An ultra-wideband (UWB) spatial filter is proposed to mitigate multipath effects in a one-way, one-dimensional time-of-arrival (TOA) localization system for use inside a tunnel. The spatial filter is a weighted array of judiciously placed antennas and it exploits the fact that electromagnetic waves propagate as modes in a tunnel by selectively extracting these mode(s). The design of several spatial filters is presented alongside vigorous analyses to characterize the localization performance afforded by them in a noisy environment. The filters are evaluated using data from an analytical equation waveguide model, a ray tracer model and measurements. These spatial filters deliver accurate localization estimates across distance and well-designed filters can operate at higher SNRs and further distances than single sensors. Insights into successful spatial filter design are provided and this spatial filtering technique has created a new branch of multipath-aware localization systems.

The purpose of this thesis is to emphasize the lacking areas in the field of direction of arrival estimation and to propose building blocks for continued solution development in the area. A review of current methods are discussed and their pitfalls are emphasized. DOA estimators are compared to each other for usage on a conformal microphone array which receives impulsive, wideband signals. Further, many DOA estimators rely on the number of source signals prior to DOA estimation. Though techniques exist to achieve this, they lack robustness to estimate for certain signal types, particularly in the case where multiple radar targets exist in the same range bin. A deep neural network approach is proposed and evaluated for this particular case. The studies detailed in this thesis are specific to acoustic and radar applications for DOA estimation.

Sound source localization is an important task for several applications and the use of deep learning for this task has recently become a popular research topic. While the majority of the previous work has focused on static sound sources, in this work we evaluate the performance of a deep learning classification system for localization of high-speed moving sound sources. In particular, we systematically evaluate the effect of a wide range of parameters at three levels including: data generation (e.g., acoustic conditions), feature extraction (e.g., STFT parameters), and model training (e.g., neural network architectures). We evaluate the performance of multiple metrics in terms of precision, recall, F-score and confusion matrix in a multi-class multi-label classification framework. We used four different deep learning models: feedforward neural networks, recurrent neural network, gated recurrent networks and temporal Convolutional neural network. We showed that (1) the presence of some reverberation in the training dataset can help in achieving better detection for the direction of arrival of acoustic sources, (2) window size does not affect the performance of static sources but highly affects the performance of moving sources, (3) sequence length has a significant effect on the performance of recurrent neural network architectures, (4) temporal convolutional neural networks can outperform both recurrent and feedforward networks for moving sound sources, (5) training and testing on white noise is easier for the network than training on speech data, and (6) increasing the number of elements in the microphone array improves the performance of the direction of arrival estimation.

No / In this work we present the angle of arrival estimation techniques and their comparison at different values of SNR using a 5 element UCA. The techniques that have been considered include phase interferometry, Multiple Signal Classification and covariance. The results show that for very low values of SNR the performance of the covariance matrix based algorithm is the best but for slightly higher values of SNR, MUSIC algorithm outperforms covariance.

Advancements in Global Positioning System (GPS) technology now make GPS data collection for transportation studies and other transportation applications a reality. Base map for the application can be obtained by importing the road centerline map into GIS software like AutoCAD Map, or Arc/Info or MapixTM. However, such kinds of Road Centerline maps are not available for all places. Therefore, it may be necessary to collect the data using GPS units. This thesis details the use of GPS technology to produce route maps that can be used to predict arrival time of a bus. This application is particularly useful in rural areas, since the bus headway in a rural area is generally larger than that in an urban area. The information is normally communicated through various interfaces such as internet, cable TV, etc., based on the GPS bus location data. The objective of this thesis is to develop an operational procedure to obtain the digitized route map of any desired interval or link length and to examine the accuracy of the digitized map. The operational procedure involved data collection, data processing, algorithm development and coding to produce the digitized route maps. An algorithm was developed produce the digitized route map from the base map of the route, coded in MATLAB, and can be used to digitize the base map into any desired interval of distance. The accuracy comparison is made to determine the consistency between the digitized route map and the base map. / Master of Science

Yes / This paper presents the advantages of using a wideband spectrum adopting multi-carrier to improve targets localization within a simulated indoor environment using the Time of Arrival (TOA) technique. The study investigates the effect of using various spectrum bandwidths and a different number of carriers on localization accuracy. Also, the paper considers the influence of the transmitters’ positions in line-of-sight (LOS) and non-LOS propagation scenarios. It was found that the accuracy of the proposed method depends on the number of sub-carriers, the allocated bandwidth (BW), and the number of access points (AP). In the case of using large BW with a large number of subcarriers, the algorithm was effective to reduce localization errors compared to the conventional TOA technique. The performance degrades and becomes similar to the conventional TOA technique while using a small BW and a low number of subcarriers.

Direction of Arrival (DOA) estimation with digital arrays under unknown Gaussian distributed element location perturbation has detrimental effects to the performance of traditional DOA estimation techniques. This work proposes an artificial intelligence (AI) approach as a solution to this problem. A Deep Convolutional Neural Network (DCNN) is proposed and experimentation into network parameters, classification networks, and how the DCNN is applied to the DOA problem are studied. It is shown that this AI based approach is successful in estimating the DOA with perturbed arrays where traditional approaches fail.

