Performance of Networked Passive Radar Systems with Multiple Transmitters and Receivers

具有多个发射器和接收器的网络化无源雷达系统的性能

• 批准号: 1405579
• 负责人: Rick Blum
• 金额: $ 23.36万
• 依托单位: Lehigh University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1405579&HistoricalAwards=false
• 关键词: Performance; Networked; Passive; Radar; Systems

The goal of this project is to demonstrate the outstanding but untapped potential of passive radar systems with multiple transmitters and receivers. Passive radar is a powerful approach that uses existing ambient communication signals such as radio and television broadcasts, or satellite, cellular and WiFi signals, to detect, image or classify objects and estimate their position and motion. Since passive radar uses existing communication signals it can drastically reduce cost, complexity and energy usage while being especially important in emergency settings where one needs to quickly deploy a radar. Through theoretical analysis, algorithm assessment, the project will demonstrate the tremendous performance gains obtained through moderate increases in the numbers of transmit and receive antennas for realistic radar system models. These contributions should have significant impact to signal processing, sensor networking, machine learning and radar systems research. As new statistical problems will be considered, new theory developed should provide contributions in mathematics and statistics while leading to practical algorithms and ultimately improved radar systems for air traffic control, homeland security, law enforcement (through-wall imaging), surveillance, ocean monitoring, weather monitoring, and environmental monitoring. These investigations should provide contributions relating to the performance analysis of multiple target cases in active radar, sonar, ultrasound, acoustics and other similar active and nonactive sensor technologies. It should encourage new applications for smart homes, businesses and cars. This project will also offer ample opportunities for educating graduate students, preferably from under-represented groups, in the important cross-disciplinary areas of signal processing and energy via coordination between this research project, classes and Lehigh's Integrated Networks for Electricity (INE) initiative, which the PI is leading. The sensing research in this project couples well with several activities within the INE initiative. Research results will also be incorporated into current and future Lehigh classes with the hope that class notes will evolve into a book and short course on networked passive radar to provide broad educational impact.The optimum possible performance for realistically estimating the position and velocity vectors of objects using a passive radar with M transmit and N receive stations will be derived for the first time. Based on presented preliminary results for a simplified system model, the project is expected to demonstrate the tremendous performance gains obtained through moderate increases in MN for realistic system models. These gains have not been observed to date and should encourage a tremendous increase in research activity on passive radar technology with MN 1. These contributions should have significant impact to signal processing, sensor networking, machine learning and radar systems research. The proposed approach will employ local/nonlocal/Bayesian/nonBayesian bounds for finite MN performance; recent convergence results for sums of dependent random variables to guide enlightening asymptotic analysis; carefully chosen models for correlated reflection coefficients, correlated noise and other important degradations; enhanced models based on electromagnetic theory; recently developed target and clutter models; and the most promising signals of opportunities, including MIMO communication signals which show significant promise. The well-developed topics of multiuser/iterative detection and interference channels will be employed to include the degradation incurred when estimating the transmitted signals of opportunity and to account for the any components of the direct path signals that may leak into what is thought to be only the reflected signals. The impact of simultaneously employing several different types of signals of opportunity and different station placements will be uncovered.

该项目的目的是证明具有多个发射器和接收器的被动雷达系统的出色但未开发的潜力。被动雷达是一种强大的方法，它使用现有的环境通信信号，例如广播和电视广播，或卫星，蜂窝和WiFi信号来检测，图像或分类对象并估算其位置和运动。由于被动雷达使用现有的通信信号，因此可以大大降低成本，复杂性和能源的使用，同时在需要快速部署雷达的紧急情况下尤为重要。通过理论分析，算法评估，该项目将证明通过中等增加的雷达系统模型的传输数量和接收天线获得的巨大性能增长。这些贡献应对信号处理，传感器网络，机器学习和雷达系统研究产生重大影响。随着新的统计问题的考虑，开发的新理论应在数学和统计学方面提供贡献，同时导致实用算法，并最终改善用于空中交通管制，国土安全部，执法（通过沃尔壁成像），监视，海洋监测，气候监测和环境监测的雷达系统。这些研究应提供与主动雷达，声纳，超声，声学以及其他类似活动和非活动传感器技术的多个目标病例的性能分析有关的贡献。它应该鼓励智能家居，企业和汽车的新应用。 该项目还将在重要的跨学科处理和能源的重要跨学科领域，通过该研究项目，班级和Lehigh的综合电力网络（INE）倡议，为研究生提供足够的机会，最好是来自代表性不足的群体的研究生。 该项目的感应研究与INE计划中的几项活动都很好地融合在一起。研究结果还将纳入当前和未来的Lehigh课程中，希望课堂笔记能够演变成网络被动雷达的书籍和简短的课程，以提供广泛的教育影响。实际上，使用M Transmit和N接收站的被动radar的位置和速度向量的现实估算对象的最佳性能将用于第一次。基于简化系统模型的提出的初步结果，预计该项目将证明通过MN的MN对于现实系统模型的中等增加而获得的巨大性能提高。迄今为止，尚未观察到这些收益，并应鼓励使用MN 1的被动雷达技术研究大幅度提高。这些贡献应对信号处理，传感器网络，机器学习和雷达系统研究产生重大影响。 拟议的方法将采用本地/非局部/贝叶斯/nonbayesian界限进行有限的MN性能；相关随机变量的总和来指导启发性渐近分析的最新收敛结果；精心选择的模型，用于相关反射系数，相关噪声和其他重要降解模型；基于电磁理论的增强模型； 最近开发了目标和混乱模型；以及最有希望的机遇信号，包括表现出巨大希望的MIMO通信信号。多源/迭代检测和干扰通道的发达主题将包括在估计传输机会的信号时产生的降级，并说明直接路径信号的任何组件，这些降解可能泄漏到只有被认为是反射的信号中的降级。同时使用几种不同类型的机会信号和不同的车站安置的影响将被发现。