Adaptive OFDM Waveform Design for SpatioTemporalSparsity Exploited STAP Radar
Abstract
In this chapter, we describe a sparsitybased spacetime adaptive processing (STAP) algorithm to detect a slowly moving target using an orthogonal frequency division multiplexing (OFDM) radar. The motivation of employing an OFDM signal is that it improves the targetdetectability from the interfering signals by increasing the frequency diversity of the system. However, due to the addition of one extra dimension in terms of frequency, the adaptive degreesoffreedom in an OFDMSTAP also increases. Therefore, to avoid the construction a fully adaptive OFDMSTAP, we develop a sparsitybased STAP algorithm. We observe that the interference spectrum is inherently sparse in the spatiotemporal domain, as the clutter responses occupy only a diagonal ridge on the spatiotemporal plane and the jammer signals interfere only from a few spatial directions. Hence, we exploit that sparsity to develop an efficient STAP technique that utilizes considerably lesser number of secondary data compared to the other existing STAP techniques, and produces nearly optimum STAP performance. In addition to designing the STAP filter, we optimally design the transmit OFDM signals by maximizing the output signaltointerferenceplusnoise ratio (SINR) in order to improve the STAP performance. The computation of output SINR depends on the estimated value of the interference covariance matrix, whichmore »
 Authors:
 ORNL
 Publication Date:
 Research Org.:
 Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
 Sponsoring Org.:
 USDOE
 OSTI Identifier:
 1412071
 DOE Contract Number:
 AC0500OR22725
 Resource Type:
 Book
 Country of Publication:
 United States
 Language:
 English
Citation Formats
Sen, Satyabrata. Adaptive OFDM Waveform Design for SpatioTemporalSparsity Exploited STAP Radar. United States: N. p., 2017.
Web.
Sen, Satyabrata. Adaptive OFDM Waveform Design for SpatioTemporalSparsity Exploited STAP Radar. United States.
Sen, Satyabrata. 2017.
"Adaptive OFDM Waveform Design for SpatioTemporalSparsity Exploited STAP Radar". United States.
doi:.
@article{osti_1412071,
title = {Adaptive OFDM Waveform Design for SpatioTemporalSparsity Exploited STAP Radar},
author = {Sen, Satyabrata},
abstractNote = {In this chapter, we describe a sparsitybased spacetime adaptive processing (STAP) algorithm to detect a slowly moving target using an orthogonal frequency division multiplexing (OFDM) radar. The motivation of employing an OFDM signal is that it improves the targetdetectability from the interfering signals by increasing the frequency diversity of the system. However, due to the addition of one extra dimension in terms of frequency, the adaptive degreesoffreedom in an OFDMSTAP also increases. Therefore, to avoid the construction a fully adaptive OFDMSTAP, we develop a sparsitybased STAP algorithm. We observe that the interference spectrum is inherently sparse in the spatiotemporal domain, as the clutter responses occupy only a diagonal ridge on the spatiotemporal plane and the jammer signals interfere only from a few spatial directions. Hence, we exploit that sparsity to develop an efficient STAP technique that utilizes considerably lesser number of secondary data compared to the other existing STAP techniques, and produces nearly optimum STAP performance. In addition to designing the STAP filter, we optimally design the transmit OFDM signals by maximizing the output signaltointerferenceplusnoise ratio (SINR) in order to improve the STAP performance. The computation of output SINR depends on the estimated value of the interference covariance matrix, which we obtain by applying the sparse recovery algorithm. Therefore, we analytically assess the effects of the synthesized OFDM coefficients on the sparse recovery of the interference covariance matrix by computing the coherence measure of the sparse measurement matrix. Our numerical examples demonstrate the achieved STAPperformance due to sparsitybased technique and adaptive waveform design.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = 2017,
month =
}

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