Abstract
This chapter aims to discuss recent advances on Bayesian KA-STAP techniques. It unfolds as the classical STAP signal model in Section 5.2 evolves into a framework of KA-STAP model including a knowledge-aided homogeneous model, a knowledge-aided partially homogeneous model, and a knowledge-aided compound Gaussian model in Section 5.3. Then in Section 5.4, a hierarchical two-layered STAP model is discussed, which provides a new way to describe the non-homogeneity between the test and training data. Section 5.5 is devoted to parametric Bayesian detectors that integrate structural space-time information, i.e., a multi-channel autoregressive (AR) process, for the interference model and the consequent Bayesian estimation. The resulting Bayesian parametric detectors allow a fast implementation and further reduction in the amount of training data needed for reliable detection.
Original language | English |
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Title of host publication | Modern Radar Detection Theory |
Pages | 133-164 |
Number of pages | 32 |
ISBN (Electronic) | 9781613532003 |
DOIs | |
State | Published - 1 Jan 2016 |
Keywords
- AR process
- Autoregressive processes
- Bayes methods
- Bayesian KA-STAP technique
- Bayesian estimation
- Bayesian radar detection
- Classical STAP signal model
- Estimation theory
- Hierarchical two-layered STAP model
- Interference model
- Knowledge-aided compound Gaussian model
- Knowledge-aided partially homogeneous model
- Multichannel autoregressive process
- Parameter estimation
- Parametric Bayesian detector
- Radar detection
- Radar interference
- Space-time adaptive processing
- Structural space-time information