By Francesco Bandiera, Danilo Orlando, Giuseppe Ricci, Jose Moura (Series Editor)
Adaptive detection of signals embedded in correlated Gaussian noise has been an active field of research in the last decades. Such topic is important in many areas of signal processing as, just to give some examples, radar, sonar, communications, and hyperspectral imaging. Most of existing adaptive algorithms have been designed following the lead of the derivation of Kelly's detector which assumes perfect knowledge of the target steering vector. However, in realistic scenarios, mismatches are likely to occur, due to both environmental and instrumental factors. When a mismatched signal is present in the data under test, conventional algorithms may suffer severe performance degradation and, more important, they cannot adjust their behavior to face with the different operative environment. The presence of strong interferers in the cell under test makes the detection task even more challenging. An effective way to cope with this scenario relies on the use of tunable detectors, i.e., detectors capable of changing their characteristics through the tuning of proper parameters. The aim of this book is to present some recent advances in the design of tunable detectors and the focus is on the so-called two-stage detectors, i.e., adaptive algorithms obtained cascading two detectors with opposite behavior. In this context we present innovative solutions that increase the operational range in terms of tunability in comparison to existing ones, while retaining, at the same time, an overall performance, in presence of matched signals, commensurate with Kelly's detector. Proposed detectors also possess the desirable constant false alarm rate property. Finally, we derive exact closed-form expressions for the resulting probability of false alarm and the probability of detection for both matched and mismatched signals embedded in homogeneous Gaussian noise.
Price: US $35.00
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