Modified Multiple Signal Classification Algorithm for WSNs Localization

Lafta, Nabeel A.; Hreshee, Saad S.

doi:10.1109/iiceta50496.2020.9318808

Cited by 2 publications

(2 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The high-resolution spectrum estimation method calculates the correlation matrix of each element to obtain subspaces under different parameters, thereby obtaining the orientation information of the sound source. Veerendra Dakulagi [17] modified the classical MUSIC algorithm by absorbing the Jordon normalization matrix in the covariance matrix to reconstruct the data. With this modification, even coherent sources can be accurately estimated in low-SNR environments.…”

Section: Introductionmentioning

confidence: 99%

Minimum Variance Distortionless Response—Hanbury Brown and Twiss Sound Source Localization

Liu

Zhao

2023

Applied Sciences

View full text Add to dashboard Cite

Sound source target localization is an extremely useful technique that is currently utilized in many fields. The Hanbury Brown and Twiss (HBT) interference target localization method based on sound fields is not accurate enough for localization at low signal-to-noise ratios (below 0 dB). To address this problem, this paper introduces Minimum Variance Distortionless Response (MVDR) beamforming and proposes a new MVDR-HBT algorithm. Specifically, for narrowband signals, the inverse of the correlation matrix of the sound signal is calculated, and a guiding vector is constructed to compute the MVDR direction weights. These direction weights are then used to weight the correlation function of the HBT algorithm. Subsequently, the MVDR-HBT algorithm is extended from narrowband signals to broadband signals. As a result, the directivity of the HBT algorithm is optimized for wide- and narrowband signals, resulting in improved localization accuracy. Finally, the target localization accuracy of the MVDR-HBT algorithm is analyzed through simulation and localization experiments. The results show that the MVDR-HBT algorithm can accurately determine the direction of a sound source, with localization errors at different positions that are smaller than those produced by HBT. The localization performance of MVDR-HBT is considerably better than that of HBT, further verifying the simulation results. This study provides a new idea for target localization within an acoustic propagation medium (air).

show abstract

Section: Introductionmentioning

confidence: 99%

Minimum Variance Distortionless Response—Hanbury Brown and Twiss Sound Source Localization

Liu

Zhao

2023

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Sound source localization techniques based on microphone arrays and their applications are currently receiving increasing attention in academia [3]. Currently, there are many theories and methods for microphone-array-based sound source localization, which can be broadly classified into the Received Signal Strength (RSS) localization method [4,5], Angle of Arrival (AOA) localization method [6,7], Time of Arrival (TOA) localization method [8,9], Time Difference of Arrival (TDOA) localization methods [10][11][12], Frequency Difference of Arrival (FDOA) [13,14] localization method, Multiple Signal Classification (MUSIC) positioning method [15,16], and many other methods based on beamforming or other advanced methods [2,17,18], etc.…”

Section: Introductionmentioning

confidence: 99%

A Sound Source Localization Method Based on Frequency Divider and Time Difference of Arrival

Zhao

Zhang

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

In recent years, sound source localization, as a passive localization technique with higher safety and convenience compared with other localization techniques such as active emission of electromagnetic waves, has received more and more attention in academia. This paper researches and improves the far-field sound source localization algorithm based on the generalized cross-correlation method (GCC) Time Difference of Arrival (TDOA) estimation algorithm and completes the design and implementation of the microphone array sound source localization system. This paper adds a frequency divider to the traditional generalized correlation time delay estimation algorithm for pre-processing, sampling, and localization of sound source acoustic waves and adopts a low-cost microphone array deployment scheme as far as possible to improve the flexibility and practicality of the localization system; at the same time, the “Minimum Sphere Method” is used at the back end of the algorithm to classify the localization coordinates at different frequencies and, finally, output reasonable sound source coordinates. In the back-end of the algorithm, the “Minimum Sphere Method” is used to classify the localization coordinates at different frequencies and, finally, output the reasonable sound source coordinates. The experimental results show that the sound source localization system designed in this paper has good performance in terms of localization accuracy and cost-effectiveness and overcomes the failure of the generalized mutual correlation algorithm in the original application of high noise environment and multi-source environment localization.

show abstract

Modified Multiple Signal Classification Algorithm for WSNs Localization

Cited by 2 publications

References 35 publications

Minimum Variance Distortionless Response—Hanbury Brown and Twiss Sound Source Localization

Minimum Variance Distortionless Response—Hanbury Brown and Twiss Sound Source Localization

A Sound Source Localization Method Based on Frequency Divider and Time Difference of Arrival

Contact Info

Product

Resources

About