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Solutions for using radar to alert drivers to obstacles in their path and possible collision already exist but they are too expensive. In most cases, only the more luxury models on the market offer the option of an adaptive cruise control system that uses radar. But the potential market and road safety benefits are massive. And much work is in progress to lower the cost of radar. This the reviews paper current state‐of‐the‐art in radar technology and highlights two notable developments for cutting the cost.

The purpose of this paper is to examine existing radar sensor results, techniques for through‐wall radar and current applications for the technology.

The paper provides information on sensing through a high‐attenuation obstacle and the associated pitfalls and considerations. Results from ultra‐wide‐band (UWB) impulse radar, micro‐Doppler radar, and synthetic aperture radar (SAR) targeted at this area are presented. Discussion of radar clutter classification is given and also observations on presenting a system with a non‐zero false alarm rate to a user to give best confidence and maximum decision capability.

There are significant new requirements for through‐wall radar which a combination of UWB, continuous wave, and SAR techniques with recent signal processing advances and the advent of low‐cost radio and image processing can meet in distributed markets. Risk of a poor user level decision in a non‐zero‐false‐alarm‐rate system can be mitigated by increasing the number of inputs into the decision.

The paper lists challenges that have been overcome in the area of through‐wall sensing and presents results from novel radar sensors.

The paper aims to solve the problem of personnel intrusion identification within the limits of high-speed railways. It adopts the fusion method of millimeter wave radar and camera to improve the accuracy of object recognition in dark and harsh weather conditions.

This paper adopts the fusion strategy of radar and camera linkage to achieve focus amplification of long-distance targets and solves the problem of low illumination by laser light filling of the focus point. In order to improve the recognition effect, this paper adopts the YOLOv8 algorithm for multi-scale target recognition. In addition, for the image distortion caused by bad weather, this paper proposes a linkage and tracking fusion strategy to output the correct alarm results.

Simulated intrusion tests show that the proposed method can effectively detect human intrusion within 0–200 m during the day and night in sunny weather and can achieve more than 80% recognition accuracy for extreme severe weather conditions.

(1) The authors propose a personnel intrusion monitoring scheme based on the fusion of millimeter wave radar and camera, achieving all-weather intrusion monitoring; (2) The authors propose a new multi-level fusion algorithm based on linkage and tracking to achieve intrusion target monitoring under adverse weather conditions; (3) The authors have conducted a large number of innovative simulation experiments to verify the effectiveness of the method proposed in this article.

This paper aims to present an approach of utilizing multiple-input multiple-output (MIMO) radar concept to enhance pedestrian classification in automotive sensors. In a practical environment, radar signals reflected from pedestrians and slow-moving vehicles are similar in terms of reflecting angle and Doppler returns, inducing difficulty for target discrimination. An efficient discrimination between the two targets depends on the ability of the sensor to extract unique characteristics from each target, for example, by exploiting Doppler signatures. This study describes the utilization of MIMO radar for Doppler measurement and demonstrates its application to improve pedestrian classification through actual laboratory measurements.

Multiple non-modulated sinusoidal signals are transmitted orthogonally over a MIMO array using time division scheme, illuminating human and non-human targets. The reflected signal entering each of the receiving antenna are combined at the radar receiver prior to Doppler processing. Doppler histogram was formulated based on a series of measurements, and the Doppler spread of the targets was determined from the histograms. Results were compared between MIMO and conventional single antenna systems.

Measurement results indicated that the MIMO configuration provides able to capture more Doppler information compared to conventional single antenna systems, enabling a more precise discrimination between pedestrian and other slow-moving objects on the road.

The study demonstrated the effectiveness of using MIMO configuration in radar-based automotive sensor to enhance the accuracy of Doppler estimation, which is seldom highlighted in literature of MIMO radars. The result also indicated its usefulness in improving target discrimination capability of the radar, through actual measurement.

To the industrial application of intelligent and connected vehicles (ICVs), the robustness and accuracy of environmental perception are critical in challenging conditions. However, the accuracy of perception is closely related to the performance of sensors configured on the vehicle. To enhance sensors’ performance further to improve the accuracy of environmental perception, this paper aims to introduce an obstacle detection method based on the depth fusion of lidar and radar in challenging conditions, which could reduce the false rate resulting from sensors’ misdetection.

Firstly, a multi-layer self-calibration method is proposed based on the spatial and temporal relationships. Next, a depth fusion model is proposed to improve the performance of obstacle detection in challenging conditions. Finally, the study tests are carried out in challenging conditions, including straight unstructured road, unstructured road with rough surface and unstructured road with heavy dust or mist.

The experimental tests in challenging conditions demonstrate that the depth fusion model, comparing with the use of a single sensor, can filter out the false alarm of radar and point clouds of dust or mist received by lidar. So, the accuracy of objects detection is also improved under challenging conditions.

A multi-layer self-calibration method is conducive to improve the accuracy of the calibration and reduce the workload of manual calibration. Next, a depth fusion model based on lidar and radar can effectively get high precision by way of filtering out the false alarm of radar and point clouds of dust or mist received by lidar, which could improve ICVs’ performance in challenging conditions.

This paper aims to propose a method for accurate radar echo simulation of wind turbines (WTs) array. It can solve the problem of passive interference from wind farms to neighboring radar stations.

