Performance of Networked Passive Radar Systems with Multiple Transmitters and Receivers

该项目的目标是展示具有多个发射器和接收器的无源雷达系统的突出但尚未开发的潜力。被动雷达是一种强大的方法，它利用现有的环境通信信号，如无线电和电视广播，或卫星、蜂窝和WiFi信号，来检测、成像或分类目标，并估计其位置和运动。由于无源雷达使用现有的通信信号，它可以大大降低成本、复杂性和能源使用，在需要快速部署雷达的紧急情况下尤其重要。通过理论分析和算法评估，该项目将展示在实际雷达系统模型中，通过适度增加发射和接收天线的数量而获得的巨大性能收益。这些贡献将对信号处理、传感器网络、机器学习和雷达系统研究产生重大影响。随着新的统计学问题将被考虑，新发展的理论应该会在数学和统计学方面做出贡献，同时导致实用的算法，并最终改进用于空中交通管制、国土安全、执法(穿透墙成像)、监视、海洋监测、天气监测和环境监测的雷达系统。这些研究应该为有源雷达、声纳、超声波、声学和其他类似的有源和非有源传感器技术中的多目标情况的性能分析提供帮助。它应该鼓励智能家居、企业和汽车的新应用。该项目还将通过该研究项目、班级和利哈伊的电力综合网络(INE)倡议之间的协调，为信号处理和能源等重要跨学科领域的研究生提供大量的教育机会，最好是来自代表性不足的群体。该项目中的传感研究与INE倡议内的几项活动很好地结合在一起。研究成果也将被纳入目前和未来的LeHigh课程，希望课堂笔记将演变为一本关于联网无源雷达的书籍和短期课程，以产生广泛的教育影响。将首次得出使用具有M个发射站和N个接收站的无源雷达实际估计物体的位置和速度矢量的最佳可能性能。基于提供的简化系统模型的初步结果，该项目有望展示在实际系统模型中通过适度增加MN而获得的巨大性能收益。到目前为止，还没有观察到这些进展，应该会鼓励与MN-1合作的无源雷达技术研究活动的大幅增加。这些贡献应该对信号处理、传感器联网、机器学习和雷达系统研究产生重大影响。该方法将使用有限MN性能的局部/非局部/贝叶斯/非贝叶斯界限；相依随机变量和的最新收敛结果来指导启发式渐近分析；相关反射系数、相关噪声和其他重要退化的精心选择的模型；基于电磁理论的增强模型；最近发展的目标和杂波模型；以及最有希望的机会信号，包括显示出显著前景的MIMO通信信号。将采用多用户/迭代检测和干扰信道的成熟主题，以包括在估计发送的机会信号时引起的退化，并考虑可能泄漏到被认为仅是反射信号的直接路径信号的任何分量。同时使用几种不同类型的机会信号和不同的站点布置的影响将被揭示。

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.