Detection using frequency diversity with distributed sensors

Abstract

This thesis investigates detection using a frequency diverse, distributed, radar system. Distributed sensing systems provide an inherent spatial diversity by viewing a potential target from different aspect angles. By using different frequencies at each platform, a diversity gain is obtained in addition to the advantages of spatial diversity while also avoiding mutual interference. Here, since platforms are distributed spatially, true time delay is used at each platform to align the sample look point in time. Data models for a distributed system with and without frequency diversity are developed. These models are used to analyze the corresponding signalto-interference-plus-noise ratio and probability of detection for the two cases in the context of space time adaptive processing. The simulation results presented here illustrate the limitations imposed by mutual interference and the significant benefits of spatial and frequency diversity. In addition, two reduced rank approaches to implement adaptive processing in a distributed sensor system are presented.A new method of obtaining frequency diversity using orthogonal frequency division multiplexing (OFDM) is presented next. Exploiting spatial diversity, the key advantage of a distributed aperture radar, requires $\emph{orthogonality}$ in one of the frequency, time, waveform, dimensions across sensors. This thesis focuses on the simplest of these cases; frequency orthogonality. Here we address the key drawback associated with frequency diversity: whereas the use of multiple frequency bands requires additional RF hardware, an OFDM-based system needs only a single oscillator and demodulator while yet maintaining frequency orthogonality. OFDM employs many sub-carriers within a single frequency band instead of occupying different frequency bands. Separation of the signals can be performed oversampling of the incoming signal followed by a Fast Fourier transform (FFT). Array sensor calibration problem has been an impo...