Probability and Measure

(1987). Applied Probability and Queues. Journal of the Operational Research Society: Vol. 38, No. 11, pp. 1095-1096.

https://link.springer.com/content/pdf/10.1057%2Fjors.1987.184.pdf

Applied Probability and Queues

생체의 모델링과 Simulation

Applied Probability and Queues.

https://nottingham-repository.worktribe.com/preview/2315217/Multi_Time_Series_Anomaly_Detection_for_Industry_4_0__Neurocomputing_.pdf

Multi-head CNN–RNN for multi-time series anomaly detection: An industrial case study

Recently it was discovered that the Pareto distribution is a good model for low-grazing-angle high-resolution radar sea clutter returns. Validation of it as a model for high-grazing-angle radar clutter is provided.

Assessing Pareto fit to high-resolution high-grazing-angle sea clutter

Pareto distribution has been introduced into the radar community recently as a suitable model for X-band high resolution maritime sea clutter returns. This intensity clutter model is a simple two parameter power law distribution. It is thus important to consider the development of constant false alarm rate detection processes for targets embedded within such clutter. It is shown that a simple functional transform can produce such detection schemes, whose false alarm probability and threshold are related through simple analytical expressions. These relationships are related intrinsically to Gaussian detection counterparts. Three detectors will be introduced and their performance analysed in both homogeneous and heterogeneous clutter environments. The effect of interfering targets in the training cells will also be examined.

Constant false alarm rate detectors for pareto clutter models

The Pareto distribution has been validated as a suitable model for X-band maritime surveillance radar clutter returns, and consequently there has been much interest in developing radar detection algorithms under such a clutter model assumption. Recent research has shown that it is possible to apply a transformation approach to adapt the traditional constant false alarm rate (CFAR) detectors, designed to operate in exponentially distributed clutter, to the Pareto setting. However, it was found that this approach resulted in the decision rule requiring a priori knowledge of the Pareto scale parameter. It is shown here that this shortcoming can be rectified by application of a complete sufficient statistic to the transformed detector. Consequently, new decision rules are derived and it is shown that they not only achieve the CFAR property but in some instances can improve the performance of the decision rules from which they are derived.

On the Construction of CFAR Decision Rules via Transformations

General transformation approach for constant false alarm rate detector development

Constant false alarm rate detection processes are important in radar signal processing. Such detection strategies are used as an alternative to optimal Neyman-Pearson based decision rules, since they can be implemented as a sliding window process running on a radar range-Doppler map. This book examines the development of such detectors in a modern framework. With a particular focus on high resolution X-band maritime surveillance radar, recent approaches are outlined and examined. Performance is assessed when the detectors are run in real X-band radar clutter. The book introduces relevant mathematical tools to allow the reader to understand the development, and follow its implementation.

Graham V. Weinberg

Papers

Assessing Pareto fit to high-resolution high-grazing-angle sea clutter

Constant false alarm rate detectors for pareto clutter models

On the Construction of CFAR Decision Rules via Transformations

General transformation approach for constant false alarm rate detector development

Radar Detection Theory of Sliding Window Processes