Device-Free Localization via an Extreme Learning Machine with Parameterized Geometrical Feature Extraction

Zhang, Jie; Xiao, Wendong; Zhang, Sen; Huang, Shoudong

doi:10.3390/s17040879

Cited by 48 publications

(23 citation statements)

References 51 publications

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“…The UWB data is time dependent and LSTM model is the best deep learning model for UWB localization as compared to recurrent neural networks (RNN) [30], and extreme learning machine (ELM) [31] models. LSTM is the improved form of RNN and it is used to connect historical information to the current input.…”

Section: Lstm Based Uwb Localizationmentioning

confidence: 99%

UWB Indoor Localization Using Deep Learning LSTM Networks

2020

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Localization using ultra-wide band (UWB) signals gives accurate position results for indoor localization. The penetrating characteristics of UWB pulses reduce the multipath effects and identify the user position with precise accuracy. In UWB-based localization, the localization accuracy depends on the distance estimation between anchor nodes (ANs) and the UWB tag based on the time of arrival (TOA) of UWB pulses. The TOA errors in the UWB system, reduce the distance estimation accuracy from ANs to the UWB tag and adds the localization error to the system. The position accuracy of a UWB system also depends on the line of sight (LOS) conditions between the UWB anchors and tag, and the computational complexity of localization algorithms used in the UWB system. To overcome these UWB system challenges for indoor localization, we propose a deep learning approach for UWB localization. The proposed deep learning model uses a long short-term memory (LSTM) network for predicting the user position. The proposed LSTM model receives the distance values from TOA-distance model of the UWB system and predicts the current user position. The performance of the proposed LSTM model-based UWB localization system is analyzed in terms of learning rate, optimizer, loss function, batch size, number of hidden nodes, timesteps, and we also compared the mean localization accuracy of the system with different deep learning models and conventional UWB localization approaches. The simulation results show that the proposed UWB localization approach achieved a 7 cm mean localization error as compared to conventional UWB localization approaches.

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Section: Lstm Based Uwb Localizationmentioning

confidence: 99%

UWB Indoor Localization Using Deep Learning LSTM Networks

2020

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“…In order to tackle the above issues, device-free localization (DFL) was introduced as a new radio frequency (RF)-based localization approach, where the target does not need to attach any electronic device [5]. DFL can be applied to both indoor and outdoor environments, and is particularly useful in smoky, dark, and obscure scenarios, which makes it an attractive and promising technique for a variety of applications [6]. In a DFL system, radio transmitters (RTs) and radio receivers (RXs) are used as the sensor nodes to sense the target collaboratively.…”

Section: Introductionmentioning

confidence: 99%

Multilayer probability extreme learning machine for device-free localization

Zhang

Xiao

et al. 2020

Neurocomputing

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Device-free localization (DFL) is becoming one of the new techniques in wireless localization field, due to its advantage that the target to be localized does not need to attach any electronic device. One of the key issues of DFL is how to characterize the influence of the target on the wireless links, such that the target's location can be accurately estimated by analyzing the changes of the signals of the links. Most of the existing related research works usually extract the useful information from the links through manual approaches, which are labor-intensive and time-consuming. Deep learning approaches have attempted to automatically extract the useful information from the links, but the training of the conventional deep learning approaches are time-consuming, because a large number of parameters need to be fine-tuned multiple times. Motivated by the fast learning speed and excellent generalization performance of extreme learning machine (ELM), which is an emerging training approach for generalized single hidden layer feedforward neural networks (SLFNs), this paper proposes a novel hierarchical ELM based on deep learning theory, named multilayer probability ELM (MP-ELM), for automatically extracting the useful information from the links, and implementing fast and accurate DFL. The proposed MP-ELM is stacked by ELM autoencoders, so it also keeps the very fast learning speed of ELM. In addition, considering the uncertainty and redundant links existing in DFL, MP-ELM outputs the probabilistic estimation of the target's location instead of the deterministic output. The validity of the proposed MP-ELM-based DFL is evaluated both in the indoor and the outdoor environments, respectively. Experimental results demonstrate that the proposed MP-ELM can obtain better performance compared with classic ELM, multilayer ELM (ML-ELM), hierarchical ELM (H-ELM), deep belief network (DBN), and deep Boltzmann machine (DBM).

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“…The salient features of ELM are that its hidden layer parameters do not require manual intervention and can be assigned randomly before training, and the output weight is determined analytically via the least squares estimation method, making it easy to implement with better generalization performance and faster learning speed [ 7 , 8 , 9 ]. Nowadays, ELM has been widely used in many fields, such as landmark recognition [ 10 ], industrial production [ 11 ], electronic nose [ 12 ], localization [ 13 , 14 ], etc.…”

Section: Introductionmentioning

confidence: 99%

A Novel Online Sequential Extreme Learning Machine for Gas Utilization Ratio Prediction in Blast Furnaces

Zhang

Yin

et al. 2017

Sensors

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Gas utilization ratio (GUR) is an important indicator used to measure the operating status and energy consumption of blast furnaces (BFs). In this paper, we present a soft-sensor approach, i.e., a novel online sequential extreme learning machine (OS-ELM) named DU-OS-ELM, to establish a data-driven model for GUR prediction. In DU-OS-ELM, firstly, the old collected data are discarded gradually and the newly acquired data are given more attention through a novel dynamic forgetting factor (DFF), depending on the estimation errors to enhance the dynamic tracking ability. Furthermore, we develop an updated selection strategy (USS) to judge whether the model needs to be updated with the newly coming data, so that the proposed approach is more in line with the actual production situation. Then, the convergence analysis of the proposed DU-OS-ELM is presented to ensure the estimation of output weight converge to the true value with the new data arriving. Meanwhile, the proposed DU-OS-ELM is applied to build a soft-sensor model to predict GUR. Experimental results demonstrate that the proposed DU-OS-ELM obtains better generalization performance and higher prediction accuracy compared with a number of existing related approaches using the real production data from a BF and the created GUR prediction model can provide an effective guidance for further optimization operation.

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Device-Free Localization via an Extreme Learning Machine with Parameterized Geometrical Feature Extraction

Cited by 48 publications

References 51 publications

UWB Indoor Localization Using Deep Learning LSTM Networks

UWB Indoor Localization Using Deep Learning LSTM Networks

Multilayer probability extreme learning machine for device-free localization

A Novel Online Sequential Extreme Learning Machine for Gas Utilization Ratio Prediction in Blast Furnaces

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