Paper
17 May 2006 Real-time radar data fusion and registration systems for single integrated air picture
Andrew L. Drozd, Ruixin Niu, Irina Kasperovich, Pramod K. Varshney, Clifford E. Carroll
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Abstract
Real-time fusion of data collected from a variety of radars that acquire information from multiple perspectives and/or different frequencies, is being shown to provide a more accurate picture of the adversary threat cloud than any single radar or group of radars operating independently. This paper describes a cooperative multi-sensor approach in which multiple radars operate together in a non-interference limited manner, and where decision algorithms are applied to optimize the acquisition, tracking, and discrimination of moving targets with low false alarm rate. The approach is twofold: (i) measure and process radar returns in a shared manner for target feature extraction by exploiting frequency and spatial diversity; and (ii) employ feature-aided track/fusion algorithms to detect, discriminate, and track real targets from the adversary noise cloud. The results of computer simulations are provided that demonstrate the advantages of this approach.
© (2006) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Andrew L. Drozd, Ruixin Niu, Irina Kasperovich, Pramod K. Varshney, and Clifford E. Carroll "Real-time radar data fusion and registration systems for single integrated air picture", Proc. SPIE 6235, Signal Processing, Sensor Fusion, and Target Recognition XV, 62350U (17 May 2006); https://doi.org/10.1117/12.665786
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KEYWORDS
Radar

Sensors

Target detection

Detection and tracking algorithms

Data fusion

Monte Carlo methods

Target recognition

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