On Localization Performance in Imaging Sensor Nets

We propose a massively scalable ldquoimagingrdquo architecture for sensor networks, in which sensor nodes act as ldquopixelsrdquo that electronically reflect (and possibly modulate data on top of) a beacon transmitted by a collector node. The collector employs sophisticated radar and image processing techniques to localize the responding sensor nodes, and (if data modulation is present) multiuser data demodulation techniques to extract the data sent by multiple sensors. The sensors do not need to know their own locations, do not need to communicate with each other, and can be randomly deployed. In this initial exposition, we develop basic insight into the localization capabilities of this approach, ignoring sensor data modulation. This reduces to an idealized one-bit, on-off keyed, communication model in which the the sensors are either ldquoactiverdquo or ldquoinactive,rdquo with the active sensors responding to the collector's beacon without superimposing data modulation. We consider a moving collector, with the sensor reflections creating a synthetic aperture radar (SAR)-like geometry. However, the collector must employ significant modifications to SAR signal processing for estimation of the location of the active sensors: noncoherent techniques similar to those in noncoherent radar tomography to account for the lack of carrier synchronization between sensor and collector nodes, and decision feedback mechanisms for estimation of the locations of multiple closely spaced active sensors. Measures for localization performance are defined, and the effect of system parameters such as bandwidth, beamwidth and signal-to-noise-ratio (SNR) on performance is investigated.