# A k-nearest neighbors approach to the design of radar detectors

## TL;DR

This paper explores using a k-nearest neighbors (KNN) method for designing radar detectors, demonstrating its effectiveness and theoretical properties such as CFAR, with simulations validating the approach.

## Contribution

It introduces a KNN-based radar detector design that leverages raw data or receiver statistics, providing theoretical analysis and performance characterization.

## Key findings

- The KNN detector has the CFAR property.
- Closed-form expressions for detection and false alarm probabilities are derived.
- Simulations confirm the effectiveness of the proposed approach.

## Abstract

A k-nearest neighbors (KNN) approach to the design of radar detectors is investigated. The idea is to start with either raw data or well-known radar receiver statistics as feature vector to be fed to the KNN decision rule. In the latter case, the probability of false alarm and probability of detection are characterized in closed-form; moreover, it is proved that the detector possesses the constant false alarm rate (CFAR) property and the relevant performance parameters are identified. Simulation examples are provided to illustrate the effectiveness of the proposed approach.