DOA Estimation in Partially Correlated Noise Using Low-Rank/Sparse Matrix Decomposition

We consider the problem of direction-of-arrival (DOA) estimation in unknown partially correlated noise environments where the noise covariance matrix is sparse. A sparse noise covariance matrix is a common model for a sparse array of sensors consisted of several widely separated subarrays. Since interelement spacing among sensors in a subarray is small, the noise in the subarray is in general spatially correlated, while, due to large distances between subarrays, the noise between them is uncorrelated. Consequently, the noise covariance matrix of such an array has a block diagonal structure which is indeed sparse. Moreover, in an ordinary nonsparse array, because of small distance between adjacent sensors, there is noise coupling between neighboring sensors, whereas one can assume that nonadjacent sensors have spatially uncorrelated noise which makes again the array noise covariance matrix sparse. Utilizing some recently available tools in low-rank/sparse matrix decomposition, matrix completion, and sparse representation, we propose a novel method which can resolve possibly correlated or even coherent sources in the aforementioned partly correlated noise. In particular, when the sources are uncorrelated, our approach involves solving a second-order cone programming (SOCP), and if they are correlated or coherent, one needs to solve a computationally harder convex program. We demonstrate the effectiveness of the proposed algorithm by numerical simulations and comparison to the Cramer-Rao bound (CRB).Comment: in IEEE Sensor Array and Multichannel signal processing workshop (SAM), 201

Successive Concave Sparsity Approximation for Compressed Sensing

In this paper, based on a successively accuracy-increasing approximation of the $\ell_0$ norm, we propose a new algorithm for recovery of sparse vectors from underdetermined measurements. The approximations are realized with a certain class of concave functions that aggressively induce sparsity and their closeness to the $\ell_0$ norm can be controlled. We prove that the series of the approximations asymptotically coincides with the $\ell_1$ and $\ell_0$ norms when the approximation accuracy changes from the worst fitting to the best fitting. When measurements are noise-free, an optimization scheme is proposed which leads to a number of weighted $\ell_1$ minimization programs, whereas, in the presence of noise, we propose two iterative thresholding methods that are computationally appealing. A convergence guarantee for the iterative thresholding method is provided, and, for a particular function in the class of the approximating functions, we derive the closed-form thresholding operator. We further present some theoretical analyses via the restricted isometry, null space, and spherical section properties. Our extensive numerical simulations indicate that the proposed algorithm closely follows the performance of the oracle estimator for a range of sparsity levels wider than those of the state-of-the-art algorithms.Comment: Submitted to IEEE Trans. on Signal Processin

The Effects of Port Water Injection on Spark Ignition Engine Performance and Emissions Fueled by Pure Gasoline, E5 and E10

It has been proven that vehicle emissions such as oxides of nitrogen (NOx) are negatively affecting the health of human beings as well as the environment. In addition, it was recently highlighted that air pollution may result in people being more vulnerable to the deadly COVID-19 virus. The use of biofuels such as E5 and E10 as alternatives of gasoline fuel have been recommended by different researchers. In this paper, the impacts of port injection of water to a spark ignition engine fueled by gasoline, E5 and E10 on its performance and NOx production have been investigated. The experimental work was undertaken using a KIA Cerato engine and the results were used to validate an AVL BOOST model. To develop the numerical analysis, design of experiment (DOE) method was employed. The results showed that by increasing the ethanol fraction in gasoline/ethanol blend, the brake specific fuel consumption (BSFC) improved between 2.3% and 4.5%. However, the level of NOx increased between 22% to 48%. With port injection of water up to 8%, there was up to 1% increase in engine power whereas NOx and BSFC were reduced by 8% and 1%, respectively. The impacts of simultaneous changing of the start of combustion (SOC) and water injection rate on engine power and NOx production was also investigated. It was found that the NOx concentration is very sensitive to SOC variation

Exergy analysis of a diesel engine with waste cooking biodiesel and triacetin

This study uses the first and second laws of thermodynamics to investigate the effect of 18 oxygenated fuels on the quality and quantity of energy in a turbo-charged, common-rail six19 cylinder diesel engine. This work was performed using a range of fuel oxygen content based 20 on diesel, waste cooking biodiesel, and a triacetin. The experimental engine performance and 21 emission data was collected at 12 engine operating modes. Energy and exergy parameters were 22 calculated, and results showed that the use of oxygenated fuels can improve the thermal 23 efficiency leading to lower exhaust energy loss. Waste cooking biodiesel (B100) exhibited the 24 lowest exhaust loss fraction and highest thermal efficiency (up to 6% higher than diesel). 25 Considering the exergy analysis, lower exhaust temperatures obtained with oxygenated fuels 26 resulted in lower exhaust exergy loss (down to 80%) and higher exergetic efficiency (up to 27 10%). Since the investigated fuels were oxygenated, this study used the oxygen ratio (OR) 28 instead of the equivalence ratio to provide a better understanding of the concept. The OR has 29 increased with decreasing engine load and increasing engine speed. Increasing the OR 30 decreased the fuel exergy, exhaust exergy and destruction efficiency. With the use of B100, 31 there was a very high exergy destruction (up to 55%), which was seen to decrease with the 32 addition of triacetin (down to 29%)

Particulate number emissions during cold-start with diesel and biofuels: A special focus on particle size distribution

The share of biofuels in the transportation sector is increasing. Previous studies revealed that the use of biofuels decreases the size of particles (which is linked to an increase in particulate toxicity). Current emission regulations do not consider small particles (sub-23 nm); however, there is a focus in future emissions regulations on small particles. These and the fact that within cold-start emissions are higher than during the warmed-up operation highlight the importance of a research that studies particulate matter emissions during cold-start. This research investigates the influence of biofuel on PN and PM concentration, size distribution, median diameter and cumulative share at different size ranges (including sub-23 nm and nucleation mode) during cold-start and warm-up operations using diesel and 10, 15 and 20% mixture (coconut biofuel blended with diesel). During cold-start, between 19 and 29% of total PN and less than 0.8% of total PM were related to the nucleation mode (sub-50 nm). Out of that, the share of sub-23 nm was up to 9% for PN while less than 0.02% for PM. By using biofuel, PN increased between 27 and 57% at cold-start; while, the increase was between 4 and 19% during hot-operation. The median diameter also decreased at cold-start and the nucleation mode particles (including sub-23 nm particles) significantly increased. This is an important observation because using biofuel can have a more adverse impact within cold-start period which is inevitable in most vehicles’ daily driving schedules.<br/