cell, the cell is determined anomalous. Normal cells are classified as noise or different type of clutter based on their strength on each Doppler band. Anomalous cells are analyzed using a longer time window, which emulates a longer coherent illumination. Based on the decorrelation behavior of the response in the long time window, the anomalous cells are classified as clutter, an airplane or a helicopter. The algorithm is tested with both experimental and simulated radar data. The experimental radar data has been recorded in a forested landscape.