WIMAX TESTBED

WiMAX, the Worldwide Interoperability for Microwave Access, is a telecommunications technology aimed at providing wireless data over long distances in a variety of ways, from point-to-point links to full mobile cellular type access. It is based on the IEEE 802.16 standard, which is also called Wire IessMAN. The name WiMAX was created by the WiMAX Forum, which was formed in June 2001 to promote conformance and interoperability of the standard. The forum describes WiMAX as a standards-based technology enabling the delivery of last mile wireless broadband access as an alternative to cable and DSL. This Final Year Project attempts to simulate via Simulink, the working mechanism of a WiMAX testbed that includes a transmitter, channel and receiver. This undertaking will involve the baseband physical radio link. Rayleigh channel model together with frequency and timing offsets are introduced to the system and a blind receiver will attempt to correct these offsets and provide channel equalization. The testbed will use the Double Sliding Window for timing offset synchronization and the Schmid! & Cox algorithm for Fractional Frequency Offset estimation. The Integer Frequency Offset synchronization is achieved via correlation of the incoming preamble with its local copy whereas Residual Carrier Fr~quency Offset is estimated using the L th extension method. A linear Channel Estimator is added and combined with all the other blocks to form the testbed. From the results, this testbed matches the standard requirements for the BER when SNR is 18dB or higher. At these SNRs, the receiver side of the testbed is successful in performing the required synchronization and obtaining the same data sent. Sending data with SNR lower than 18dB compromises its performance as the channel equalizer is non-linear. This project also takes the first few steps of hardware implementation by using Real Time Workshop to convert the Simulink model into C codes which run outside MATLAB. In addition, the Double Sliding Window and Schmid! & Cox blocks are converted to Xilinx blocks and proven to be working like their Simulink counterparts

Pedestrian Navigation using Artificial Neural Networks and Classical Filtering Techniques

The objective of this thesis is to explore the improvements achieved through using classical filtering methods with Artificial Neural Network (ANN) for pedestrian navigation techniques. ANN have been improving dramatically in their ability to approximate various functions. These neural network solutions have been able to surpass many classical navigation techniques. However, research using ANN to solve problems appears to be solely focused on the ability of neural networks alone. The combination of ANN with classical filtering methods has the potential to bring beneficial aspects of both techniques to increase accuracy in many different applications. Pedestrian navigation is used as a medium to explore this process using a localization and a Pedestrian Dead Reckoning (PDR) approach. Pedestrian navigation is primarily dominated by Global Positioning System (GPS) based navigation methods, but urban and indoor environments pose difficulties for using GPS for navigation. A novel urban data set is created for testing various localization and PDR based pedestrian navigation solutions. Cell phone data is collected including images, accelerometer, gyroscope, and magnetometer data to train the ANN. The ANN methods are explored first trying to achieve a low root mean square error (RMSE) of the predicted and original trajectory. After analyzing the localization and PDR solutions they are combined into an extended Kalman Filter (EKF) to achieve a 20% reduction in the RMSE. This takes the best localization results of 35m combined with underperforming PDR solution with a 171m RMSE to create an EKF solution of 28m of a one hour test collect

Target Tracking in UWB Multistatic Radars

Detection, localization and tracking of non-collaborative objects moving inside an area is of great interest to many surveillance applications. An ultra- wideband (UWB) multistatic radar is considered as a good infrastructure for such anti-intruder systems, due to the high range resolution provided by the UWB impulse-radio and the spatial diversity achieved with a multistatic configuration. Detection of targets, which are typically human beings, is a challenging task due to reflections from unwanted objects in the area, shadowing, antenna cross-talks, low transmit power, and the blind zones arised from intrinsic peculiarities of UWB multistatic radars. Hence, we propose more effective detection, localization, as well as clutter removal techniques for these systems. However, the majority of the thesis effort is devoted to the tracking phase, which is an essential part for improving the localization accuracy, predicting the target position and filling out the missed detections. Since UWB radars are not linear Gaussian systems, the widely used tracking filters, such as the Kalman filter, are not expected to provide a satisfactory performance. Thus, we propose the Bayesian filter as an appropriate candidate for UWB radars. In particular, we develop tracking algorithms based on particle filtering, which is the most common approximation of Bayesian filtering, for both single and multiple target scenarios. Also, we propose some effective detection and tracking algorithms based on image processing tools. We evaluate the performance of our proposed approaches by numerical simulations. Moreover, we provide experimental results by channel measurements for tracking a person walking in an indoor area, with the presence of a significant clutter. We discuss the existing practical issues and address them by proposing more robust algorithms

Histogram equalization for robust text-independent speaker verification in telephone environments

Word processed copy. Includes bibliographical references

Tracking with Sparse and Correlated Measurements via a Shrinkage-based Particle Filter

This paper presents a shrinkage-based particle filter method for tracking a mobile user in wireless networks. The proposed method estimates the shadowing noise covariance matrix using the shrinkage technique. The particle filter is designed with the estimated covariance matrix to improve the tracking performance. The shrinkage-based particle filter can be applied in a number of applications for navigation, tracking and localization when the available sensor measurements are correlated and sparse. The performance of the shrinkage-based particle filter is compared with the posterior Cramer-Rao lower bound, which is also derived in the paper. The advantages of the proposed shrinkage-based particle filter approach are demonstrated via simulation and experimental results

Vibration Monitoring: Gearbox identification and faults detection

L'abstract è presente nell'allegato / the abstract is in the attachmen