High-resolution Direction-of-Arrival estimation

Direction of Arrival (DOA) estimation is considered one of the most crucial problems in array signal processing, with considerable research efforts for developing efficient and effective direction-finding algorithms, especially in the transportation industry, where the demand for an effective, real-time, and accurate DOA algorithm is increasing. However, challenges must be addressed before real-world deployment can be realised. Firstly, there is the requirement for fast computational time for real-time detection. Secondly, there is a demand for high-resolution and accurate DOA estimation. In this thesis, two state-of-the-art DOA estimation algorithms are proposed and evaluated to address the challenges. Firstly, a novel covariance matrix reconstruction approach for single snapshot DOA estimation (CbSS) was proposed. CbSS was developed by exploiting the relationship between the theoretical and sample covariance matrices to reduce estimation error for a single snapshot scenario. CbSS can resolve accurate DOAs without requiring lengthy peak searching computational time by computationally changing the received sample covariance matrix. Simulation results have verified that the CbSS technique yields the highest DOA estimation accuracy by up to 25.5% compared to existing methods such as root-MUSIC and the Partial Relaxation approach. Furthermore, CbSS presents negligible bias when compared to the existing techniques in a wide range of scenarios, such as in multiple uncorrelated and coherent signal source environments. Secondly, an adaptive diagonal-loading technique was proposed to improve DOA estimation accuracy without requiring a high computational load by integrating a modified novel and adaptive diagonal-loading method (DLT-DOA) to further improve estimation accuracy. An in-depth simulation performance analysis was conducted to address the challenges, with a comparison against existing state-of-the-art DOA estimation techniques such as EPUMA and MODEX. Simulation results verify that the DLT-DOA technique performs up to 8.5% higher DOA estimation performance in terms of estimation accuracy compared to existing methods with significantly lower computational time. On this basis, the two novel DOA estimation techniques are recommended for usage in real-world scenarios where fast computational time and high estimation accuracy are expected. Further research is needed to identify other factors that could further optimize the algorithms to meet different demands

Journal of Telecommunications and Information Technology, 2014, nr 1

kwartalni

Efficient method of estimating Direction of Arrival (DOA) in communications systems.

Masters Degree. University of KwaZulu- Natal, Durban.In wireless communications systems, estimation of Direction of Arrival (DOA) has been used both for military and commercial purposes. The signal whose DOA is being estimated, could be a signal that has been reflected from a moving or stationary object, or a signal that has been generated from unwanted or illegal transmitter. When combined with estimating time of arrival, it is also possible to pinpoint the location of a target in space. Localization in space can also be achieved by estimating DOA using two receiving nodes with capability of estimating DOA. The beamforming pattern in smart antenna system is adjusted to emphasize the desired signal and to minimize the interference signal. Therefore, DOA estimation algorithms are critical for estimating the Angle of Arrival (AOA) and beamforming in smart antennas. This dissertation investigates the performance, angular accuracy and resolution of the Minimum Variance Distortionless Response (MVDR), Multiple Signal Classification (MUSIC) and our proposed method Advanced Multiple Signal Classification (A-MUSIC) as DOA algorithms on both Non-Uniform Array (NLA) and Uniform Linear Array (ULA). DOA is critical in antenna design for emphasizing the desired signal and minimizing interference. The scarcity of radio spectrum has fuelled the migration of communication networks to higher frequencies. This has resulted into radio propagation challenges due to the adverse environmental elements otherwise unexperienced at lower frequencies. In rainfall-impacted environments, DOA estimation is greatly affected by signal attenuation and scattering at the higher frequencies. Therefore, new DOA algorithms cognisant of these factors need to be developed and the performance of the existing algorithms quantified. This work investigates the performance of the Conventional Minimum Variance Distortion-less Look (MVDL), Subspace DOA Estimation Methods of Multiple Signal Classification (MUSIC) and the developed hybrid DOA algorithm on a weather impacted wireless channel. The performance of the proposed Advanced-MUSIC (A-MUSIC) algorithm is compared to the conventional DOA estimation algorithms of Minimum Variance Distortionless Response (MVDR) and the Multiple Signal Classification (MUSIC) algorithms for both NLA and ULA antenna arrays. The developed simulation results show that A-MUSIC shows superior performance compared to the two other algorithms in terms of Signal Noise Ratio (SNR) and the number of antenna elements. The results show performance degradation in a rainfall impacted communication network with the developed algorithm showing better performance degradation