First of all, the equivalent model of scattering centers of a single WT is obtained by using the spatial spectrum estimation method, and the accuracy of this model is verified by the scaled model experiment; then scattering centers model of WTs array was established by using the spatial coordinate transformation method. According to the position relationship between the model and the radar, and combined with the multipath scattering theory, the radar echo equation of WTs array was deduced. Finally, the simulation analysis is carried out with the four GoldWind 77/1500 WTs as an example and compared with the traditional methods.

This paper verifies the accuracy of the equivalent model of scattering centers through the WT scaled model experiment, and through simulation analysis, it is found that the result of this method is more consistent with the multipath scattering of radar echo between WTs array in practical engineering than the traditional method.

Based on the theory of high-frequency scattering, this paper introduces scattering centers into the solution of radar echo and considers the multipath scattering of radar echo, then a method for solving the radar echo of WTs array based on scattering centers is proposed.

The purpose of this paper is to provide a solution to investigate the deception effects of the radar equipped with decoys.

Since the attacking process of the anti‐radiation missile (ARM) is very complicated, numerical simulations are used here to analyze the effects of the operating parameters and the layout parameters of the radar and the decoys on the survival probabilities.

The survival probabilities of all the radar and the decoys can be over 99.5 percent when three decoys distribute near the radar in an appropriate way and the decoy level is set to some appropriate values.

The movement model of the ARM is simple and should be improved further.

The numerical results may be applied directly in practice and the dynamic simulation algorithms may be as a reference of the radar‐decoy technique in the future works.

Some coordinate systems are built. Based on it, the models of the radar and decoys against an ARM are introduced, including the fields radiated by the radar and the decoys, the guidance signal and the movement of the ARM. Numerical simulations have been performed here. Some conclusions have been given.

This paper aims to review the critical technology development of avian radar system at airports.

After the origin of avian radar technology is discussed, the target characteristics of flying birds are analyzed, including the target echo amplitude, flight speed, flight height, trajectory and micro-Doppler. Four typical airport avian radar systems of Merlin, Accipiter, Robin and CAST are introduced. The performance of different modules such as antenna, target detection and tracking, target recognition and classification, analysis of bird information together determines the detection ability of avian radar. The performances and key technologies of the ubiquitous avian radar are summarized and compared with other systems, and their applications, deployment modes, as well as their advantages and disadvantages are introduced and analyzed.

The ubiquitous avian radar achieves the long-time integration of target echoes, which greatly improves detection and classification ability of the targets of birds or drones, even under strong background clutter at airport. In addition, based on the big data of bird situation accumulated by avian radar, the rules of bird activity around the airport can be mined to guide the bird avoidance work.

This paper presented a novel avian radar system based on ubiquitous digital radar technology. The authors’ experience has confirmed that this system can be effective for airport bird strike prevention and management. In the future, the avian radar system will see continued improvement in both software and hardware, as the system is designed to be easily extensible.

Modern fighter aircraft using active electronically scanned array (AESA) fire control radars are able to detect and track targets at long ranges, in the order of 50 nautical miles or more. Low observable or stealth technology has contested the radar capabilities, reducing detection/tracking ranges roughly to one-third (or even less, for fighter aircraft radar). Hence, infrared search and track (IRST) systems have been reconsidered as an alternative to the radar. This study aims to explore and compare the capabilities and limitations of these two technologies, AESA radars and IRST systems, as well as their synergy through sensor fusion.

The AESA radar range is calculated with the help of the radar equation under certain assumptions, taking into account heat dissipation requirements, using the F-16 fighter as a case study. Concerning the IRST sensor, a new model is proposed for the estimation of the detection range, based on the emitted infrared radiation caused by aerodynamic heating.

The maximum detection range provided by an AESA radar could be restricted because of the increased waste heat which is produced and the relevant constraints concerning the cooling capacity of the carrying aircraft. On the other hand, IRST systems exhibit certain advantages over radars against low observable threats. IRST could be combined with a datalink with the help of data fusion, offering weapons-quality track.

An original approach is provided for the IRST detection range estimation. The AESA/IRST comparison offers valuable insight, while it allows for more efficient planning, at the military acquisition phase, as well as at the tactical level.

The paper aims to argue that new tools are needed for operating, developing and learning in work‐life networks where academic and practice knowledge are intertwined in multiple levels of and in boundary‐crossing across activities. At best, tools for learning are designed in a process of co‐configuration, as the analysis of one tool, Development Radar, aims to demonstrate.

The “Development Radar” narrative offers a way to analyse what co‐configuration might mean in the development practices of the learning network. The data consist of the researchers' and participants' tool‐related actions in planning and running a workshop of the Forum of Workplace Development, for which Development Radar was created. Analysis draws from cultural‐historical activity theory by including cultural sources of knowledge beyond the immediate pedagogic interaction.

Metaphors seem to be facilitative in the early phase of co‐configuration of a tool but not enough for sustainable workplace learning. What is needed is opening up the core concepts for all parties involved and providing ongoing negotiations and elaboration concerning their potential and meaning.

Expansive learning is supported by co‐configuration of tools that simultaneously provide a generic orientation basis of learning and are open to contextual knowledge creation in and across the levels of developmental activities.

The visual co‐configuration of tools may be crucial for understanding learning, development and the implementation of tools in a specific context, and even have an effect on the professional identity of users.

The significance of tools for the quality of workplace learning is generally acknowledged but the investigation into the pedagogical dynamics and material co‐configuration of tools needs more attention.