# A Nonlinear Transform-Based Variability Index CFAR Detector for Doppler-Extended Targets

## TL;DR

This paper introduces a new radar detection method to better identify moving targets in complex environments by improving false alarm control and detection accuracy.

## Contribution

A novel nonlinear transform-based variability index CFAR detector is proposed for Doppler-extended targets in FMCW radar systems.

## Key findings

- The proposed DET-NTVI-CFAR method achieves stable false alarm control in complex clutter backgrounds.
- Simulation and field test results show improved detection probability for Doppler-extended targets.
- Threshold parameters are derived to guide the selection of detection branches under varying conditions.

## Abstract

In frequency-modulated continuous-wave (FMCW) radar systems, the detection of Doppler-extended targets (DETs) is a critical challenge. The micro-Doppler effects induced by the motion of extended targets such as pedestrians cause the echo energy to spread along the Doppler dimension. As a result, a single range-Doppler cell is unlikely to form a pronounced amplitude peak above the background noise level. Consequently, existing constant false alarm rate (CFAR) methods that rely on single-cell amplitude decisions tend to suffer from performance degradation in DET scenarios and exhibit limited adaptability under varying clutter conditions. To solve these issues, we propose a nonlinear transform–based variability index CFAR detector for DET (DET-NTVI-CFAR), with the aim of improving detection probability and maintaining stable false alarm control in complex clutter backgrounds. This work constructs a detection statistic by applying a nonlinear transform to the accumulated power cells and derives the threshold from the corresponding probability distribution model. A variability index CFAR (VI-CFAR) decision strategy is introduced to select the appropriate detection branch under different operating conditions. In the threshold design stage, the false alarm probability expressions of three sub-detection methods are derived to guide the selection of threshold parameters. Simulation results demonstrate that the proposed method achieves stable false alarm control and improves detection probability in various environments. Field test results also confirm the applicability of the DET-NTVI-CFAR detector.