Chahé Nerguizian

Radio-channel characterization of an underground mine at 2.4 GHz

This paper presents comprehensive experimental results obtained from narrowband and wideband radio-channel measurements in an underground mine with narrow veins at 2.4 GHz. From continuous-wave (CW) measurement data, large-scale distance-power curves and path-loss exponents of the environment are determined. Other relevant parameters, such as the mean excess delay, the maximum excess delay, the root-mean-square (rms) delay spread, and the coherence bandwidth are extracted from the wideband-measurement data. Results show a propagation behavior that is specific for these underground environments with rough surfaces. The rms delay spread does not follow a dual-slope relation with respect to distance, as in environments with smooth surfaces. Moreover, the dependence of the rms delay spread on the bidimensional position of the user is found to be very significant. For the majority of locations, the rms delay-spread values are less than 60 ns.

New-Wave Radio

Radio communications in the past century have relied primarily on nonlinear devices to modulate and demodulate signals for wireless transmissions. This article reviews initial laboratory results obtained with new radios using linear interferometers to modulate and demodulate ultra-wide-band (UWB) signals. Automotive and chip fabrication industries apply such interferometers in new commercial radios for UWB communications.

A Novel Approach for Mobile Robot Navigation with Dynamic Obstacles Avoidance

This paper proposes a new approach for trajectory optimization of a mobile robot in a general dynamic environment. The new method combines the static and dynamic modes of trajectory planning to provide an algorithm that gives fast and optimal solutions for static environments, and generates a new path when an unexpected situation occurs. The particularity of the method is in the representation of the static environment in a judicious way facilitating the path planning and reducing the processing time. Moreover, when an unexpected obstacle blocks the robot trajectory, the method uses the robot sensors to detect the obstacle, finds a best way to circumvent it and then resumes its path toward the desired destination. Experimental results showed the effectiveness of the proposed approach.

Cooperative Localization in Mines Using Fingerprinting and Neural Networks

Localizing people in confined and underground areas is one of the topics under research in mining labs and industries. The position of personnel and equipments in areas such as mines is of high importance because it improves industrial safety and security. Due to the special nature of underground environments, signals transmitted in a mine gallery/tunnel suffer from severe multipath effects caused by reflection, refraction, diffraction and collision with humid rough surfaces. In such cases and in cases where the signals are blocked due to the non-line of sight (NLOS) regions, traditional localization techniques based on the RSS, AOA and TOA/TDOA lead to high position estimation errors. One of the proposed solutions to such challenging situations is based on extracting channel impulse response (CIR) fingerprints with reference to one wireless receiver and using an artificial neural network as a matching algorithm to localize. In this article we study this approach in a multiple access network where multiple access points are present. The diversity of the collected fingerprints will allow us to create artificial neural networks that will work separately or cooperatively using the same localization technique. The results will show that using cooperative artificial intelligence in the presence of multiple signatures from different reference points improves significantly the accuracy, precision, scalability and the overall performance of the localization system.

Accuracy enhancement of an indoor ANN-based fingerprinting location system using Kalman filtering

This paper presents an accuracy enhancement solution to mobilepsilas location and tracking systems in indoor wireless local area network (WLAN) environments. The enhancement method consists of the Kalman filtering application to an artificial neural network (ANN) based fingerprinting location technique. The application of Kalman filtering has the advantage of using information about the mobilepsilas motion to reduce location errors (caused by the WLAN received signal strength- RSS variations) and to avoid trajectory discontinuities (caused by the static estimation of the ANN-based fingerprinting technique). To process the RSS-based fingeprinting location technique, two ANN-based pattern-matching algorithms have been examined: the generalized regression neural network (GRNN) and the multi-layer perceptron (MLP) and they have been compared to the classic K-nearest neighbors (KNN) method. Experimental results, conducted in a specific in-building environment, showed that the GRNN algorithm performs better than the MLP and KNN algorithms. The application of Kalman filtering to the considered GRNN-based fingerprinting location technique improved the location accuracy of about 22.4 % in terms of location mean error.

