3,548 research outputs found
Target Detection Performance Bounds in Compressive Imaging
This paper describes computationally efficient approaches and associated
theoretical performance guarantees for the detection of known targets and
anomalies from few projection measurements of the underlying signals. The
proposed approaches accommodate signals of different strengths contaminated by
a colored Gaussian background, and perform detection without reconstructing the
underlying signals from the observations. The theoretical performance bounds of
the target detector highlight fundamental tradeoffs among the number of
measurements collected, amount of background signal present, signal-to-noise
ratio, and similarity among potential targets coming from a known dictionary.
The anomaly detector is designed to control the number of false discoveries.
The proposed approach does not depend on a known sparse representation of
targets; rather, the theoretical performance bounds exploit the structure of a
known dictionary of targets and the distance preservation property of the
measurement matrix. Simulation experiments illustrate the practicality and
effectiveness of the proposed approaches.Comment: Submitted to the EURASIP Journal on Advances in Signal Processin
Measurement Matrix Design for Compressive Sensing Based MIMO Radar
In colocated multiple-input multiple-output (MIMO) radar using compressive
sensing (CS), a receive node compresses its received signal via a linear
transformation, referred to as measurement matrix. The samples are subsequently
forwarded to a fusion center, where an L1-optimization problem is formulated
and solved for target information. CS-based MIMO radar exploits the target
sparsity in the angle-Doppler-range space and thus achieves the high
localization performance of traditional MIMO radar but with many fewer
measurements. The measurement matrix is vital for CS recovery performance. This
paper considers the design of measurement matrices that achieve an optimality
criterion that depends on the coherence of the sensing matrix (CSM) and/or
signal-to-interference ratio (SIR). The first approach minimizes a performance
penalty that is a linear combination of CSM and the inverse SIR. The second one
imposes a structure on the measurement matrix and determines the parameters
involved so that the SIR is enhanced. Depending on the transmit waveforms, the
second approach can significantly improve SIR, while maintaining CSM comparable
to that of the Gaussian random measurement matrix (GRMM). Simulations indicate
that the proposed measurement matrices can improve detection accuracy as
compared to a GRMM
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