Radar networks: A review of features and challenges

Networks of multiple radars are typically used for improving the coverage and tracking accuracy. Recently, such networks have facilitated deployment of commercial radars for civilian applications such as healthcare, gesture recognition, home security, and autonomous automobiles. They exploit advanced signal processing techniques together with efficient data fusion methods in order to yield high performance of event detection and tracking. This paper reviews outstanding features of radar networks, their challenges, and their state-of-the-art solutions from the perspective of signal processing. Each discussed subject can be evolved as a hot research topic.Comment: To appear soon in Information Fusio

3D Localization and Tracking Methods for Multi-Platform Radar Networks

Multi-platform radar networks (MPRNs) are an emerging sensing technology due to their ability to provide improved surveillance capabilities over plain monostatic and bistatic systems. The design of advanced detection, localization, and tracking algorithms for efficient fusion of information obtained through multiple receivers has attracted much attention. However, considerable challenges remain. This article provides an overview on recent unconstrained and constrained localization techniques as well as multitarget tracking (MTT) algorithms tailored to MPRNs. In particular, two data-processing methods are illustrated and explored in detail, one aimed at accomplishing localization tasks the other tracking functions. As to the former, assuming a MPRN with one transmitter and multiple receivers, the angular and range constrained estimator (ARCE) algorithm capitalizes on the knowledge of the transmitter antenna beamwidth. As to the latter, the scalable sum-product algorithm (SPA) based MTT technique is presented. Additionally, a solution to combine ARCE and SPA-based MTT is investigated in order to boost the accuracy of the overall surveillance system. Simulated experiments show the benefit of the combined algorithm in comparison with the conventional baseline SPA-based MTT and the stand-alone ARCE localization, in a 3D sensing scenario

Survey of location-centric target tracking with mobile elements in wireless sensor networks

介绍目标跟踪的过程以及移动跟踪的特点;通过区分目标定位为主的方法和目标探测为主的方法,介绍定位为主的移动式目标跟踪方法(称为目标的移动式定位跟踪; )的研究现状;分析和比较不同方法的特点和应用领域,发现现有方法虽然可以提高跟踪质量、降低网络整体能耗,但是还存在一些问题。基于此,总结目标的移动; 式定位跟踪方法在方法类型、网络结构和节点模型等方面可能存在的研究热点,指出其研究和发展趋势。The basic process of target tracking and the properties of tracking; solutions with mobile elements were introduced. By distinguishing; location-centric methods and detection-centric methods, the current; research status of the location-centric target tracking methods were; reviewed. The properties and application fields of different solutions; were analyzed and compared. Although the existing solutions can; significantly improve tracking quality and reduce energy consumption of; the whole network, there are also some problems. Based on these; discoveries, some possible research hotspots of mobile solutions were; summarized in many aspects, such as method types, network architecture,; node model, and so on, indicating the future direction of research and; development.国家自然科学基金资助项目; 国家科技支撑计划项

Overview of Environment Perception for Intelligent Vehicles

This paper presents a comprehensive literature review on environment perception for intelligent vehicles. The state-of-the-art algorithms and modeling methods for intelligent vehicles are given, with a summary of their pros and cons. A special attention is paid to methods for lane and road detection, traffic sign recognition, vehicle tracking, behavior analysis, and scene understanding. In addition, we provide information about datasets, common performance analysis, and perspectives on future research directions in this area