MultiOutput Regression Indoor Localization Algorithm Based on Hybrid Grey Wolf Particle Swarm Optimization
Abstract
:1. Introduction
 In the data preprocessing phase, an improved DBSCAN clustering algorithm is proposed for denoising CSI amplitude, and autoencoders are used for feature extraction. These methods’ effectiveness is demonstrated compared with the standard DBSCAN and PCA algorithms.
 We employ MSVR for CSI fingerprint localization to bridge the gap in localization efficiency of SVR, and to our knowledge, this is the first time MSVR has been applied in CSI localization.
 An improved hybrid optimization algorithm, IPSOGWO, is proposed to adjust the hyperparameters of MSVR to obtain globally optimal parameters. Compared to the unimproved PSOGWO algorithm, the adjusted model can achieve optimal performance.
 The superiority of the proposed method is proven by comparing several domestic and international methods in two scenarios.
2. Framework and Methodologies
2.1. Systems Framework
2.2. Channel State Information
2.3. Noise Reduction
Algorithm 1: 2–3 uses ADBSCAN to remove CSI noise 
Input: Amplitude information X of a single RT link, iteration count of K Output: Denoised amplitude X^{*}

2.4. Feature Extraction
3. Improved Grey Wolf Particle Swarm Hybrid Optimization MSVR Localization Model Construction
3.1. MultiOutput Support Vector Regression
3.2. Grey Wolf Optimization Algorithm
3.3. The IPSOGWO Model
3.3.1. Improved Tent Chaos Mapping
3.3.2. Improved Location Updating Strategy
3.3.3. IPSOGWOMSVR Positioning Model
4. Experiments and Results Analysis
4.1. Data Collection
4.2. Comparison of Preprocessing Methods
4.3. Comparison with Similar Location Methods
4.4. Comparison of Advanced Positioning Techniques
5. Discussion and Conclusions
5.1. Discussion
5.2. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Specifications  Transmitter  Receiver 

Network Standard  IEEE 802.11 b/g/n  
Maximum Transmission Rate  300 Mbps  
Operating Band  2.4 GHz  
Number of Antennas  4  3 
MIMO Mode  2 × 2 MIMO  2 × 3 MIMO 
Reduction Algorithms  Parameters  Value 

AE  Coding dimension  20 
Iterations  200  
Activation functions (encoding and decoding layers)  tanh  
Adam optimizer learning rate  0.01  
Loss function  MSE  
PCA  Variance explained/number of principal components  99%/74 
KPCA  Kernel function  RBF 
Variance explained/number of principal components  99%/110 
Scenarios  Value  C  σ  γ 

Scenarios 1  GWOSVR  0.1  0.1  0.04 
GWOMSVR  0.1  0.1  0.66  
IPSOGWOMSVR  0.1  11.8  0.23  
NormalPSOGWOMSVR  0.1  9.7  0.62  
Scenarios 2  GWOSVR  0.3  0.1  0.05 
GWOMSVR  0.2  0.1  0.71  
IPSOGWOMSVR  0.2  8.1  0.47  
NormalPSOGWOMSVR  0.3  7.5  0.86 
Scenarios  Method  Maximum Error (m)  Minimum Error (m)  Mean Error (m) 

Scenario 1  FIFS  2.61  0.47  1.50 
Cmap  2.07  0.18  1.03  
LCAF  2.21  0.36  1.24  
PSOGWOMSVR  1.35  0.11  0.59  
Scenario 2  FIFS  3.27  0.42  2.20 
Cmap  2.44  0.33  1.34  
LCAF  2.56  0.32  1.58  
PSOGWOMSVR  1.92  0.41  1.12 
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Xie, S.; Yu, X.; Guo, Z.; Zhu, M.; Han, Y. MultiOutput Regression Indoor Localization Algorithm Based on Hybrid Grey Wolf Particle Swarm Optimization. Appl. Sci. 2023, 13, 12167. https://doi.org/10.3390/app132212167
Xie S, Yu X, Guo Z, Zhu M, Han Y. MultiOutput Regression Indoor Localization Algorithm Based on Hybrid Grey Wolf Particle Swarm Optimization. Applied Sciences. 2023; 13(22):12167. https://doi.org/10.3390/app132212167
Chicago/Turabian StyleXie, Shicheng, Xuexiang Yu, Zhongchen Guo, Mingfei Zhu, and Yuchen Han. 2023. "MultiOutput Regression Indoor Localization Algorithm Based on Hybrid Grey Wolf Particle Swarm Optimization" Applied Sciences 13, no. 22: 12167. https://doi.org/10.3390/app132212167