WLAN indoor target intrusion detection algorithm based on  adaptive-depth ray tree

[1] Huggins K R, Mcgrath M A, Zheng Y F, et al. Computer vision localization based on pseudo-satellites. IEEE Aerospace and Electronic Systems Magazine, 2010, 25: 4--10. Google Scholar

[2] Jo K, Chu K, Sunwoo M. Interacting Multiple Model Filter-Based Sensor Fusion of GPS With In-Vehicle Sensors for Real-Time Vehicle Positioning. IEEE Trans Intell Transp Syst, 2012, 13: 329-343 CrossRef Google Scholar

[3] Wei S, Zhang W, Deng C. BriGuard: a lightweight indoor intrusion detection system based on infrared light spot displacement. IET Sci Measurement Tech, 2015, 9: 306-314 CrossRef Google Scholar

[4] Li Y P, Yang J, Li X, et al. Ultrasonic intruder detection system for home security. Intelligent Control and Automation, 2006: 1108--1115 doi: 10.1007/978-3-540-37256-1_143. Google Scholar

[5] Bo K, Kvarstein B, Hagen R, et al. Pelvic floor muscle exercise for the treatment of female stress urinary incontinence: reliability of vaginal pressure measurements of pelvic floor muscle strength. Neurourology Urodynamics, 2010, 9: 471--477. Google Scholar

[6] Yuan W, Wu N, Wang H, et al. Factor graph approach for joint passive localization and receiver synchronization in wireless sensor networks. In: Proceedings of IEEE Conference on Annual International Symposium on Personal, Indoor, and Mobile Radio Communications, Valencia, 2016. 1--5. Google Scholar

[7] Youssef M, Mah M, Agrawala A. Challenges: device-free passive localization for wireless environments. In : Proceedings of ACM International Conference on Mobile Computing and Networking, Montreal, 2007. 222--229. Google Scholar

[8] Jin S, Choi S. A Seamless Handoff With Multiple Radios in IEEE 802.11 WLANs. IEEE Trans Veh Technol, 2014, 63: 1408-1418 CrossRef Google Scholar

[9] Wang Q, Yigitler H, Jantti R. Localizing Multiple Objects Using Radio Tomographic Imaging Technology. IEEE Trans Veh Technol, 2016, 65: 3641-3656 CrossRef Google Scholar

[10] Condell J, Deak D, Deak G. Detection of multi-occupancy using device-free passive localisation. IET Wireless Sens Syst, 2014, 4: 130-137 CrossRef Google Scholar

[11] Savazzi S, Nicoli M, Carminati F. A Bayesian Approach to Device-Free Localization: Modeling and Experimental Assessment. IEEE J Sel Top Signal Process, 2014, 8: 16-29 CrossRef ADS Google Scholar

[12] Liu Z Y, Guo L X, Tao W. Full automatic preprocessing of digital map for 2.5D ray tracing propagation model in urban microcellular environment. Waves Random Complex Media, 2013, 23: 267-278 CrossRef Google Scholar

[13] Specht D F. Probabilistic neural network and general regression neural network. Fuzzy Logic Neural Netw Handbook, 1996, 3: 301--344. Google Scholar

[14] Mahmoud S F, Wait J R. Geometrical optical approach for electromagnetic wave propagation in rectangular mine tunnels. Radio Sci, 2016, 9: 1147--1158. Google Scholar

[15] Ang T, Tan S, Tan H. Analytical methods to determine diffraction points on multiple edges and cylindrical scatterers in UTD ray-tracing. Microw Opt Tech Lett, 2015, 22: 304--309. Google Scholar

[16] Dersch U, Zollinger E. Propagation mechanisms in microcell and indoor environments. IEEE Trans Veh Technol, 1994, 43: 1058-1066 CrossRef Google Scholar

[17] Farace P, Righetto R, Deffet S. Technical Note: A direct ray-tracing method to compute integral depth dose in pencil beam proton radiography with a multilayer ionization chamber. Med Phys, 2016, 43: 6405-6412 CrossRef PubMed ADS Google Scholar

[18] Coco S, Laudani A, Mazzurco L. A Novel 2-D Ray Tracing Procedure for the Localization of EM Field Sources in Urban Environment. IEEE Trans Magn, 2004, 40: 1132-1135 CrossRef ADS Google Scholar

[19] Sabar N R, Ayob M, Kendall G. A dynamic multiarmed bandit-gene expression programming hyper-heuristic for combinatorial optimization problems.. IEEE Trans Cybern, 2015, 45: 217-228 CrossRef PubMed Google Scholar

[20] Xu Y, Erricolo D, and Uslenghi P L E. A novel approach to 3D propagation in urban environments. In: Proceedings of IEEE Antennas and Propagation Society International Symposium, San Antonio, 2002. 338--341. Google Scholar

[21] Erceg V, Rustako A J, Roman R S. Diffraction around corners and its effects on the microcell coverage area in urban and suburban environments at 900 MHz, 2 GHz, and 4 GHz. IEEE Trans Veh Technol, 1994, 43: 762-766 CrossRef Google Scholar

[22] Hata M, Nagatsu T. Mobile location using signal strength measurements in a cellular system. IEEE Trans Veh Technol, 1980, 29: 245-252 CrossRef Google Scholar