Journal of Telecommunications and Information Technology, 2018, nr 2

kwartalni

The Use of X-Ray Pulsars for Aiding GPS Satellite Orbit Determination

This research proposes the use of an existing signal of opportunity - namely x-ray pulsars - to improve the accuracy and robustness of the GPS satellite and clock estimation algorithm. Improvement in satellite and clock accuracy results in a direct benefit to the user. A simulation has been developed to determine the effects of using x-ray pulsar measurements on the GPS Operational Control Segment. The epoch-specific position, velocity, and clock errors of all GPS satellites in the constellation were estimated using both pseudoranges and time-difference-of-arrival (TDOA) measurements from pulsars. The primary measure of accuracy is a constellation Signal-In-Space Range Error (SISRE). Results indicate that marginal SISRE improvements (approximately 1%) can be achieved if the x-ray detector is accurate to an order of approximately 40 m for the strongest pulsar. Increasing the accuracy of the x-ray detector by a factor of 100 can yield accuracy improvements up to 26% over the pseudorange-only based GPS system. Additionally, results show that using only 1 strong pulsar to create TDOA observations, may be comparable to using tens of weakly timed pulsars. Pulsar geometry analysis showed that the geometry does have a significant impact on the overall system performance. Results indicate that using TDOAs in the absence of pseudoranges may aid the OCS in keeping track of the GPS satellites until the ground station links can be reestablished

Direction of Arrival Estimation and Tracking with Sparse Arrays

Direction of Arrival (DOA) estimation and tracking of a plane wave or multiple plane waves impinging on an array of sensors from noisy data are two of the most important tasks in array signal processing, which have attracted tremendous research interest over the past several decades. It is well-known that the estimation accuracy, angular resolution, tracking capacity, computational complexity, and hardware implementation cost of a DOA estimation and/or tracking technique depend largely on the array geometry. Large arrays with many sensors provide accurate DOA estimation and perfect target tracking, but they usually suffer from a high cost for hardware implementation. Sparse arrays can yield similar DOA estimates and tracking performance with fewer elements for the same-size array aperture as compared to the traditional uniform arrays. In addition, the signals of interest may have rich temporal information that can be exploited to effectively eliminate background noise and significantly improve the performance and capacity of DOA estimation and tracking, and/or even dramatically reduce the computational burden of estimation and tracking algorithms. Therefore, this thesis aims to provide some solutions to improving the DOA estimation and tracking performance by designing sparse arrays and exploiting prior knowledge of the incident signals such as AR modeled sources and known waveforms. First, we design two sparse linear arrays to efficiently extend the array aperture and improve the DOA estimation performance. One scheme is called minimum redundancy sparse subarrays (MRSSA), where the subarrays are used to obtain an extended correlation matrix according to the principle of minimum redundancy linear array (MRLA). The other linear array is constructed using two sparse ULAs, where the inter-sensor spacing within the same ULA is much larger than half wavelength. Moreover, we propose a 2-D DOA estimation method based on sparse L-shaped arrays, where the signal subspace is selected from the noise-free correlation matrix without requiring the eigen-decomposition to estimate the elevation angle, while the azimuth angles are estimated based on the modified total least squares (TLS) technique. Second, we develop two DOA estimation and tracking methods for autoregressive (AR) modeled signal source using sparse linear arrays together with Kalman filter and LS-based techniques. The proposed methods consist of two common stages: in the first stage, the sources modeled by AR processes are estimated by the celebrated Kalman filter and in the second stage, the efficient LS or TLS techniques are employed to estimate the DOAs and AR coefficients simultaneously. The AR-modeled sources can provide useful temporal information to handle cases such as the ones, where the number of sources is larger than the number of antennas. In the first method, we exploit the symmetric array to transfer a complex-valued nonlinear problem to a real-valued linear one, which can reduce the computational complexity, while in the second method, we use the ordinary sparse arrays to provide a more accurate DOA estimation. Finally, we study the problem of estimating and tracking the direction of arrivals (DOAs) of multiple moving targets with known signal source waveforms and unknown gains in the presence of Gaussian noise using a sparse sensor array. The core idea is to consider the output of each sensor as a linear regression model, each of whose coefficients contains a pair of DOAs and gain information corresponding to one target. These coefficients are determined by solving a linear least squares problem and then updating recursively, based on a block QR decomposition recursive least squares (QRD-RLS) technique or a block regularized LS technique. It is shown that the coefficients from different sensors have the same amplitude, but variable phase information for the same signal. Then, simple algebraic manipulations and the well-known generalized least squares (GLS) are used to obtain an asymptotically-optimal DOA estimate without requiring a search over a large region of the parameter space

