M
Muralidhar Rangaswamy
Researcher at Air Force Research Laboratory
Publications - 283
Citations - 5992
Muralidhar Rangaswamy is an academic researcher from Air Force Research Laboratory. The author has contributed to research in topics: Radar & Clutter. The author has an hindex of 38, co-authored 271 publications receiving 5061 citations. Previous affiliations of Muralidhar Rangaswamy include Northeastern University & Wright-Patterson Air Force Base.
Papers
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MIMO Radar Waveform Design With Constant Modulus and Similarity Constraints
TL;DR: Two sequential optimization procedures to maximize the Signal to Interference plus Noise Ratio (SINR) are presented, accounting for a constant modulus constraint as well as a similarity constraint involving a known radar waveform with some desired properties.
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Non-Gaussian random vector identification using spherically invariant random processes
TL;DR: E elegant and tractable techniques are presented for characterizing the probability density function (PDF) of a correlated non-Gaussian radar vector and an important result providing the PDF of the quadratic form of a spherically invariant random vector (SIRV) is presented.
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Parametric adaptive matched filter for airborne radar applications
Jaime R. Roman,Muralidhar Rangaswamy,Dennis W. Davis,Qingwen Zhang,Braham Himed,J.H. Michels +5 more
TL;DR: The parametric adaptive matched filter (PAMF) for space-time adaptive processing (STAP) is introduced via the matched filter, multichannel linear prediction, and the multichannels LDU decomposition.
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Cognitive Radar Framework for Target Detection and Tracking
TL;DR: This paper develops a general cognitive radar framework for a radar system engaged in target tracking that includes the higher-level tracking processor and specifies the feedback mechanism and optimization criterion used to obtain the next set of sensor data.
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Computer generation of correlated non-Gaussian radar clutter
TL;DR: Two canonical simulation procedures for the generation of correlated non-Gaussian clutter are presented and a new approach for the goodness-of-fit test is proposed in order to assess the performance of the simulation procedure.