Through-Wall Detection with LS-SVM under Unknown Wall Characteristics

One of the main challenges in through-wall imaging (TWI) is the presence of the walls, whose returns tend to obscure the target behind the walls and must be considered and computed in the imaging procedure. In this paper, a two-step procedure for the through-wall detection is proposed. Firstly, an effective clutter mitigation method based on singular value decomposition (SVD) is used. It does not require knowledge of the background scene or rely on accurate modeling and estimation of wall parameters. Then, TWI problem is cast as a regression one and solved by means of least-squares support vector machine (LS-SVM). The complex scattering process due to the presence of the walls is automatically included in the nonlinear relationship between the feature vector extracted from the target scattered fields and the position of the target. The relationship is obtained through a training phase using LS-SVM. Simulated results show that the proposed approach is effective. We also analyze the impacts of training samples and signalto-noise ratio (SNR) on test detection accuracy. Simulated results reveal that the proposed LS-SVM based approach can provide comparative performances in terms of accuracy, convergence, robustness, and generalization in comparison with the support vector machine (SVM) based approach

Indoor localization techniques using wireless network and artificial neural network

This research focuses on improving indoor localization using wireless network and artificial neural network (ANN). This involves strategic study on wireless signal behavior and propagation inside buildings, suitable propagation model to simulate indoor propagation and evaluations on different localization methods such as distance based, direction based, time based and signature based. It has been identified that indoor signal propagation impairments are severe, non-linear and custom to a specific indoor location. To accommodate these impairments, an ANN is proposed to provide a viable solution for indoor location prediction as it learns the location specific parameters during training, and then performs positioning based on the trained data, while being robust to severe and non-linear propagation effects. The versatility of ANN allows different setup and optimization possibilities to affect location prediction capabilities. This research identified the best feedforward backpropagation neural network configuration for the generated simulation data and introduced a new optimization method. Indoor-specific received signal strength data were developed with the Lee’s in-building model according to a custom indoor layout. Simulation work was done to test localization performance with different feedforward backpropagation neural network setups with the generated received signal strength data as input. A data preparation method that converts the received signal strength raw data into average, median, min and max values prior to be fed into the neural network process was carried out. The method managed to increase location prediction performance using feedforward neural network with two hidden layers trained with Bayesian Regularization algorithm producing root mean squared error of 0.0821m, which is 50% better in comparison to existing research work. Additional tests conducted with six different relevant scenarios verified the scheme for localization performance robustness. In conclusion, the research has improved the performance of indoor localization using wireless network and ANN

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

An Efficient and Accurate Indoor Positioning System

In this thesis, an indoor localization method using on-line independent support vector machine (OISVM) classification method and under-sampling techniques is proposed. The proposed positioning method is based on the received signal strength indicator (RSSI) of Wi-Fi signals. A new under-sampling algorithm is developed to address the imbalanced data problem associated with the OISVM, and a kernel function parameter selection algorithm is introduced for the training process. The time complexity of both the training process and the prediction process are decreased. Comparative experimental results indicate that the training speed and the prediction speed are improved by at least 10 times and 5 times, respectively. Furthermore, through on-line learning, the estimation error is decreased by 0.8m. Such an improvement makes the proposed method an ideal indoor positioning solution for portable devices where the processing power and the memory capacity are limited. A new Particle Filter (PF) scheme for indoor localization using Wi-Fi received signal strength indicator (RSSI) and inertial sensor measurements has also been presented. RSSI is affected significantly by multipath fading, building structure and obstacles in indoor environments. The information provided by inertial sensors combined with the proposed particle filter are used to develop a positioning algorithm supporting a smooth and stable localization experience. To differentiate similar fingerprints, a single-hidden layer feedforward networks (SLFNs) is used to model the multiple probabilistic estimations and to improve the performance of the PF. A new initialization algorithm using Random Sample Consensus (RANSAC) has also been presented to reduce the convergence time. Experimental measurements were carried out to determine the performance of the proposed algorithm. The results indicate that the positioning error falls to less than 1.2 (m)

Mobile user positioning in public land mobile networks by using methods based on support vector machines.

Tokom prethodnih godina, potreba za podrškom sve većeg broja LBS (Location Based Services) servisa dovela je do intezivnog razvoja tehnika za pozicioniranje mobilnih korisnika (objekata) u radio sistemima. Pri tom, zahtevi koje sistemi za pozicioniranje treba da ispune, prvenstveno po pitanju tačnosti, ali i po pitanju kašnjenja, dostupnosti servisa, kompleksnosti i cene implementacije, postaju sve strožiji...Over the last years, the necessity of providing the support for various Location Based Services (LBS) has led to the intensive development of the techniques for mobile user (objects) positioning in radio systems. At the same time, the requirements that need to be fulfilled by the positioning technique in terms of accuracy, latency, availability, complexity and implementation costs, are getting higher..