Radar Detection Theory of Sliding Window Processes

@inproceedings{Weinberg2017RadarDT,
  title={Radar Detection Theory of Sliding Window Processes},
  author={Graham Victor Weinberg},
  year={2017}
}
Sliding window decision rules are suboptimal non-coherent detectors designed to regulate the false alarm rate while introducing a loss relative to optimal Neyman-Pearson decision rules. In earlier low-resolution radars these detectors were very popular and during the 1960s were investigated extensively. However, with the systematic improvements in radar resolution such detectors became difficult to design so that they achieved the full constant false alarm rate (CFAR) property. This is… 
DISTRIBUTION OF THE CELL UNDER TEST IN SLIDING WINDOW DETECTION PROCESSES
  • G. V. Weinberg
  • Mathematics
    Progress In Electromagnetics Research Letters
  • 2019
Radar sliding window detection processes are often used in signal processing as alternatives to Neyman-Pearson based decision rules, due to the fact that they have a simpler receiver implementation
Compensating for Interference in Sliding Window Detection Processes using a Bayesian Paradigm
TLDR
The Bayesian approach to the construction of sliding window detectors with the constant false alarm rate property, with the capacity to manage interfering targets, will be outlined.
Optimal Predictive Inference and Noncoherent CFAR Detectors
TLDR
It will be demonstrated that this Bayesian predictive inference approach can produce such detectors with the constant false alarm rate (CFAR) property for clutter modeled by statistics, which are invariant with respect to a group of scale and power transformations.
REVIEW OF RADAR DETECTORS WITH CONSTANT FALSE ALARM RATE
TLDR
This article presents the theoretical framework required by CFAR techniques, as well as a literature review in this matter, and the application of non-coherent integration to CFAR detection is included as a distinctive element.
Interference control in sliding window detection processes using a Bayesian approach
TLDR
Here it will be shown how the Bayesian approach can be extended to produce sliding window detectors which are immune to the presence of interference.
BURR DISTRIBUTION FOR X-BAND MARITIME SURVEILLANCE RADAR CLUTTER
Recent research has shown that the Pareto family of distributions provides suitable intensity models for high resolution X-band maritime surveillance radar clutter. In particular, the two parameter
Minimum-Based Sliding Window Detectors in Correlated Pareto Distributed Clutter
  • G. V. Weinberg
  • Mathematics, Computer Science
    IEEE Geoscience and Remote Sensing Letters
  • 2017
TLDR
The structure of the sample minimum is investigated, which can then be used to produce decision rules robust to interference, and two detectors will be examined, and their performance in real high-resolution X-band maritime radar clutter will be investigated.
An Introduction to Radar Sliding Window Detectors
An introduction to the theory of sliding window detection processes, used as alternatives to optimal Neyman-Pearson based radar detectors, is presented. Included is an outline of their historical
Trimmed geometric mean order statistic CFAR detector for Pareto distributed clutter
  • G. V. Weinberg
  • Computer Science, Mathematics
    Signal Image Video Process.
  • 2018
TLDR
A trimmed geometric mean order statistic constant false alarm rate detector is developed and compared with some recently derived detectors and it will be shown that this new detector can be designed to not only manage interference in the clutter range profile but can be very effective at managing range spread targets.
A Bayesian CFAR detector for interference control in Weibull clutter
TLDR
This paper proposes a Bayesian CFAR detector for Weibull clutter under the assumption that the shape parameter is known and extends the WeIBull CFAR detectors for interference control where the number of interfering targets is determined or not.
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