DeepAI AI Chat
Log In Sign Up

Extension of the Geometric Mean Constant False Alarm Rate Detector to Multiple Pulses

01/02/2019
by   Graham V. Weinberg, et al.
0

The development of sliding window detection processes, based upon a single cell under test, and operating in clutter modelled by a Pareto distribution, has been examined extensively. This includes the construction of decision rules with the complete constant false alarm rate property. However, the case where there are multiple pulses available has only been examined in the partial constant false alarm rate scenario. This paper outlines in the latter case how the probability of false alarm can be produced, for a geometric mean detector, using properties of gamma distributions. The extension of this result, to the full constant false alarm rate detector case, is then presented.

READ FULL TEXT

page 1

page 2

page 3

page 4

12/23/2018

A Note on the Bayesian Approach to Sliding Window Detector Development

Recently a Bayesian methodology has been introduced, enabling the constr...
01/11/2019

Multipulse Order Statistic Constant False Alarm Rate Detector in Pareto Background

In a recent study, the extension of sliding window detectors from the si...
01/04/2019

Compensating for Interference in Sliding Window Detection Processes using a Bayesian Paradigm

Sliding window detectors are non-coherent decision processes, designed i...
12/26/2020

Scene Text Detection for Augmented Reality – Character Bigram Approach to reduce False Positive Rate

Natural scene text detection is an important aspect of scene understandi...
10/19/2022

CFAR based NOMP for Line Spectral Estimation and Detection

The line spectrum estimation problem is considered in this paper. We pro...
06/12/2022

Learning to Detect with Constant False Alarm Rate

We consider the use of machine learning for hypothesis testing with an e...
11/07/2022

Impedance Variation Detection at MISO Receivers

Techniques have been proposed to estimate unknown antenna impedance due ...