- Introduction Adaptive Radar Detection of Targets Adaptive Detection Schemes for Mismatched Signals Enhanced Adaptive Sidelobe Blanking Algorithms Conclusions.
- (source: Nielsen Book Data)

- Introduction
- Adaptive radar detection of targets with perfectly known steering vector
- The radar scenario
- Discrete-time target and noise models
- The GLRT and the two-step GLRT-based design procedure with application to adaptive detection of a signal known up to a complex factor
- Adaptive detection of a signal known up to a complex factor
- Adaptive detection schemes for mismatched signals
- Mismatched signals
- Robust receivers
- Selective receivers
- Tunable receivers
- Parametric receivers
- Two-stage receivers
- Enhanced adaptive sidelobe blanking algorithms
- The subspace-based adaptive sidelobe blanker (S-ASB)
- Pfa and Pd of the S-ASB
- Performance prediction in presence of mismatched signals
- The W-ABORT and subspace-based adaptive sidelobe blanker (WAS-ASB)
- Pfa and Pd of the WAS-ASB
- Performance prediction in presence of mismatched signals
- Conclusions
- Complex normal related statistics
- Statistical characterization of Kelly's detector, AMF, ACE, and W-ABORT
- Statistical characterization of S-ASB and WAS-ASB
- Stochastic representations for tSD and β
- Statistical characterization of b and c
- Distributions under H0
- Distributions under H1 87
- Bibliography.

Adaptive detection of signals embedded in correlated Gaussian noise has been an active field of research in the last decades. This topic is important in many areas of signal processing such as, just to give some examples, radar, sonar, communications, and hyperspectral imaging. Most of the 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. 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 directivity 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 behaviors. 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. It turns out that such solutions guarantee a wide operational range in terms of tunability while retaining, at the same time, an overall performance in presence of matched signals commensurate with Kelly's detector.

(source: Nielsen Book Data)