High resolution direction-of-arrival estimation and location determination in wireless networks

Minghui Li, Yilong Lu, Hsiao-Hwa Chen

Research output: Chapter in Book/Report/Conference proceedingChapter


Emitter angle of arrival (AOA) estimation is a significant technology in wireless networks, which can be exploited to locate mobile units, enhance communication efficiency and network capacity, and support location-aided routing, dynamic network planning, and many location-based service and applications. In recent years, smart antenna arrays have been proposed for mobile communication systems to overcome the problem of limited channel bandwidth and growing capacity demand. An adaptive array with AOA capabilities may significantly improve the system performance by increasing channel capacity and spectrum efficiency, extending range coverage, and reducing delay spread, multipath fading, co-channel interference, bit error rate (BER), outage probability, power consumption, and system complexity. To achieve this objective, the array needs to differentiate the desired signal from the co-channel interference, and AOA estimation is one of the most promising techniques. There exists a variety of methods for AOA measurement with conflicting demands of accuracy and computation. This paper discusses in details the many different high-resolution algorithms for AOA estimation in white Gaussian noise, such as Capon algorithm, multiple signal classification (MUSIC), estimation of signal parameters via rotational invariance techniques (ESPRIT), and maximum likelihood (ML) techniques, and the algorithms recently proposed for more practical scenarios involving spatially correlated noise, mutual coupling, and other model errors. Their performance is analyzed and compared, and evaluated against the theoretical lower bounds. Guidelines on appropriate selection of AOA algorithms based on pros and cons of each algorithm are provided.

Original languageEnglish
Title of host publicationNetworks and Quantum Computing
PublisherNova Science Publishers, Inc.
Number of pages30
ISBN (Print)9781611227550
Publication statusPublished - 2011 Dec 1

All Science Journal Classification (ASJC) codes

  • Computer Science(all)


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