This thesis addresses the possibility of using a delay-on-ground concept in which flights with less than 1 hour flying time (often referred to as pop-up flights) absorb their arrival sequencing delay at the departure gate by being issued their Arrival Manager (AMAN)-scheduled time as a Required Time of Arrival (RTA) that is inserted into the Flight Management System (FMS). Due to their short duration these flights are currently often inserted into the AMAN sequence shortly before Terminal Manoeuvring Area (TMA) entry and thereby often need to absorb their arrival sequencing delay in the inefficient manner of airborne holding or vectoring close to the arrival airport. The literature review examines current operational procedures of AMANs and Departure Managers (DMANs), the current FMS RTA function and live trials in which the delay-on-ground concept was tested in real operations. A case study airport in Europe that has potential to benefit from the concept is identified. The performance of the delay-on-ground concept for the case study airport is then assessed by performing 180 fast-time Monte Carlo simulation runs. For each run the arrival flow to the case study airport and the departure flows from two medium-sized airports from which the pop-up flights originate are simulated. Each run represents an operational day and variations in departure/arrivals time is put into the timetables to simulate the variation in actual departure/arrival times resulting from operational factors normally encountered in day-to-day operations. An algorithm is written in Matlab to simulate an AMAN-DMAN cooperation in which pop-up flights are locked to the required departure times to meet their RTAs. It is shown that a significant reduction in airborne delay time and fuel consumption can be achieved at the case study airport by using the concept. It is also shown that it is possible to ensure that the pop-up flights depart at the required times to meet their RTAs without negatively affecting the departure sequences.

ITC/USA 2010 Conference Proceedings / The Forty-Sixth Annual International Telemetering Conference and Technical Exhibition / October 25-28, 2010 / Town and Country Resort & Convention Center, San Diego, California / Traditional angle of arrival algorithms operate with uniform receiver arrays. Non-uniform arrays typically introduce significant elevation of computation complexity. This paper utilizes the double-integration method for the accurate estimation of the angle of arrival with non-uniform receiver arrays, while maintaining high computation efficiency. Because of the simplicity, the double-integration method is not significantly affected by the increase of the number of receivers or the non-uniform configuration. This approach allows us to perform high-speed high-accuracy estimation of the two-dimensional bearing angle without the constraints of structured receiver arrays, which is important to the realization of real-time tracking of mobile acoustic sources.

The demand of wireless communication has increased significantly in the past few decades due to huge demand to deliver multimedia content instantly. The expansion of mobile content paired with affordable mobile devices has opened a new trend for having access to the latest information on mobile devices. This trend is made possible by the technology of smart antenna systems as well as array signal processing algorithms. Array signal processing is not limited to wireless communication, but also found in other applications such as radar, sonar and automotive. One of the important components in array signal processing is its ability to estimate the direction of incoming signals known as directional-of-arrival (DOA). The performance of DOA algorithms depends on the steering vector since it contains information about the direction of incoming signals. One of the main factors to affect the DOA estimation is the array geometries since the array factor of the array geometries determines the definition of the steering vector. Another issue in DOA estimation is that the DOA algorithms are designed based on the ideal assumption that the antenna arrays are free from imperfection conditions. In practice, ideal conditions are extremely difficult to obtain and thus the imperfect conditions will severely degraded the performance of DOA estimation. The imperfect conditions include the presence of mutual coupling between elements and are also characteristic of directional antenna. There are three topics being discussed in this thesis. The first topic being investigated is new geometry of antenna array to improve the performance of DOA estimation. Two variants of the circular-based array are proposed in this thesis: semi-circular array and oval array. Another proposed array is Y-bend array, which is a variant of V-shape array. The proposed arrays are being put forward to offer a better performance of DOA estimation and have less acquired area compared with the circular array. It is found out that the semi-circular array has 5.7% better estimation resolution, 76% lower estimation error, and 20% higher estimation consistency than the circular array. The oval array improves the estimation resolution by 33%, estimation error by 60%, and estimation consistency by 20% compared with the circular array. In addition, for the same number of elements, the oval array requires 12.5% to 15% less area than the circular array. The third proposed array, Y-bend array, has 23% smaller estimation resolution, 88% lower estimation error, and 7% higher estimation consistency than the V-shape array. Among the proposed arrays, the semi-circular possessed the best performance with 25% smaller estimation resolution, ten times smaller estimation error, and 5% higher estimation consistency over the other proposed arrays. Secondly, this thesis investigates the DOA estimation algorithm when using the directional antenna array. In this case, a new algorithm is proposed in order to suit the characteristics of the directional antenna array. The proposed algorithm is a modified version of the Capon algorithm, one of the algorithms in beamforming category. In elevation angle estimation, the proposed algorithm achieves estimation resolution up to 1°. The proposed algorithm also manages to improve the estimation error by 80% and estimation consistency by 10% compared with the Capon algorithm. In azimuth angle estimation, the proposed algorithm achieves 20 times lower estimation error and 20% higher estimation consistency than the Capon algorithm. These simulation results show that the proposed algorithm works effectively with the directional antenna array. Finally, the thesis proposes a new method in DOA estimation process for directional antenna array. The proposed method is achieved by means of modifying covariance matrix calculation. Simulation results suggest that the proposed method improves the estimation resolution by 5° and the estimation error by 10% compared with the conventional method. In summary, this thesis has contributed in three main topics related to DOA estimation; array geometry design, algorithm for the directional antenna array, and method in DOA estimation process for the directional antenna array.