A K-nearest neighbor indoor fingerprint location method based on coarse positioning circular domain and the highest similarity threshold

Li, Xiaonian; Dai, Zhicheng; He, Lijuan

doi:10.1088/1361-6501/ac924b

Cited by 6 publications

(5 citation statements)

References 27 publications

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“…To further validate the efectiveness of model improvement, we compared the number of parameters, network training time, and network testing time between the proposed SIR-CNN and the other four networks. As shown in Table 6, it can be seen that the proposed SIR-CNN has fewer network parameters, which is consistent with the analysis of equation (14). We also noticed that the number of parameters has an impact on the training and testing times of the network.…”

Section: Te Efectiveness Of Sir-cnnsupporting

confidence: 83%

“…More than 80% of the existing literature on bearing fault diagnosis employs vibration signal-analysis methods. Tis method usually uses manual approaches such as the fast Fourier transform (FFT) [10], wavelet transform (WT) [11], and empirical mode decomposition (EMD) [12] to extract signal features and then uses a support vector machine (SVM) [13], K-nearest neighbor (KNN) [14], and BP neural network (BPNN) [15] to obtain diagnostic results. However, these feature extraction methods rely on expert experience and knowledge, which can easily introduce artifcial errors and have poor generalization ability.…”

Section: Introductionmentioning

confidence: 99%

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An Integrated Bearing Fault Diagnosis Method Based on Multibranch SKNet and Enhanced Inception-ResNet-v2

Hu,

Liu,

et al. 2024

Shock and Vibration

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Deep learning has recently received extensive attention in the field of rolling-bearing fault diagnosis owing to its powerful feature expression capability. With the help of deep learning, we can fully extract the deep features hidden in the data, significantly improving the accuracy and efficiency of fault diagnosis. Despite this progress, deep learning still faces two outstanding problems. (1) Each layer uses the same convolution kernel to extract features, making it difficult to adaptively select convolution kernels based on the features of the input image, which limits the network’s adaptability to different input features and leads to weak feature extraction. (2) Large number of parameters and long training time. To solve the above problems, this paper proposes an integrated deep neural network that combines an improved selective kernel network (SKNet) with an enhanced Inception-ResNet-v2, named SIR-CNN. First, based on the SKNet, a new three-branch SKNet was designed. Second, the new SKNet is embedded into a depthwise separable convolution network such that the model can adaptively select convolution kernels of different sizes during training. Furthermore, the convolution structure in the Inception-ResNet-v2 network was replaced by the improved depthwise separable convolution network to achieve effective feature extraction. Finally, the time-frequency maps of the raw vibration signals are obtained through short-time Fourier transform (STFT) and then sent to the proposed SIR-CNN network for experiments. The experimental results show that the proposed SIR-CNN achieves superior performance compared to other methods.

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Section: Te Efectiveness Of Sir-cnnsupporting

confidence: 83%

Section: Introductionmentioning

confidence: 99%

An Integrated Bearing Fault Diagnosis Method Based on Multibranch SKNet and Enhanced Inception-ResNet-v2

Hu,

Liu,

et al. 2024

Shock and Vibration

View full text Add to dashboard Cite

show abstract

“…This method is robust against fluctuations in the RSSI, as well as deviations from the measured RSS. Zhao et al [21] proposed an asymmetric Gaussian filtering algorithm IWKNN based on the signal strength distribution characteristics of smart venues. The method combines a specific signal distribution model and proposes asymmetric Gaussian filtering to improve generalization ability.…”

Section: Related Workmentioning

confidence: 99%

A fingerprint location framework for uneven WiFi signals based on machine learning

Lu,

Zhong,

Guan

et al. 2024

IEEE Latin Am. Trans.