AIS message extraction from overlapped AIS signals for SAT-AIS applications

The AIS (Automatic Identification System) is a communication standard for ships traveling the seas and oceans. It serves as a collision avoidance system by identifying nearby ships, thus assisting in safe navigation. The SAT-AIS (Satellite based Automatic Identification System) is a communication technology for ship traffic surveillance from space and is under active research and development worldwide. The basic principle of the SAT-AIS system is to monitor AIS channels. The motivation for using terrestrial AIS technologies with space applications is of great interest to safety organizations that monitor ship traffic in high seas and oceans. These regions far away from coastal zones are unreachable from the terrestrial antennas, which have a usual range of 40 kilometres. Successful application of the SAT-AIS could provide AIS data to coast guards and other agencies, with an hourly ship location update from every place on the planet. The first trials of SAT-AIS in 2006 suffered from some serious difficulties. As AIS was initially designed to be a terrestrial traffic avoidance application for ships, with the traffic participants communicating among their neighbours and the nearby coast guard, it was developed without resistivity against effects which arise when applied for space applications. Apart from signal strength and Doppler shift effects, which could be constructively handled, the demodulation of overlapped AIS messages proved to be a great challenge. This work analyses the problem of overlapping AIS signals and proposes innovative approaches for reconstructing these based on L^2 norm orthogonalization and projections. Moreover, the work showcases results of demodulation efficiency analysis for simulated real world application of satellite passes over a dedicated shipping region based on AIS channel simulation in noisy environment For more reliable AIS data reception in space, new dedicated frequencies are allocated for channels AIS3 and AIS4, which are being affirmed for all AIS transceiver installations from 2013. These new frequency channels carry dedicated messages with a ship position report, encapsulated into smaller data packets at lower report rates, which promises to partly eliminate the packet overlapping problem. Since the new Space-AIS format does not completely solve the packet collision problem and as the steady growth of interest on terrestrial-AIS message content received from space continues to persist, the topic of solving overlapped AIS signals remains vital for SAT-AIS applications

