MATRIX Software-Defined Radio
Multichannel Adaptive TRansceiver Information eXtractor (MATRIX) is a state-of-the-art, specialized software-defined radio (SDR). MATRIX treats all ambient signals in the environment, most of which are not intended as positioning or navigation sources, as potential signals of opportunity. Examples of these signals include audio (e.g., AM, FM), television (e.g., HDTV), cellular (e.g., CDMA, GSM, LTE), and satellite communication (e.g., Iridium). MATRIX continuously searches for opportune signals from which it draws navigation and timing information, employing signal characterization on-the-fly as necessary.
Our research in this thrust focuses on modeling signals of opportunity at a high level of granularity and designing appropriate MATRIX SDR modules for such signals, which are capable of producing accurate navigation observables.
Collaborative and Opportunistic Navigation
Future autonomous systems will demand full situational awareness and reliable, consistent, tamper-proof, and highly accurate navigation systems. Global navigation satellite system (GNSS) is at the heart of virtually all current navigation systems. However, GNSS will not meet the demands of future autonomous systems. First, GNSS signals are extremely weak and unusable indoors and in deep urban canyons. Second, GNSS signals are susceptible to intentional and unintentional jamming and interference. Third, civilian GNSS signals are unencrypted, unauthenticated, and specified in publicly-available documents, making them spoofable.
Our research in this thrust asks: what information is already available in the surrounding environment, and how can it be exploited for positioning, navigation, and timing? The information extracted from the environment is fused with on-board sensors to build a spatio-temporal map of the environment within which the autonomous system localizes itself in space and time. Information is also shared among multiple systems to achieve global situational awareness within the environment.
Optimal Information Gathering in Stochastic Environments
Future autonomous systems will operate in unfamiliar areas with minimal or no human interaction for prolonged periods of time. The luxury of building prior detailed maps of these environments could be (1) prohibitive (e.g., disaster areas), (2) impractical (e.g., signal landscapes and congested downtowns), or (3) economically not viable (e.g., hospital buildings and national forests). With no human-in-the-loop before or during operation, one expects future autonomous systems to (1) possess full situational awareness and (2) gather sufficient information about their environment. These two tasks need to seamlessly integrate into the overall mission of the autonomous system.
Our research in this thrust focuses on developing theory, algorithms, and tools for autonomous systems deployed in unknown, dynamic stochastic environments to optimally gather sufficient information to successfully accomplish their mission. Our research specifically considers autonomous systems with limited sensing, computation, actuation, and communication capabilities.