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WiFi fingerprint positioning is a common method for indoor location determination. Existing methods are susceptible to fluctuations in WiFi signal strength during the offline phase, leading to unevenly received signals. Additionally, during online positioning, there is a lack of integration with historical trajectory information. These problems can result in errors in both offline fingerprint acquisition and online location positioning. To address these problems, we propose a method that combines normality detection in the offline phase and Location Weighted K-nearest Neighbor(LWKNN) positioning in the online phase. In the offline phase, initial Received Signal Strength Indication(RSSI) samples undergo preprocessing based on skewness and kurtosis for normality detection. If the samples conform to a normal distribution model, the probability density is estimated using the normal distribution function. If not, estimation occurs using the kernel density function model. Subsequently, values are averaged after Kalman filtering to establish a high-precision fingerprint database. During the online positioning phase, the LWKNN algorithm is employed. Initially, the Weighted K-nearest Neighbor method estimates the position, and this information is utilized as features to construct a Long Short Term Memory(LSTM) network model. The optimal path is determined through the least square method. Finally, the obtained outputs are integrated with historical data from the fingerprint positioning trajectory to enhance target positioning accuracy. Experimental results demonstrate that our indoor localization method significantly improves WiFi fingerprint localization accuracy compared to traditional localization methods.

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“…After the transmitter and receiver are determined, the RSS measurement is only related to Gaussian noise. According to equation (17), the RSS measurements received by AP p and AP q at p and q meters away from the radio transmitter can be expressed as equations (18) and (19), respectively. By subtracting the RSS received from AP i and AP j , the RSSD can be obtained as shown in equation (20),…”

Section: Improved Online Rssd Vectormentioning

confidence: 99%

“…The matching methods mainly include distance measurement and similarity measurement. Euclidean distance is used as a matching basis by classical algorithms such as nearest neighbor (NN), k-nearest neighbor (KNN) and weighted k-nearest neighbor (WKNN) [18][19][20]. The positioning accuracy of NN and KNN algorithms based on RSSD was explored in [21].…”

Section: Introductionmentioning

confidence: 99%

Indoor unknown radio transmitter localization using improved RSSD and grey correlation degree

Zhang,

Xu,

Gao

et al. 2024

Meas. Sci. Technol.

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Accurate location of unknown radio transmitter (URT) is the key to secure wireless communication. Since the fingerprint positioning methods based on received signal strength difference (RSSD) can adapt to the diversity of transmitting power and frequency, RSSD has become a popular scheme for locating the unknown radio transmitter. However, the RSSD is obtained by subtracting the RSS from two different access points (APs), so the interference of noise on the RSS is inherited and amplified by the RSSD. Besides, the need for more APs to ensure positioning accuracy leads to an increase in hardware costs. In this paper, a RSSD-based fuzzy weight grey correlation degree positioning algorithm, called FUZZY-GREY, is proposed to reduce the interference of noise, save AP hardware cost and improve the positioning accuracy. Firstly, online RSSD vector is improved by using fuzzy weight to reduce the noise interference. Secondly, the RSSD-based grey correlation coefficient is designed to calculate the correlation degree of the corresponding RSSD and ensure data integrity. Finally, a RSSD-based grey correlation degree scheme combining with fuzzy weight is proposed to select optimal reference points (RPs). Simulation and experimental results show that the proposed algorithm has better positioning performance than weighted k-nearest neighbor (WKNN), maximum correlation coefficient estimation (MCORE), Naive Bayes and support vector machine (SVM) in the case of different selected K numbers, grid distances, noise levels and AP numbers.

show abstract

A K-nearest neighbor indoor fingerprint location method based on coarse positioning circular domain and the highest similarity threshold

Cited by 6 publications

References 27 publications

An Integrated Bearing Fault Diagnosis Method Based on Multibranch SKNet and Enhanced Inception-ResNet-v2

An Integrated Bearing Fault Diagnosis Method Based on Multibranch SKNet and Enhanced Inception-ResNet-v2

A fingerprint location framework for uneven WiFi signals based on machine learning

Indoor unknown radio transmitter localization using improved RSSD and grey correlation degree

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