# Cyclic Weighted Centroid Algorithm for Transmitter Localization in the Presence of Interference

## TL;DR

This paper introduces a cyclic weighted centroid localization algorithm that leverages cyclic autocorrelation to accurately locate transmitters amidst interference in cognitive radio networks, improving robustness over traditional methods.

## Contribution

It proposes a novel cyclic WCL algorithm utilizing cyclic autocorrelation and feature variation coefficient for interference mitigation in transmitter localization.

## Key findings

- Cyclic WCL outperforms traditional WCL in interfered environments.
- Eliminating CRs near the interferer enhances localization accuracy.
- Theoretical analysis provides RMSE estimates for the proposed method.

## Abstract

This paper addresses the problem of localizing a non-cooperative transmitter in the presence of a spectrally overlapped interferer in a cognitive receiver (CR) network. It has been observed that the performance of non-cooperative weighted centroid localization (WCL) algorithm degrades in the presence of a spectrally overlapped interferer. We propose cyclic WCL algorithm that uses cyclic autocorrelation (CAC) of received signals at CRs in the network to estimate the location coordinates of the target transmitter. Performance of the proposed algorithm is further improved by eliminating CRs in the vicinity of the interferer from the localization process. In order to identify and eliminate CRs in the vicinity of the interferer, the ratio of the variance and the mean of the square of absolute value of the CAC, referred to as feature variation coefficient, is used. Theoretical analysis of the cyclic WCL algorithm is presented in order to compute the root mean square error in the location estimates. We further study the impacts of the interferer's power and location, CR density, and fading environment on the performance of cyclic WCL. The comparison between cyclic WCL and traditional WCL is also presented.