Cyclic weighted centroid localization for spectrally overlapped sources in cognitive radio networks
We consider the problem of localizing spectrally overlapped sources in cognitive radio networks. A new weighted centroid localization algorithm (WCL) called Cyclic WCL is proposed, which exploits the cyclostationary feature of the target signal to estimate its location coordinates. In order to analyze the algorithm in terms of root-mean-square error (RMSE), we model the location estimates as the ratios of quadratic forms in a Gaussian random vector. With analysis and simulation, we show the impact of the interferer location and its modulation scheme on the RMSE. We also study the RMSE performance of the algorithm for different power levels of the target and the interference. Further, the comparison between Cyclic WCL and WCL w/o cyclostationarity is presented. It is observed that the Cyclic WCL provides significant performance gain over WCL.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Cyclic Weighted Centroid Algorithm for Transmitter Localization in the Presence of Interference
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-co…
CenterLoc3D: Monocular 3D Vehicle Localization Network for Roadside Surveillance Cameras
Monocular 3D vehicle localization is an important task in Intelligent Transportation System (ITS) and Cooperative Vehicle Infrastructure System (CVIS), which is usually achieved by monocular 3D vehicle detection. However…
Camera Calibrationvehicle detectionA Support Vector Approach in Segmented Regression for Map-assisted Non-cooperative Source Localization
This paper presents a non-cooperative source localization approach based on received signal strength (RSS) and 2D environment map, considering both line-of-sight (LOS) and non-line-of-sight (NLOS) conditions. Conventiona…
regressionSafeNav: Safe Path Navigation using Landmark Based Localization in a GPS-denied Environment
In battlefield environments, adversaries frequently disrupt GPS signals, requiring alternative localization and navigation methods. Traditional vision-based approaches like Simultaneous Localization and Mapping (SLAM) an…
Sensor FusionSimultaneous Localization and MappingVisual LocalizationVisual OdometryData Centroid Based Multi-Level Fuzzy Min-Max Neural Network
Recently, a multi-level fuzzy min max neural network (MLF) was proposed, which improves the classification accuracy by handling an overlapped region (area of confusion) with the help of a tree structure. In this brief, a…
ClassificationGeneral Classification