Extended Order Statistic CFAR Detection For Multiple interfering targets in Log-Normal Clutter

This communication examines Constant False Alarm Rate (CFAR) detection in the presence of secondary targets within a log-normal background. The modified Order Statistic (OS) CFAR is derived from a universal test statistic, which is expressed as a function of two different scale-invariant functions. This approach combines the conventional OS-CFAR and Weber-Haykin (WH) CFAR algorithms, resulting in a CFAR detector characterized by three ordered statistics. The CFAR properties and detection performance of the proposed threshold are evaluated against existing CFAR methods in both homogeneous and heterogeneous clutter scenarios. Monte Carlo simulations demonstrate that the proposed WHOS-CFAR detector maintains robustness even in the presence of strong interfering targets.

Authors

Institutions

Publication Details

Published
2024-10-01
DOI
https://doi.org/10.1109/icaecot62402.2024.10994848
Primary Topic
Radar Systems and Signal Processing
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Extended Order Statistic CFAR Detection For Multiple interfering targets in Log-Normal Clutter

Amar Mezache, Khaled Zebiri
Radar Systems and Signal Processing
article

Extended Order Statistic CFAR Detection For Multiple interfering targets in Log-Normal Clutter

Amar Mezache, Khaled Zebiri
article en

Abstract

This communication examines Constant False Alarm Rate (CFAR) detection in the presence of secondary targets within a log-normal background. The modified Order Statistic (OS) CFAR is derived from a universal test statistic, which is expressed as a function of two different scale-invariant functions. This approach combines the conventional OS-CFAR and Weber-Haykin (WH) CFAR algorithms, resulting in a CFAR detector characterized by three ordered statistics. The CFAR properties and detection performance of the proposed threshold are evaluated against existing CFAR methods in both homogeneous and heterogeneous clutter scenarios. Monte Carlo simulations demonstrate that the proposed WHOS-CFAR detector maintains robustness even in the presence of strong interfering targets.

University Mohamed Boudiaf of M'sila (DZ)
Climate action
Openalex Percentile: Top 51%
Radar Systems and Signal Processing
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.