@misc{FeiMukhopadhyayDaCostaetal., author = {Fei, Tai and Mukhopadhyay, Subhas and Da Costa, Jo{\~a}o Paulo Javidi and Gardill, Markus and Liu, Shengheng and Roychaudhuri, Chirasree and Lan, Lan and Demitri, Nevine}, title = {Guest editorial special issue on smartness and robustness of spatial environment perception in automated systems}, series = {IEEE Sensors Journal}, volume = {24}, journal = {IEEE Sensors Journal}, number = {14}, issn = {1558-1748}, doi = {10.1109/JSEN.2024.3409137}, pages = {21800 -- 21800}, abstract = {As the curtains close on this special issue dedicated to advanced sensor research, we reflect on the critical role sensor technology plays in the future of automation. Throughout this issue, we have explored significant advancements and ongoing challenges in developing intelligent, resilient automated systems. A key theme is the vital need for smarter, more robust sensing systems. Modern sensors, now more adaptable, adjust their operations based on external changes, crucial for maximizing automation in varied real-world situations. Efforts to enhance sensor robustness have produced improvements in performance in extreme conditions, cybersecurity, cost-effective solutions, and flexible system requirements while preserving performance. The addition of self-monitoring and calibration features allows for continuous refinement of sensor accuracy and rapid problem detection, boosting system reliability. The capacity for seamlessly integrating alternative technologies when specific sensors fail further strengthens system resilience. This issue includes 33 manuscripts selected from 65 submissions, exploring these diverse aspects.}, language = {en} } @misc{LiuMaoLiuetal., author = {Liu, Shengheng and Mao, Zihuan and Liu, Yiran and Fei, Tai and Gardill, Markus and Huang, Yongming}, title = {TRANM: Decoherenced DoA Estimation for Automotive Radar Using Generalized Sparse Arrays}, series = {Signal Processing}, volume = {231}, journal = {Signal Processing}, publisher = {Elsevier}, doi = {10.1016/j.sigpro.2025.109895}, pages = {11}, abstract = {This paper tackles the challenge of coherent single-snapshot direction-of-arrival estimation in automotive linear frequency modulated continuous wave (LFMCW) radar using a generalized sparse array. By leveraging atomic-norm minimization (ANM)-based interpolation and Toeplitz rearrangement, a TRANM framework is proposed to address the rank-deficiency issue in the range-Doppler domain. To further enhance computational efficiency, we re-formulate the TRANM problem into an equivalent optimization with reduced dimensionality. The problem is then solved using the alternating direction method of multipliers, which provides an optimal solution via an iterative process. Numerical simulations validate that the proposed approach can accurately resolve coherent signals with improved degrees of freedom and achieve super-resolution, all while maintaining a low computational cost.}, language = {en} }