Analysis for Capon and MUSIC DOA estimation algorithms

Weber, Raymond J.; Huang, Yikun

doi:10.1109/aps.2009.5171460

Cited by 39 publications

(17 citation statements)

References 4 publications

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“…Comparison of DoA Estimation Algorithms in SDMA System T. S. Dhope, D. Simunic, R. Zentner minimize power contributed by noise and any signals coming from other direction than desired [9][10][11].…”

Section: Capon's Methods Is Also Called Minimum Variance Distortionlesmentioning

confidence: 99%

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Comparison of DoA Estimation Algorithms in SDMA System

2013

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Original scientific paperWith Space Division Multiple Access (SDMA) schemes employing digital signal processing algorithms in its smart antenna systems, it is possible to improve the system capacity of modern wireless communication systems by enabling user's angular separation. Angular separation ability enhances reception in Signal-of-Interest direction and minimizes interference in Signal-of-Not-Interest direction. Direction of Arrival (DoA) algorithms are used for estimation of a number of incident plane waves on the antenna array and their incidence angles. This paper compares performance of three DoA algorithms: MUSIC, root-MUSIC and Capon applied on the uniform linear array in the presence of uncorrelated white noise. The simulation results show that MUSIC outperformed root-MUSIC and Capon in both required number of snapshots and number of array elements as well as in signal-to-noise ratio requirements.Key words: Capon, DoA estimation, MUSIC, root-MUSIC, MVDR Usporedba algoritama procjene smjera dolaska u sustavima prostorno raspodjeljenog višestrukog pristupa. Korištenjem digitalnih sustava obradbe signala u inteligentnim antenama za sustave prostorno raspodjeljenog višestrukog pristupa, moguće je, ostvarivanjem razlučivanja korisnika po kutu, unaprijediti ukupni kapacitet sustava suvremenih bežičnih komunikacijskih sustava. Mogućnost razdvajanja po kutu pojačava prijam u smjeru korisnog signala i minimizira smetnju iz smjerova smetajućih signala. Algoritmi procjene smjera dolaska se koriste za procjenu broja planarnih valova koji upadaju na antenski niz, te za procjenu njihovih upadnih kutova. Ovaj rad usporeduje kakvoću tri algoritama procjene kuta upada: MUSIC, root-MUSIC i Capon, primijenjenih za uniformni antenski niz u prisustvu nekoreliranog bijelog šuma. Rezultati simulacija pokazuju da je MUSIC algoritam davao najbolje rezultate, u smislu minimalnog potrebnog broja uzoraka, minimalnog broja potrebnih elemenata niza, te u smislu minimalnih potreba odnosa signal-šum.

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Section: Capon's Methods Is Also Called Minimum Variance Distortionlesmentioning

confidence: 99%

“…A number of sources will correspond to a number of peaks. The array response is steered by forming a linear combination of the sensor outputs [5], [9].…”

Section: Beamforming Techniquesmentioning

confidence: 99%

Comparison of DoA Estimation Algorithms in SDMA System

2013

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“…Moreover, subspace algorithms comprise a set wherein the orthogonality between the signal and noise subspaces is exploited [17]. Thus, multiple signal classification (MUSIC), root-MUSIC, capon, and estimation of signal parameters via rotational invariance technique (ESPRIT) [20] are most efficient for estimating the DOA of the signals using array antennas. However, we use the recursive least squares (RLS) algorithm [21] based on minimum mean square error (MMSE) [22] criteria for estimating the DOA of drones.…”

Section: Introductionmentioning

confidence: 99%

Classification, positioning, and tracking of drones by HMM using acoustic circular microphone array beamforming

Guo

Ahmad

Chang

2020

J Wireless Com Network

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This paper addresses issues with monitoring systems that identify and track illegal drones. The development of drone technologies promotes the widespread commercial application of drones. However, the ability of a drone to carry explosives and other destructive materials may pose serious threats to public safety. In order to reduce these threats, we propose an acoustic-based scheme for positioning and tracking of illegal drones. Our proposed scheme has three main focal points. First, we scan the sky with switched beamforming to find sound sources and record the sounds using a microphone array; second, we perform classification with a hidden Markov model (HMM) in order to know whether the sound is a drone or something else. Finally, if the sound source is a drone, we use its recorded sound as a reference signal for tracking based on adaptive beamforming. Simulations are conducted under both ideal conditions (without background noise and interference sounds) and non-ideal conditions (with background noise and interference sounds), and we evaluate the performance when tracking illegal drones.

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“…Here a low-rank matrix approximation based on CS is solved through a quadratic semi define programming (SDP) [4]. Classical method of DOA estimation involves delay and sum method in which DOA estimation is done by locating peaks in the spatial spectrum and the other method is capon's minimum variance method [5]. In TLS ESPRIT the sensor array is decomposed to two identical sub arrays which exhibit translational invariance [6].…”

Section: Introductionmentioning

confidence: 99%

Multilayer Perceptron Scheme for Beamforming and Channel Estimation of Massive MIMO

2019

IJRTE

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 Abstract: Massive MIMO being one of the core enabler of 5G network, estimation of channel characteristics in such network plays a vital role. Also the channel is most affected by the phase information rather than the amplitude information. Thus phase estimation plays a very important role in order to ensure error free transmission especially in the case of rapidly changing channels like in massive MIMO. When an accurate transmit beamforming technique is employed to this scheme, it results in maximum user data separation thereby improving the directivity of the signal. The proposed scheme works on two multilayer perceptron (MLP) mechanism model, one for beamforming and other for channel estimation. First MLP model takes the transmitted signal that is passing through the channel to generate the beamforming vectors and these beamforming vectors after prediction are then multiplied with the channel coefficients to enable beamforming. The transmit beamformed signal after passing through the channel and applying appropriate noise, will be received in different directions and these training samples are then given to the second MLP model to predict DOA of the received signal which in turn estimate the channel of massive MIMO. In our proposed scheme, as both beamforming and channel estimation are handled by deep neural network, along with achieving very much better accuracy, mean square error (MSE) and bit error rate (BER) performance, it reduces the number of epochs required for training which results in an efficient learning scheme that is capable of predicting real time rapidly varying channel of massive MIMO.

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Analysis for Capon and MUSIC DOA estimation algorithms

Cited by 39 publications

References 4 publications

Comparison of DoA Estimation Algorithms in SDMA System

Comparison of DoA Estimation Algorithms in SDMA System

Classification, positioning, and tracking of drones by HMM using acoustic circular microphone array beamforming

Multilayer Perceptron Scheme for Beamforming and Channel Estimation of Massive MIMO

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