Compressive Sensing Based Estimation of Direction of Arrival in Antenna Arrays

This thesis is concerned with the development of new compressive sensing (CS) techniques both in element space and beamspace for estimating the direction of arrival of various types of sources, including moving sources as well as fluctuating sources, using one-dimensional antenna arrays. The problem of estimating the angle of arrival of a plane electromagnetic wave is referred to as the direction of arrival (DOA) estimation problem. Such algorithms for estimating DOA in antenna arrays are often used in wireless communication network to increase their capacity and throughput. DOA techniques can be used to design and adapt the directivity of the array antennas. For example, an antenna array can be designed to detect a number of incoming signals and accept signals from certain directions only, while rejecting signals that are declared as interference. This spatio-temporal estimation and filtering capability can be exploited for multiplexing co-channel users and rejecting harmful co-channel interference that may occur because of jamming or multipath effects. In this study, three CS-based DOA estimation methods are proposed, one in the element space (ES), and the other two in the beamspace (BS). The proposed techniques do not require a priori knowledge of the number of sources to be estimated. Further, all these techniques are capable of handling both non-fluctuating and fluctuating source signals as well as moving signals. The virtual array concept is utilized in order to be able to identify more number of sources than the number of the sensors used. In element space, an extended version of the least absolute shrinkage and selection operator (LASSO) algorithm, the adaptable LASSO (A-LASSO), is presented. A-LASSO is utilized to solve the DOA problem in compressive sensing framework. It is shown through extensive simulations that the proposed algorithm outperforms the classical DOA estimation techniques as well as LASSO using a small number of snapshots. Furthermore, it is able to estimate coherent as well as spatially-close sources. This technique is then extended to the case of DOA estimation of the sources in unknown noise fields. In beamspace, two compressive sensing techniques are proposed for DOA estimation, one in full beamspace and the other in multiple beam beamspace. Both these techniques are able to estimate correlated source signals as well as spatially-close sources using a small number of snapshots. Furthermore, it is shown that the computational complexity of the two beamspace-based techniques is much less than that of the element-space based technique. It is shown through simulations that the performance of the DOA estimation techniques in multiple beam beamspace is superior to that of the other two techniques proposed in this thesis, in addition to having the lowest computational complexity. Finally, the feasibility for real-time implementation of the proposed CS-based DOA estimation techniques, both in the element-space and the beamspace, is examined. It is shown that the execution time of the proposed algorithms on Raspberry Pi board are compatible for real-time implementation

Acoustic tomography imaging for atmospheric temperature and wind velocity field reconstruction

Owing to its non-invasive nature, fast imaging speed, low equipment cost, scalability for a variety of measurement ranges, and ability to simultaneously monitor both temperature and wind velocity fields, acoustic tomography has attracted considerable interest in the field of atmospheric imaging. This thesis aims to improve the reconstruction quality of the acoustic tomography system for temperature and wind velocity field imaging. Focusing on this goal, the contribution of the thesis can be summarised from the perspectives of data collection system development, robust and accurate TOF estimation method, and high-quality scalar and vector tomographic image reconstruction methods for temperature and wind velocity fields respectively. Details are given below. Firstly, in order to facilitate the experimental study of acoustic tomography imaging, the design and evaluation of the data collection system and TOF estimation method was presented. The evaluation results indicate that the presented data acquisition system and TOF estimation method has good quantitative accuracy in the lab-scale experiments. The temporal resolution is of great significance for the real-time monitoring of the fast-changing temperature field. To improve the temporal resolution, a novel online time-resolved reconstruction (OTRR) method is presented, which can reconstruct high quality time-resolved images by using fewer TOFs per frame. Compared to state-of-the-art dynamic reconstruction algorithms such as the Kalman filter reconstruction, the proposed algorithm demonstrated superior spatial resolution and preferable quantitative accuracy in the reconstructed images. These features are necessary for the real-time monitoring of the fast-changing temperature field. The forward modelling of most acoustic tomography problems is based on a straight ray model, which may result in large modelling errors due to the refraction effect under a large gradient temperature field. In order to reduce the inaccuracy of using the straight ray model, a bent ray model and nonlinear reconstruction algorithm is applied, which allows the sound propagation ray paths and temperature distribution to be reconstructed iteratively from the TOFs. Using acoustic tomography to reconstruct large-scale temperature and wind velocity fields, a fully parallel TOF measurement scheme is necessary. To achieve this goal, a set of orthogonal acoustic waveforms based on the filtered and modulated Kasami sequence is designed and a cross-correlation based TOF estimation method is used for data collection. Besides, to overcome the invisible field problem and improve the image quality of the wind velocity reconstruction, a divergence-free regularised vector tomographic reconstruction algorithm is studied. The proposed method is able to provide accurate tomographic reconstruction of the 2D horizontal wind velocity field from the TOF measurements. In summary, this thesis focuses on the improvement of acoustic tomography techniques for temperature and wind velocity fields, including the phase corrected Akaike information criterion (AIC) TOF estimation for accurate and robust TOF estimation, the online time-resolved reconstruction method for real-time monitoring of the fast changing temperature field, the nonlinear reconstruction based on the bent ray model to reconstruct the temperature field with a large gradient, and the divergence-free regularised reconstruction method to visualise the 2D horizontal wind velocity field