The increase of the technology related to radio localization and the exponential rise in the data traffic demanded requires a large number of base stations to be installed. This increase in the base stations density also causes a sharp rise in energy consumption of cellular networks. Consequently, energy saving and cost reduction is a significant factor for network operators in the development of future localization networks. In this paper, a localization method based on ray-tracing and fingerprinting techniques is presented. Simulation tools based on high frequencies are used to characterize the channel propagation and to obtain the ray-tracing data. Moreover, the fingerprinting technique requires a costly initial learning phase for cell fingerprint generation (radio-map). To estimate the localization of mobile stations, this paper compares power levels and delay between rays as cost function with different distance metrics. The experimental results show that greater accuracy can be obtained in the location process using the delay between rays as a cost function and the Mahalanobis distance as a metric instead of traditional methods based on power levels and the Euclidean distance. The proposed method appears well suited for localization systems applied to indoor and outdoor scenarios and avoids large and costly measurement campaigns.

Localization Approach Based on Ray-Tracing Simulations and Fingerprinting Techniques for Indoor–Outdoor Scenarios

A vehicle-deployed parts-per-billion in volume (ppbv)-level off-axis integrated cavity output spectroscopic (OA-ICOS) CH4/C2H6 sensor system was experimentally presented for mobile inspection of natural gas leakage in urban areas. For the time-division-multiplexing-based dual-gas sensor system, an antivibration 35-cm-long optical cavity with an effective path length of ∼2510 m was fabricated with a high-stability temperature and pressure control design. An Allan deviation analysis yielded a minimum detection limit of 0.2 ppbv for CH4 detection and 10 ppbv for C2H6 detection for a 1 s averaging time. A natural gas leakage source location algorithm was proposed using an improved hybrid Nelder-Mead simplex search method and a particle swarm optimization (NM-PSO) algorithm. For field industrial application, the accuracy of the sensor system and leakage source location algorithm was confirmed through a CH4/C2H6 cylinder leakage experiment on the campus. Furthermore, through natural gas pipeline network inspection measurements in urban areas, three types of leakage sources, including natural gas, biogas, and possible leakage source were respectively located and confirmed using the global positioning system and wind speed and direction measurement system, verifying the reliability and potential application of the vehicle-deployed inspection system for future natural gas pipeline leakage monitoring.

Vehicle-Deployed Off-Axis Integrated Cavity Output Spectroscopic CH4/C2H6 Sensor System for Mobile Inspection of Natural Gas Leakage.

As a key aspect of radio monitoring and positioning, direction of arrival (DOA) techniques have been widely used in wireless communications, radar detection, and radio astronomy. DOA estimation can be used to locate a source in space and provide technical support for signal enhancement. However, in complex electromagnetic environments, distributed source models have high complexity; thus, the existing DOA estimation algorithms for distributed sources cannot adapt to application scenarios that require high real-time performance. This letter derives a low-complexity distributed source model and proposes a novel DOA estimation algorithm based on the minimum variance distortionless response criterion. The experimental results demonstrate that the proposed algorithm is superior to some other algorithms in terms of parameter estimation accuracy and source resolution for distributed sources. 

A Low-Complexity DOA Estimation Algorithm for Distributed Source Localization

Non-linear least squares problems arise from data fitting have received recently a lot of attention, particularly for the estimates of the model parameters over networked systems. Although the diffusion Gauss-Newton method offers many advantages for solving the non-linear least squares problem in wireless sensor network to estimate target position parameter, there are some key challenges when applying it to practice, including singularity of Gauss-Newton Hessian, selection to constant step sizes and steady state oscillation. These remaining issues lead to obvious performance degradation such as high computational cost, vulnerability to step size change and resulting instability on estimation.In this paper, to eliminate the singularity, we develop a diffusion Levenberg-Marquardt method such that the problem of constant step size is also addressed together. Then, to reach agreement on estimated vector, a consensus implementation is further proposed, thus eliminating the oscillation during steady state. Consequently, the proposed consensus-based diffusion Levenberg-Marquardt method provides a general solution for the non-linear least squares problems with an objective that takes the form of a sum of squared residual terms. By applying to collaborative localization and distributed optimization arise in large scale machine learning, simulation results confirm the effectiveness and wide applicability of proposed method in terms of convergence rate, accuracy and consistency of estimates.

/pdf/a-consensus-based-diffusion-levenberg-marquardt-method-for-142dxedwt8.pdf

A Consensus-Based Diffusion Levenberg-Marquardt Method for Collaborative Localization With Extension to Distributed Optimization

Pollution source localization is important prerequisites for source control, pollution isolation, purification, and emergency evacuation. Most available studies on pollution source localization have not comprehensively investigated practical problems, such as location accuracy, obstacle avoidance, energy saving, and motion control. Besides, some multi-agent olfaction methods result in wasting resources in small detecting area. Aiming at the issues, this study presents a single agent-based autonomous approach to pollution source localization for the complex environment. In our solution, the NEAT algorithm is used to generate different networks with different weights and structures, the neural network is used to control the agent according to the concentration and distance information, and the fitness function is set to choose the optimal neural network with the best performance. In particular, the fitness function is constructed by combining the pollution concentration and the residual energy of the agent to consider both positioning accuracy and energy saving. Comprehensive simulations show that the proposed method can find the optimal neural network under energy constraints and the agent under the control of the optimal neural network can avoid obstacles and accurately locate the pollution source in different complex environments.

Agent-based autonomous pollution source localization for complex environment

Gas leakage source localization based on sensor networks has an important practical significance in many fields such as environmental monitoring, security protection and pollution control. This paper proposed a gas leakage source localization algorithm using distributed maximum likelihood estimation method for mobile sensor network to improve the lower performance with static sensor network. Firstly, the likelihood function of gas leakage source parameters was deduced based on the gas turbulent diffusion model. Then, the parameters of gas leakage source were estimated based on the likelihood function with the gas concentration measurement in environment. Finally, the gas leakage source location would be achieved through the iterative optimization of the likelihood function. The preliminary experimental results show that the proposed distributed Maximum Likelihood Estimation method could be achieved an acutely gas leakage source location in an indoor environment. And the reasonable path planning and dynamic topology changing could improve the positioning performance.

An indoor gas leakage source localization algorithm using distributed maximum likelihood estimation in sensor networks

Considering the computational complexity and redundancy of traditional array signal arrival angle (DOA) estimation algorithms, the compressed sensing technology was used to improve the real-time and accurate performance of the DOA estimation algorithm, in which, the space sparse signals were reconstructed from the array data by means of array manifold matrix. Compared with the classical MUSIC algorithm, the compressed sensing DOA estimation method could effectively improve the direction finding accuracy and angle resolution with low SNR and snapshot deficiency. Moreover, the proposed algorithm could achieve the coherent signal estimation correctly, and the simulation results show that its performance was superior to that of traditional algorithm.

A new DOA estimation algorithm based on compressed sensing

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An indoor gas leakage source localization algorithm using distributed maximum likelihood estimation in sensor networks

A new DOA estimation algorithm based on compressed sensing