Powerset Fusion Network for Target Classification in Unattended Ground Sensors

Wang, Yan; Cheng, Xiaoliu; Li, Xiang; Li, Baoqing

doi:10.1109/jsen.2021.3067648

Cited by 5 publications

(2 citation statements)

References 21 publications

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“…Neural network (NN) algorithms perform very well for acoustic-based classification [ 52 , 53 ]. The NN classification algorithms perform non-linear computing based on a collection of connected artificial neurons, as shown in Figure 13 .…”

Section: Acoustic Recognitionmentioning

confidence: 99%

Acoustic Wake-Up Technology for Microsystems: A Review

Yang

Zhao

2023

Micromachines

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Microsystems with capabilities of acoustic signal perception and recognition are widely used in unattended monitoring applications. In order to realize long-term and large-scale monitoring, microsystems with ultra-low power consumption are always required. Acoustic wake-up is one of the solutions to effectively reduce the power consumption of microsystems, especially for monitoring sparse events. This paper presents a review of acoustic wake-up technologies for microsystems. Acoustic sensing, acoustic recognition, and system working mode switching are the basis for constructing acoustic wake-up microsystems. First, state-of-the-art MEMS acoustic transducers suitable for acoustic wake-up microsystems are investigated, including MEMS microphones, MEMS hydrophones, and MEMS acoustic switches. Acoustic transducers with low power consumption, high sensitivity, low noise, and small size are attributes needed by the acoustic wake-up microsystem. Next, acoustic features and acoustic classification algorithms for target and event recognition are studied and summarized. More acoustic features and more computation are generally required to achieve better recognition performance while consuming more power. After that, four different system wake-up architectures are summarized. Acoustic wake-up microsystems with absolutely zero power consumption in sleep mode can be realized in the architecture of zero-power recognition and zero-power sleep. Applications of acoustic wake-up microsystems are then elaborated, which are closely related to scientific research and our daily life. Finally, challenges and future research directions of acoustic wake-up microsystems are elaborated. With breakthroughs in software and hardware technologies, acoustic wake-up microsystems can be deployed for ultra-long-term and ultra-large-scale use in various fields, and play important roles in the Internet of Things.

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Section: Acoustic Recognitionmentioning

confidence: 99%

Acoustic Wake-Up Technology for Microsystems: A Review

Yang

Zhao

2023

Micromachines

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“…After establishing this correspondence through the sample feature data and training the network with determined parameters, the trained network then computes the input feature data to determine the corresponding category label. Wang et al [ 13 ] proposed a powerset fusion network (PFN) method to classify moving targets using acoustic-vibrational signal features. Xu et al [ 14 ] used the parallel recurrent neural network (PRNN) method to classify vibrating targets.…”

Section: Introductionmentioning

confidence: 99%

A Neural Network-Based Weighted Voting Algorithm for Multi-Target Classification in WSN

Zhang,

Zhou

2023

Sensors

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One of the most important applications in the wireless sensor networks (WSN) is to classify mobile targets in the monitoring area. In this paper, a neural network(NN)-based weighted voting classification algorithm is proposed on the basis of the NN-based classifier and combined with the idea of voting strategy, which is implemented on the nodes of the WSN monitoring system by means of the “upper training, lower transplantation” approach. The performance of the algorithm is verified by using real-world experimental data, and the results show that the proposed method has a higher accuracy in classifying the target signal features, achieving an average classification accuracy of about 85% when utilizing a deep neural network (DNN) and deep belief network (DBN) as the base classifier. The experiment reveals that the NN-based weighted voting algorithm enhances the target classification accuracy by approximately 5% in comparison to the single NN-based classifier, but the memory and computation time required for the algorithm to run are also increased at the same time. Compared to the FFNN classifier, which exhibited the highest classification accuracy among the four selected methods, the algorithm achieves an improvement of approximately 8.8% in classification accuracy. However, it incurs greater overhead time to run.

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Research on Moving Ground Target Classification Method for Seismic and Acoustic Sensors

Wang

Ding

et al. 2022

Lecture Notes in Electrical Engineering

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Powerset Fusion Network for Target Classification in Unattended Ground Sensors

Cited by 5 publications

References 21 publications

Acoustic Wake-Up Technology for Microsystems: A Review

Acoustic Wake-Up Technology for Microsystems: A Review

A Neural Network-Based Weighted Voting Algorithm for Multi-Target Classification in WSN

Research on Moving Ground Target Classification Method for Seismic and Acoustic Sensors

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