DOA estimation and tracking of ULAs with mutual coupling

A class of subspace-based methods for direction-of-arrival (DOA) estimation and tracking in the case of uniform linear arrays (ULAs) with mutual coupling is proposed. By treating the angularly-independent mutual coupling as angularly-dependent complex array gains, the middle subarray is found to have the same complex array gains. Using this property, a new way for parameterizing the steering vector is proposed and the corresponding method for joint estimation of DOAs and mutual coupling matrix (MCM) using the whole array data is derived based on subspace principle. Simulation results show that the proposed algorithm has a better performance than the conventional subarray-based method especially for weak signals. Furthermore, to achieve low computational complexity for online and time-varying DOA estimation, three subspace tracking algorithms with different arithmetic complexities and tracking abilities are developed. More precisely, by introducing a better estimate of the subspace to the conventional tracking algorithms, two modified methods, namely modified projection approximate subspace tracking (PAST) (MPAST) and modified orthonormal PAST (MOPAST), are developed for slowly changing subspace, whereas a Kalman filter with a variable number of measurements (KFVM) method for rapidly changing subspace is introduced. Simulation results demonstrate that these algorithms offer high flexibility and effectiveness for tracking DOAs in the presence of mutual coupling. © 2006 IEEE.published_or_final_versio

A Relative Positioning Technique with Spatial Constraints for Multiple Targets Based on Sparse Wireless Sensor Network

Many applications of wireless sensor network require precise knowledge of the locations of nodes. Conventional sparse wireless sensor network, which is formed by a restricted number of nodes, has two drawbacks, i.e., low connective ratio and hop count limited, which probably cause the network link failure and/or low locating performance. To improve the relative position precision and reliability of multiple targets based on the sparse wireless sensor network, a relative locating method with spatial constraints is proposed according to the different network configuration and inter-range between target nodes for the sparse wireless sensor network, of which the spatial constraint benchmarks include two categories of datum, namely, the spatial absolute displacement datum and direction rotation datum. In particular, it is proven on the basis of the principle of survey adjustment that the nodes' position ambiguity, which is caused by the rank deficiency, could be solved while the estimating precision of the target nodes position is unchanged. The simulation results show that compared to the conventional time-varying filtering, e.g. Kalman Filtering, the proposed constraint position method may rapidly respond to network-link communication failure, and increase relative positioning precision about 32.9 % via introducing the spatial constraint benchmarks

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

Journal of Telecommunications and Information Technology, 2018, nr 2

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