Analysis of performance for multiple signal classification (MUSIC) in estimating direction of arrival

Khan, Zuhani Ismail; Kamal, Mustaffa; Hamzah, N.; Othman, Kama Azura; Khan, Naveed Iqbal

doi:10.1109/rfm.2008.4897465

Cited by 31 publications

(8 citation statements)

References 3 publications

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“…The direction finding technology of spatial spectrum estimation can achieve the simultaneous direction finding of multiple targets (including coherent signal and incoherent signal), and under the condition of low signal-to-noise ratio, the direction finding accuracy is very high, which can be fully used in the direction finding of radiation sources in complex electromagnetic environment. There are two basic algorithms, one is MUSIC (multiple signal classification) [30] algorithm. The principle of the algorithm is to decompose the autocorrelation matrix into subspaces, obtain the noise subspace and signal subspace of the matrix, and then use the orthogonal relationship between the noise subspace and the direction vector to estimate the incident direction of the signal.…”

Section: Space Spectrum Estimationmentioning

confidence: 99%

Movable Platform-Based Topology Detection for a Geographic Routing Wireless Sensor Network

Wang

Chen

2020

Sensors

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With the increasing adoption of the Internet-of-Things (IoT), the wireless sensors network (WSN), as an underlying application of IoT, has attracted increasing attention. Topology, the working structure used to observe WSN, is the most instinctive form in troubleshooting and has great significance to WSN management and safety. To this end, it is imperative to recover WSN topology for the purpose of network management and non-cooperative network detection. Traditional network topology recovery mainly relies on the monitoring modules installed in nodes, or an extra network attached. However, these two approaches have several limitations, such as high energy consumption for monitoring nodes, time synchronization problems, reuse failure, limitation to specific targeted networks and high cost. In this paper, we present a new approach to recover the topology of WSN that adopts location-based routing protocols, based on movable platforms. Our observation is that the network topology is consistent with the node routing, as the nodes choose the next hop according to the geological position of neighbor nodes. Hence, we calculate the cost parameters of choosing routing nodes for the targeted network according to the partial connection of the nodes. Based on those cost parameters, we can determine the topology of the whole network. More specifically, by collecting the geological position and data packets of the nodes from movable platforms, we are able to infer the topology of the WSN according to the recovered partial connection of nodes. Our approach can be easily adopted to many scenarios, especially for non-cooperative large-scale networks. The evaluation of 30 simulations shows that the accuracy of recovery is above 90%.

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Section: Space Spectrum Estimationmentioning

confidence: 99%

Movable Platform-Based Topology Detection for a Geographic Routing Wireless Sensor Network

Wang

Chen

2020

Sensors

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“…It uses the eigenvector decomposition and eigenvalues of the covariance matrix of the antenna array for estimating DOA of sources based on the properties of the signal and noise subspaces [24]. This assumption physically means that sources are totally uncorrelated between them [25]. This assumption physically means that sources are totally uncorrelated between them [25].…”

Section: B) Music Algorithmmentioning

confidence: 99%

The application of high-resolution methods for DOA estimation using a linear antenna array

Osman

Sfar

Gharsallah

2014

Int. j. microw. wirel. technol.

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lotfi osman, imen sfar and ali gharsallah This paper presents results of direction of arrival (DOA) estimation using multiple signal classification (MUSIC), Root-MUSIC, and estimation of signal parameters via rotational invariance technique algorithms. As is well known, these algorithms are mainly based on the specific properties of the signal covariance matrix as well as the decomposition of the observation space into two subspaces, one for the signal and the other for the noise. Here, we are particularly interested in the quality of sources localization considering only the case of uncorrelated radio frequency signals impinging on an antenna array. A measurement system consisting of a linear array antenna and a five-port network applicable to a demodulator such as a receiver is used for the DOA estimation process. Co-simulations performed with the Advanced Device System and Matlab yielded interesting results not only on their performance but also on their limitation.

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“…Multiple Signal Classification (MUSIC) is a relatively simple and efficient eigen structure method of DOA estimation. It has many variations and is perhaps the most studied method in its class (Khan et al, 2008).…”

Section: Music Algorithmmentioning

confidence: 99%

An Overview of Direction-of-Arrival Estimation Using an Antenna Array With Four Elements

Mohamed¹

2012

American Journal of Applied Sciences

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The Direction-of-Arrival (DOA) estimation techniques using antenna arrays are applied in large areas of research fields and have received great attention in literature. Mobile communication, Sonar and Radar are just some examples among a large number of possible applications. For example, in military application, it is very important to recognize the direction of any threat. Another example of commercial application is to identify the direction of an emergency cell phone call so that the rescue unit can act in time to the right location. Thereby, DOA estimation using a traditional fixed antenna has many limitations and becomes inappropriate in such applications. In this letter, we describe a DOA estimation system based on an antenna array and five port circuits for detecting arrival angles in azimuth plane of signals pitched by the antenna array. For this, we have based on Multiple Signal Classification (MUSIC) algorithm, which showed a certain limit of its performance in spite of its big reputation by the researchers. In our case, we only consider uncorrelated signals and assume the channel parameters corresponding to a real context. The purpose is to compare the quality of the detected angle in term of precision depending on the number of the samples depicted. ADS software and MATLAB are used to achieve this study. The simulation results on the proposed system designed to operate at 2.4 GHz frequency indicate that MUSIC algorithm is unable to automatically detect and localize the DOA of signals with high accuracy. Other algorithms exist and may provide more precision but require more computation time

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Analysis of performance for multiple signal classification (MUSIC) in estimating direction of arrival

Cited by 31 publications

References 3 publications

Movable Platform-Based Topology Detection for a Geographic Routing Wireless Sensor Network

Movable Platform-Based Topology Detection for a Geographic Routing Wireless Sensor Network

The application of high-resolution methods for DOA estimation using a linear antenna array

An Overview of Direction-of-Arrival Estimation Using an Antenna Array With Four Elements

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