Abstract-A distributed MIMO radar is considered, in which the transmit and receive antennas belong to nodes of a small scale wireless network. The transmit waveforms could be uncorrelated, or correlated in order to achieve a desirable beampattern. The concept of compressive sampling is employed at the receive nodes in order to perform direction of arrival (DOA) estimation. According to the theory of compressive sampling, a signal that is sparse in some domain can be recovered based on far fewer samples than required by the Nyquist sampling theorem. The DOAs of targets form a sparse vector in the angle space, and therefore, compressive sampling can be applied for DOA estimation. The proposed approach achieves the superior resolution of MIMO radar with far fewer samples than other approaches. This is particularly useful in a distributed scenario, in which the results at each receive node need to be transmitted to a fusion center.

No abstract

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 ℓ 1 -optimization problem is formulated and solved for target information. CS-based MIMO radar exploits the target sparsity in the angle-Dopplerrange 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.

Objectives To determine if serum markers for collagen I and III synthesis, the C-terminal peptide from Pro-collagen-I (PICP) and the N-terminal peptide from Pro-Collagen III (PIIINP), correlate with LA fibrosis and post-operative AF. Background Atrial fibrillation (AF) after cardiac surgery is associated with adverse outcomes. We recently demonstrated that left atrial (LA) fibrosis is associated with post-operative AF in patients with no previous history of AF. Methods Fifty-four patients having cardiac surgery without a history of AF consented to left and right atrial biopsies, and a pre-operative peripheral blood draw. Picrosirius red staining quantified the percentage of fibrosis, and RT-PCR assessed atrial tissue mRNA transcripts involved in the fibrosis pathway. PICP and PIIINP levels were measured using an enzyme immunosorbant assay. Results Eighteen patients developed AF, while 36 remained in normal sinus rhythm (NSR). Left atrial fibrosis was higher in patients who developed AF vs. NSR (6.13 ±2.9% vs. 2.03± 1.9%, p =0.03). LA mRNA transcripts for collagen I, III, transforming growth factor, and angiotensin were 1.5-2.0 fold higher in AF patients. Serum PICP and PIIINP levels were highest in AF vs. NSR (PICP: 451.7±200 vs. 293.3±114 ng/ml, p=0.006; PIIINP: 379±286 vs. 191.6±162 pg/ml, p=0.01). Further there was a linear correlation between LA fibrosis and serum PICP levels (R2= 0.2; p = 0.01), and of the markers only PICP was independently associated with AF. Conclusions This demonstrates that serum PICP and PIIINP levels correlate with the presence of left atrial fibrosis and may act as a predictor for post-operative AF even in the absence of previous history of AF.

No abstract

No abstract

This paper proposes a novel radar system, namely step-frequency with compressive sampling (SFR-CS), that achieves high target range and speed resolution using significantly smaller bandwidth than traditional step-frequency radar. This bandwidth reduction is accomplished by employing compressive sampling ideas and exploiting the sparseness of targets in the range-speed space.

No abstract