[23] El-Sallabi H M, Vainikainen P. Improvements to Diffraction Coefficient for Non-Perfectly Conducting Wedges. IEEE Trans Antennas Propagat, 2005, 53: 3105-3109 CrossRef ADS Google Scholar

[24] Dutt V, Chaudhry V, Khan I. Different approaches in pattern recognition. Computer Science and Engineering, 2011, 1: 32--35. Google Scholar

[25] Queiroz A, Trintinalia L C. An analysis of human body shadowing models for ray-tracing radio channel characterization. In: Proceedings of IEEE Conference on Microwave and Optoelectronics, Galinhas, 2016. 1--5. Google Scholar

[26] Chendong D, Zhengjia H, Hongkai J. A sliding window feature extraction method for rotating machinery based on the lifting scheme. J Sound Vib, 2007, 299: 774-785 CrossRef ADS Google Scholar

[27] Battiti R, Colla A M. Democracy in neural nets: Voting schemes for classification. Neural Networks, 1994, 7: 691-707 CrossRef Google Scholar

[28] Stehman S V. Selecting and interpreting measures of thematic classification accuracy. Remote Sens Environ, 1997, 62: 77-89 CrossRef ADS Google Scholar

[29] Youssef M, Mah M, Agrawala A. Challenges: Device-free passive localization for wireless environments. In: Proceedings of ACM International Conference on Mobile Computing and Networking, Quebec, 2007. 222--229. Google Scholar

Table 1 The calculation process of each layer of PNN neurons

Functional layer	Number of neurons	Calculation process
Input layer	Signal feature dimension $d$	(1) Compute ${z_k}~=~{\boldsymbol~x}_k^{\rm{T}}{\boldsymbol~x}~(k~=~1,{\rm{~}}~\ldots~,~n)$, in which ${\boldsymbol~x}~=~({{{x_1}}~/~{\sqrt~{\sum\nolimits_{j~=~1}^d~{x_j^2}~}~}},~\ldots~,$ ${{{x_d}}~/~{\sqrt~{\sum\nolimits_{j~=~1}^d~{x_j^2}~}~}})$ and ${{\boldsymbol~x}_k}~=~({{{x_{k1}}}~/~{\sqrt~{\sum\nolimits_{j~=~1}^d~{x_{kj}^2}~}~}},{\rm{~}}~\ldots~,{\rm{~}}{{{x_d}}~/~{\sqrt~{\sum\nolimits_{j~=~1}^d~{x_{kj}^2}~}~}})$ are normalization vectors of test sample and $k$-th training sample, respectively, $n$ is the size of training samples. (2) Assign the connection weights $\omega_{jk}$ of the $j$-th neuron in the input layer and the $k$-th neuron in the model layer, and the assignment process is described in Algorithm 3.
Model layer	$n$	(1) Compute kernel density function $\varphi~({z_k})~=~\exp~(\frac{{{z_k}~-~1}}{{{\delta~^2}}})$, in which $\delta$ is the smoothing factor. (2) Assign the connection weights ${a_{ki}}~(i~=~1,{\rm{~}}~\ldots~,{\rm{~}}c)$ of the $k$-th neuron in the model layer and the $i$-th neuron in the summation layer, in which $c$ is the number of states. If ${\boldsymbol~x}_k$ belongs to $i$-th state, then $a_{ki}=1$, otherwise $a_{ki}=0$, as described in Algorithm 3.
Summation layer	$c$	Compute the conditional probability of ${\boldsymbol~x}$ belonging to the $i$-th state ${g_i}({\boldsymbol{x)}}~=~\frac{{{P_i}}}{{{N_i}}}\sum\nolimits_{k~\in~\{~1,{\rm{~}}~\ldots~,{\rm{~}}n\}~~\cup~{a_{ki}}~=~1}~{\varphi~({z_k})}$, in which $P_i$ is the priori probability of the $i$-th state and, $N_i$ is the number of training samples that belong to the $i$-th state.
Output layer	$1$	Compute$\max~\{~{g_i}({\boldsymbol~x}),{\rm{~}}i~\in~\{~1,{\rm{~}}~\ldots~,{\rm{~}}c\}~\}$, in which the $i$ that corresponds to the maximum value of $g_i({\boldsymbol~x})$ is the output state of PNN.

Table 2 Average time cost for ray modeling between each pair of AP and MP

Performance index Ref. [18] Ref. [12] The proposed method

Time overhead (s) 6.03 7.25 3.41

Table 3 Performance comparison of different intrusion detection methods

Performance index	MA	MV	Radio tomographic imaging	Neural network	The proposed method
Missed detection probability (%)	14.46	14.26	7.08	5.23	3.42
False detection probability (%)	13.85	10.40	12.47	9.01	7.96
Successful detection probability (%)	–	–	95.8	94.5	97.40
Average time overhead	2.35	2.46	9.77	6.57	6.62
for single detection (s)

0

Citations
0

Altmetric
Article metrics

Email
Export

WLAN indoor target intrusion detection algorithm based on adaptive-depth ray tree

Abstract

Funded by

References

0

0

Interesting search

Top 5Related Articles

Performance index	Ref. [18]	Ref. [12]	The proposed method
Time overhead (s)	6.03	7.25	3.41