8,753 research outputs found
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
Segmented compressed sampling for analog-to-information conversion: Method and performance analysis
A new segmented compressed sampling method for analog-to-information
conversion (AIC) is proposed. An analog signal measured by a number of parallel
branches of mixers and integrators (BMIs), each characterized by a specific
random sampling waveform, is first segmented in time into segments. Then
the sub-samples collected on different segments and different BMIs are reused
so that a larger number of samples than the number of BMIs is collected. This
technique is shown to be equivalent to extending the measurement matrix, which
consists of the BMI sampling waveforms, by adding new rows without actually
increasing the number of BMIs. We prove that the extended measurement matrix
satisfies the restricted isometry property with overwhelming probability if the
original measurement matrix of BMI sampling waveforms satisfies it. We also
show that the signal recovery performance can be improved significantly if our
segmented AIC is used for sampling instead of the conventional AIC. Simulation
results verify the effectiveness of the proposed segmented compressed sampling
method and the validity of our theoretical studies.Comment: 32 pages, 5 figures, submitted to the IEEE Transactions on Signal
Processing in April 201
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