Accuracy Enhancement of an Indoor ANN-based Fingerprinting Location System Using Particle Filtering and a Low-Cost Sensor

This paper presents an accuracy enhancement solution to mobiles location tracking systems in indoor wireless local area network (WLAN) environments. The enhancement method consists of the particle filter application to an artificial neural network (ANN) based fingerprinting technique combined with a low-cost sensor (compass). The application of the particle filter has the advantage of using information about the mobiles motion to reduce location errors (caused by the WLAN received signal strength-RSS variations) and to avoid mobiles trajectory discontinuities (caused by the static estimation of the fingerprinting technique). A digital compass has been added to the fingerprinting system to observe the mobiles heading and then improve the trajectory orientation. To apply the filtering process, two models have been proposed: non-linear and linearized filtering models. The first model is obtained from the characterization of the pedestrians motion with the heading observation. The second model is obtained after the replacement of the heading variable, in the first model, by the pedestrians velocities along the x and y axes. Experimental results, conducted in a specific in-building environment, showed that the application of the particle filter to the ANN-based fingerprinting system mounted with a compass improves the location accuracy, in terms of mean error, of about 39% and 50% for the cases of non-linear and linearized filtering models, respectively.

Indoor Fingerprinting Geolocation using Wavelet-Based Features Extracted from the Channel Impulse Response in Conjunction with an Artificial Neural Network

This paper proposes a method to localize a mobile station in an indoor environment using wavelet- based features (WBF) extracted from the channel impulse response (CIR) in conjunction with an artificial neural network (ANN). The proposed localization system makes use of the fingerprinting technique and employs CIR information as the signature and an artificial neural network as the pattern matching algorithm. For the considered indoor environment, the obtained CIR information can not be applied directly to the input of the ANN due to the high number of the CIR samples since an ANN with a high number of inputs requires a high number of learning patterns during its training. Consequently, relevant features reflecting the CIR signature have to be extracted and then applied to the ANN. The relevant features may be some physical channel parameters or a compressed version of the CIR signature. In this paper, the extraction of the CIR features is done using a wavelet-based compression. The particularity of the method is in the representation of the CIR signature in a judicious way facilitating the design of the ANN. Moreover, when the extracted features correspond to the CIR signature, the localization system tends to give mobile location with a high precision. Simulation of measured CIR in an indoor environment, showed a precision of 2 meters for 91% and 70% of trained and untrained data, respectively.

Narrowband and wideband radio channel characteristics in underground mining environments at 2.4 GHz

This paper presents comprehensive experimental results obtained from narrowband and wideband radio channel measurements in an underground mine with narrow veins at 2.4 GHz. From CW measurement data, large-scale distance-power curves and path-loss exponents of the environment are determined. Other relevant parameters such as mean excess delay, maximum excess delay and rms delay spread are extracted from wideband measurement data. Results show a propagation behavior that is specific for these underground environments with rough surfaces.

Interference Cancellation Technique for MIMO MB-OFDM UWB Cognitive Radio

This paper presents an interference mitigation algorithm for coexistence of Multiple-Input Multiple-Output (MIMO) Multiband OFDM ultra wideband (MB UWB) Cognitive Radio systems with other wireless communication systems. While other interference cancellation algorithms are applied for SISO system, an improved Active Interference Cancelation algorithm for MIMO MB-UWB systems is proposed with aims to generate sufficiently deep spectral notch and minify excess transmit power of AIC tones. The efficiency of the algorithm in terms of several parameters, such as interference bandwidth, notch depth and optimization parameters is evaluated. The simulation results show that a spectral notch depth of - 70 dB and a power peak of 1 dB are achievable.

Cooperative geo-location in underground mines: A novel fingerprint positioning technique exploiting spatio-temporal diversity

Underground narrow-vein mines result in complex indoor scenarios which require sophisticated localization techniques to maintain basic security measures. While some traditional localization systems use the triangulation techniques for outdoor channels, fingerprint positioning techniques are mostly used in more complex indoor environments like mines. One of the techniques exploited in the quasi-curvilinear topology of underground mines is the Channel Impulse Response (CIR) based fingerprint positioning combined with Artificial Neural Networks (ANNs). This article innovates a CIR-based positioning technique within a cooperative memory-assisted approach that exploits both the temporal (from different time instances) and spatial (from different space positions) diversities of the collected fingerprints. Introducing memory-type signatures in a cooperative localization technique within the spatial confinements of the tunnel-shaped narrow-vein mines significantly increases the accuracy, precision and robustness of the localization system. The cooperative memory-assisted technique is capable of localizing a transmitter with an accuracy of less than 25 cm 90% of the time.