Resilient Fusion of LMB Densities Based on Medoids

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

IEEE Sensors Journal Pub Date : 2025-02-03 DOI:10.1109/JSEN.2025.3534989

Yao Zhou;Lin Gao;Gaiyou Li;Ping Wei

引用次数: 0

Abstract

This article deals with the problem of resilient fusion of labeled multi-Bernoulli (LMB) densities, which arises in the situation that the sensor network (SN) undergoes abnormal behaviors like malicious attacks, resulting in the change of transmitted data from each sensor node. Compared to fusion algorithms based on perfect SN conditions, a detection procedure should be deployed before performing fusion so as to exclude abnormal data. To this end, we propose to decompose the LMB densities as the union of Bernoulli components (BCs), and then the medoids of BCs are exploited to form the fused LMB density. Besides, a new density-based spatial clustering of applications with noise (DBSCAN)-based label-matching algorithm is proposed. The performance of the proposed algorithm is verified via simulations.

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本文讨论的是标注多伯努利（LMB）密度的弹性融合问题，该问题出现在传感器网络（SN）发生恶意攻击等异常行为，导致每个传感器节点传输的数据发生变化的情况下。与基于完美传感器网络条件的融合算法相比，在进行融合之前应部署一个检测程序，以排除异常数据。为此，我们建议将 LMB 密度分解为伯努利分量（BC）的结合，然后利用 BC 的中间值形成融合的 LMB 密度。此外，还提出了一种新的基于密度的带噪声应用空间聚类（DBSCAN）标签匹配算法。通过仿真验证了所提算法的性能。

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