Spatial Compressive Sensing for MIMO Radar

We study compressive sensing in the spatial domain to achieve target localization, specifically direction of arrival (DOA), using multiple-input multiple-output (MIMO) radar. A sparse localization framework is proposed for a MIMO array in which transmit and receive elements are placed at random. This allows for a dramatic reduction in the number of elements needed, while still attaining performance comparable to that of a filled (Nyquist) array. By leveraging properties of structured random matrices, we develop a bound on the coherence of the resulting measurement matrix, and obtain conditions under which the measurement matrix satisfies the so-called isotropy property. The coherence and isotropy concepts are used to establish uniform and non-uniform recovery guarantees within the proposed spatial compressive sensing framework. In particular, we show that non-uniform recovery is guaranteed if the product of the number of transmit and receive elements, MN (which is also the number of degrees of freedom), scales with K(log(G))^2, where K is the number of targets and G is proportional to the array aperture and determines the angle resolution. In contrast with a filled virtual MIMO array where the product MN scales linearly with G, the logarithmic dependence on G in the proposed framework supports the high-resolution provided by the virtual array aperture while using a small number of MIMO radar elements. In the numerical results we show that, in the proposed framework, compressive sensing recovery algorithms are capable of better performance than classical methods, such as beamforming and MUSIC.Comment: To appear in IEEE Transactions on Signal Processin

Lensless wide-field fluorescent imaging on a chip using compressive decoding of sparse objects.

We demonstrate the use of a compressive sampling algorithm for on-chip fluorescent imaging of sparse objects over an ultra-large field-of-view (>8 cm(2)) without the need for any lenses or mechanical scanning. In this lensfree imaging technique, fluorescent samples placed on a chip are excited through a prism interface, where the pump light is filtered out by total internal reflection after exciting the entire sample volume. The emitted fluorescent light from the specimen is collected through an on-chip fiber-optic faceplate and is delivered to a wide field-of-view opto-electronic sensor array for lensless recording of fluorescent spots corresponding to the samples. A compressive sampling based optimization algorithm is then used to rapidly reconstruct the sparse distribution of fluorescent sources to achieve approximately 10 microm spatial resolution over the entire active region of the sensor-array, i.e., over an imaging field-of-view of >8 cm(2). Such a wide-field lensless fluorescent imaging platform could especially be significant for high-throughput imaging cytometry, rare cell analysis, as well as for micro-array research

Concurrent focal-plane generation of compressed samples fromtime-encoded pixel values

Compressive sampling allows wrapping the relevant content of an image in a reduced set of data. It exploits the sparsity of natural images. This principle can be employed to deliver images over a network under a restricted data rate and still receive enough meaningful information. An efficient implementation of this principle lies in the generation of the compressed samples right at the imager. Otherwise, i. e. digitizing the complete image and then composing the compressed samples in the digital plane, the required memory and processing resources can seriously compromise the budget of an autonomous camera node. In this paper we present the design of a pixel architecture that encodes light intensity into time, followed by a global strategy to pseudo-randomly combine pixel values and generate, on-chip and on-line, the compressed samples.Ministerio de Economía y Competitividad TEC 2015-66878-C3-1-RJunta de Andalucía TIC 2338-2013Office of Naval Research (USA) N000141410355CONACYT (Mexico) MZO-2017-29106

Recovering Sparse Signals Using Sparse Measurement Matrices in Compressed DNA Microarrays

Microarrays (DNA, protein, etc.) are massively parallel affinity-based biosensors capable of detecting and quantifying a large number of different genomic particles simultaneously. Among them, DNA microarrays comprising tens of thousands of probe spots are currently being employed to test multitude of targets in a single experiment. In conventional microarrays, each spot contains a large number of copies of a single probe designed to capture a single target, and, hence, collects only a single data point. This is a wasteful use of the sensing resources in comparative DNA microarray experiments, where a test sample is measured relative to a reference sample. Typically, only a fraction of the total number of genes represented by the two samples is differentially expressed, and, thus, a vast number of probe spots may not provide any useful information. To this end, we propose an alternative design, the so-called compressed microarrays, wherein each spot contains copies of several different probes and the total number of spots is potentially much smaller than the number of targets being tested. Fewer spots directly translates to significantly lower costs due to cheaper array manufacturing, simpler image acquisition and processing, and smaller amount of genomic material needed for experiments. To recover signals from compressed microarray measurements, we leverage ideas from compressive sampling. For sparse measurement matrices, we propose an algorithm that has significantly lower computational complexity than the widely used linear-programming-based methods, and can also recover signals with less sparsity

Direction-of-Arrival Estimation Based on Sparse Recovery with Second-Order Statistics

Traditional direction-of-arrival (DOA) estimation techniques perform Nyquist-rate sampling of the received signals and as a result they require high storage. To reduce sampling ratio, we introduce level-crossing (LC) sampling which captures samples whenever the signal crosses predetermined reference levels, and the LC-based analog-to-digital converter (LC ADC) has been shown to efficiently sample certain classes of signals. In this paper, we focus on the DOA estimation problem by using second-order statistics based on the LC samplings recording on one sensor, along with the synchronous samplings of the another sensors, a sparse angle space scenario can be found by solving an $ell_1$ minimization problem, giving the number of sources and their DOA's. The experimental results show that our proposed method, when compared with some existing norm-based constrained optimization compressive sensing (CS) algorithms, as well as subspace method, improves the DOA estimation performance, while using less samples when compared with Nyquist-rate sampling and reducing sensor activity especially for long time silence signal

Compressive image sensor architecture with on-chip measurement matrix generation

A CMOS image sensor architecture that uses a cellular automaton for the pseudo-random compressive sampling matrix generation is presented. The image sensor employs in-pixel pulse-frequency modulation and column wise pulse counters to produce compressed samples. A common problem of compressive sampling applied to image sensors is that the size of a full-frame compressive strategy is too large to be stored in an on-chip memory. Since this matrix has to be transmitted to or from the reconstruction system its size would also prevent practical applications. A full-frame compressive strategy generated using a 1-D cellular automaton showing a class III behavior neither needs a storage memory nor needs to be continuously transmitted. In-pixel pulse frequency modulation and up-down counters allow the generation of differential compressed samples directly in the digital domain where it is easier to improve the required dynamic range. These solutions combined together improve the accuracy of the compressed samples thus improving the performance of any generic reconstruction algorithm.Ministerio de Economía y Competitividad TEC2015-66878-C3-1-RJunta de Andalucía TIC 2338-2013Office of Naval Research (USA) N00014141035