**Preface to "Radar Remote Sensing for Applications in Intelligent Transportation"**

The emergence of self-driving vehicles has sparked curiosity among people, and the technology related to it advances every day. If you were asked to ride in a self-driving vehicle, your first reaction might be: is it safe to ride in a driverless car? Yes, self-driving cars are meant to improve traffic efficiency while ensuring safety. To achieve this goal, the millimeter-wave radar, with its all-weather detection capabilities, has become an important apparatus in the field of self-driving cars. On this crucial turning point, we launched a Special Issue on radar remote sensing in intelligent transportation and received 23 manuscript submissions. After fair and rigorous peer review process, we finally accepted eleven excellent papers covering diverse aspects. Thus, this book reflects the latest research results of radar remote sensing in intelligent transportation to a certain extent.

The automotive millimeter-wave radar has obvious advantages. However, as an active transmission remote sensing technology, the signal transmitted by the radar may become a strong interfering source for neighboring radars. The intensity of this interference signal is usually much higher than that of the target signal, leading to misjudgment by unmanned driving systems and eventually turn into potential traffic accidents. As self-driving technology evolves, the demand for automotive millimeter-wave radar in the market has grown exponentially, and the probability of interference between radars has also increased. The first paper in this book detects interference zone numbers before restoring the echoes in the interference area. Specifically, a sparse-based technique is proposed to suppress non-coherent interference between frequency-modulated continuous wave radars. First, a low-pass filter-based technique is developed to detect the envelope of the interference. Next, the marked areas with interference are treated as missing data. Then, the interference suppression problem is formulated as an echo interpolation recovery problem. Finally, the process of restoring radar echoes is derived based on the alternating direction method. This method has been verified by hardware experiments and has achieved good interference suppression performance.

Sharing radar detection information with neighboring vehicles could better compensate for blind spots in individual vehicle perception and reduce interference. This involves joint communication and radar sensing integration. As a very promising direction in intelligent transportation, this book includes two papers on this topic. The second paper designs a radar spatial-temporal transceiver and communication spatial-temporal codebook in the spectrum coexistence system on moving platforms. The joint radar and communication waveform often have a high range sidelobe, which will degrade the target detection performance of an automotive joint radar and communication system. To solve this problem, the third paper proposes a joint radar and communication complementary waveform group design method by exploiting the philosophy of the complementary sequence.

The constant false alarm rate (CFAR) detector plays a vital role in adaptive target detection of the radar. However, traditional CFAR detection algorithms use a sliding window to find the target limit radar detection speed and efficiency. In such cases, the fourth paper proposes and discusses a CFAR detection method that transforms the Monte Carlo simulation principle into randomly sampling instantaneous Range–Doppler data to improve the radar detection ability for moving targets such as pedestrians and vehicles in traffic environments. The fifth article describes a millimeter-wave distributed coherent aperture radar that can be used to improve the signal-to-noise ratio of the target by coherent-on-receive synthesis. The synchronization errors are calibrated with the estimated coherent parameters of the dominant scatterer, which can be defined as an unknown strong target in radar detection scenarios. Multi-object tracking based on robust detection is an old but new issue for self-driving cars. The sixth paper develops a Reweighted Robust Particle Filtering Approach for Target Tracking in Automotive Radar Applications.

Fascinatingly, as human–computer interaction continues to evolve, hand gestures are assuming an increasingly vital role in intelligent vehicle control. The seventh paper showcased in this collection introduces a remarkable multi-hand gesture recognition system that leverages automotive frequency-modulated continuous wave (FMCW) radar. Furthermore, the eighth paper focuses on addressing the limitations of traditional radar systems for self-driving. It presents a cutting-edge target tracking algorithm that harnesses the wealth of information provided by 4D millimeter-wave radar point clouds. This novel approach enables more accurate estimation of motion states and target contours, thanks to the higher resolution target point cloud data. It offers a significant advancement in overcoming the challenges faced by traditional radar systems. Beyond land transportation, the exploration of sea transportation also takes center stage in this compilation. In the ninth paper, a groundbreaking hybrid task cascade plus method is proposed, designed to enhance ship instance segmentation in Synthetic Aperture Radar (SAR) images.

The last two papers are related to radar antenna direction. For automobile radar systems, the antenna array requires a low sidelobe level (SLL) to reduce interference. A low-SLL planar array based on linear series-fed patch sub-arrays is presented in the ninth paper. Frequency diverse array (FDA) produces a beampattern with controllable direction and range by slightly shifting the carrier frequencies across the elements, which is attractive in many applications. In the last paper, the analytical expressions of the Cramer-Rao bounds in stochastic signal cases for joint Direction of ´ Arrival-range estimation using coprime FDA are derived.

Finally, we owe a great deal to many people for this book's successful publication. The guest editors would like to express special thanks to the journal editor, Nancy Yang, for her tremendous help. It can be said that without Nancy's long-term vision and pragmatic and meticulous cooperation, this book would not have been published. Nancy's work ability is outstanding, and she is friendly, kind-hearted, beautiful and hardworking. The reason why the remote sensing journal is getting better and better is because of great talents like Nancy. In addition, thanks go to all the paper authors and reviewers. It is your careful and meticulous work that has contributed to our joint efforts in radar remote sensing for intelligent transportation.

> **Zhihuo Xu, Jianping Wang, and Yongwei Zhang** *Editors*
