An estimation method known as least absolute shrinkage and selection operator (LASSO) or ℓ1-regularized LS estimation has been found to perform well in a number of applications. In this paper, we use the majorize-minimize method to develop an algorithm for minimizing the LASSO objective function, which is the sum of a linear LS objective function plus an ℓ1 penalty term. The proposed algorithm, which we call the LASSO estimation via majorization-minimization (LMM) algorithm, is straightforward to implement, parallelizable, and guaranteed to produce LASSO objective function values that monotonically decrease. In addition, we formulate an extension of the LMM algorithm for reconstructing ground penetrating radar (GPR) images, that is much faster than the standard LMM algorithm and utilizes significantly less memory. Thus, the GPR specific LMM (GPR-LMM) algorithm is able to accommodate the big data associated with GPR imaging. We compare our proposed algorithms to the state-of-the-art ℓ1-regularized LS algorithms using a time and space complexity analysis. The GPR-LMM greatly outperforms the competing algorithms in terms of the performance metrics we considered. In addition, the reconstruction results of the standard LMM and GPR-LMM algorithms are evaluated using both simulated and real GPR data.

A signal processing approach is proposed to jointly filter and fuse spatially indexed measurements captured from many vehicles. It is assumed that these measurements are influenced by both sensor noise and measurement indexing uncertainties. Measurements from low-cost vehicle-mounted sensors (e.g., accelerometers and Global Positioning System (GPS) receivers) are properly combined to produce higher quality road roughness data for cost-effective road surface condition monitoring. The proposed algorithms are recursively implemented and thus require only moderate computational power and memory space. These algorithms are important for future road management systems, which will use on-road vehicles as a distributed network of sensing probes gathering spatially indexed measurements for condition monitoring, in addition to other applications, such as environmental sensing and/or traffic monitoring. Our method and the related signal processing algorithms have been successfully tested using field data.

Link travel times are crucial for advanced traveler information systems and traffic management applications. However, current systems for estimating them still have shortcomings that need to be addressed. In this paper, we propose a novel framework for vehicle reidentification via signature matching using signal processing techniques and a travel time estimation algorithm that is robust to potential (and often inevitable) vehicle misidentifications. Individual vehicles are matched between well-separated stations in a road transportation network using signatures captured by embedded roadway sensors. Statistical and multirate signal processing methods are used to develop data-postprocessing algorithms that are critical to the subsequent signature-matching problem, which is formulated using optimal techniques from communication theory. A probabilistic modeling of the generated matching assignments and an unsupervised data-clustering technique are then used to devise a travel time estimation algorithm. The proposed method is tested under a real traffic scenario, and accurate link travel time measures are reported.