A Bayesian Model Based on Link Distribution Features for Multitarget Passive Localization in Visible Light Sensing

IF 4.3 2区综合性期刊 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Sensors Journal Pub Date : 2025-04-23 DOI:10.1109/JSEN.2025.3561317

Shuai Zhang;Liyi Zhang;Kaihua Liu;Ya Wang

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引用次数: 0

Abstract

Passive localization using visible light (VL) sensing has been considered as a promising solution for indoor human detection. A major challenge is to avoid false target positioning in multitarget positioning scenarios. Besides, a reasonable probable target area model is also critical to the accuracy of positioning and counting targets. In this article, a novel passive localization scheme is proposed to locate multiple targets. This scheme introduces a rectangular probable target area model, which is used to calculate a rectangular area containing the potential location of the target to be located. Compared with existing models, it is more suitable for positioning scenarios under low-density deployment of sensing nodes. Furthermore, we present an improved successive cancellation (SC) algorithm to excluding false target localization. To determine the authenticity of targets by the SC algorithm, a Bayesian model is introduced to optimize the SC algorithm according to the multidimensional shadowed link information of candidate targets. Numerous simulation results show that the proposed multitarget passive localization scheme can improve the problem of false target localization in multitarget localization scenarios. And it also can achieve outstanding performance in localization accuracy.

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基于链路分布特征的贝叶斯模型在可见光传感多目标被动定位中的应用

利用可见光（VL）传感技术进行被动定位被认为是一种很有前途的室内人体检测方法。在多目标定位场景中，如何避免错误的目标定位是一个主要的挑战。此外，合理的可能目标面积模型对目标的定位和计数精度也至关重要。本文提出了一种新的多目标无源定位方案。该方案引入了矩形可能目标面积模型，用于计算包含待定位目标潜在位置的矩形面积。与现有模型相比，更适合低密度部署的传感节点定位场景。在此基础上，提出了一种改进的连续消去算法来排除错误的目标定位。为了利用SC算法确定目标的真实性，引入贝叶斯模型，根据候选目标的多维阴影链路信息对SC算法进行优化。大量仿真结果表明，所提出的多目标被动定位方案可以改善多目标定位场景中目标定位错误的问题。在定位精度方面也能取得优异的成绩。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Sensors Journal 工程技术-工程：电子与电气

CiteScore

7.70

自引率

14.00%

发文量

2058

审稿时长

5.2 months

期刊介绍： The fields of interest of the IEEE Sensors Journal are the theory, design , fabrication, manufacturing and applications of devices for sensing and transducing physical, chemical and biological phenomena, with emphasis on the electronics and physics aspect of sensors and integrated sensors-actuators. IEEE Sensors Journal deals with the following: -Sensor Phenomenology, Modelling, and Evaluation -Sensor Materials, Processing, and Fabrication -Chemical and Gas Sensors -Microfluidics and Biosensors -Optical Sensors -Physical Sensors: Temperature, Mechanical, Magnetic, and others -Acoustic and Ultrasonic Sensors -Sensor Packaging -Sensor Networks -Sensor Applications -Sensor Systems: Signals, Processing, and Interfaces -Actuators and Sensor Power Systems -Sensor Signal Processing for high precision and stability (amplification, filtering, linearization, modulation/demodulation) and under harsh conditions (EMC, radiation, humidity, temperature); energy consumption/harvesting -Sensor Data Processing (soft computing with sensor data, e.g., pattern recognition, machine learning, evolutionary computation; sensor data fusion, processing of wave e.g., electromagnetic and acoustic; and non-wave, e.g., chemical, gravity, particle, thermal, radiative and non-radiative sensor data, detection, estimation and classification based on sensor data) -Sensors in Industrial Practice