Planar array design and analysis on direction of arrival estimation for mobile communication systems

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

Numerical and Experimental Study of Target Detection and Localization in Ultra Wideband Multiple-Input Multiple-Output Radars

東京電機大学201

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