Performance of Networked Passive Radar Systems with Multiple Transmitters and Receivers

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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.