Improved Direction of Arrival Estimation using Multiple Signal Classification (MUSIC) Algorithm with Decomposition and Normalization
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Abstract
It is important to determine the direction of arrival (DoA) of targets in various applications such as radar and sonar. Multiple Signal Classification (MUSIC), Estimation of Signal Parameters with Rotational In variance Technique (ESPRIT), and Weighted Subspace Fitting (WSF) are subspace-based methods that can be used to improve DoA estimation. MUSIC is effective for high-resolution, uncorrelated signals, but may struggle in cases where there are two nearby targets with a low signal-to-noise ratio (SNR). The goal of this research is to improve the performance of the MUSIC algorithm for DoA estimation with low SNR signals. The proposed solution involves decomposing and normalizing the signal during transmission. Simulations were conducted to test the modified procedure with MUSIC algorithm for DoA estimation, and it was found that received signal power improved though there is noisy environment as well as system can detect more number of targets. The proposed technique of decomposition and normalization could also be applied in other areas such as WiFi communication, autonomous vehicles and biomedical signal and image processing etc.
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Bilik, I., Longman, O., Villeval, S., & Tabrikian, J. (2019), “The rise of radar for autonomous vehicles: Signal processing solutions and future research directions”, IEEE signal processing Magazine, 36(5), 20-31.
Lin, Y. C., Lee, T. S., Pan, Y. H., & Lin, K. H. (2019), “Low-complexity high-resolution parameter estimation for automotive MIMO radars”, IEEE Access, 8, 16127-16138.
Chen, Feng, De-Sen Yang, Di Wu, Chenyang Gui, and Shi-Qi Mo. "A novel direction-of-arrival estimation algorithm without knowing the source number." IEEE Communications Letters 25, no. 3 (2020): 985-989.
Mankal, Pavan, Sanjaykumar C. Gowre, and Veerendra Dakulagi. "A New DOA algorithm for spectral estimation." Wireless Personal Communications 119 (2021): 1729-1741.
Mr. Kankan Sarkar. (2016). Design and analysis of Low Power High Speed Pulse Triggered Flip Flop. International Journal of New Practices in Management and Engineering, 5(03), 01 - 06. Retrieved from http://ijnpme.org/index.php/IJNPME/article/view/45
Schmidt, Ralph. "Multiple emitter location and signal parameter estimation." IEEE transactions on antennas and propagation 34, no. 3 (1986): 276-280.
Mahata, Kaushik. "Subspace fitting approaches for frequency estimation using real-valued data." IEEE transactions on signal processing 53, no. 8 (2005): 3099-3110.
Roy, Richard, and Thomas Kailath. "ESPRIT-estimation of signal parameters via rotational invariance techniques." IEEE Transactions on acoustics, speech, and signal processing 37, no. 7 (1989): 984-995.
Samad, A. . (2022). Internet of Things Integrated with Blockchain and Artificial Intelligence in Healthcare System. Research Journal of Computer Systems and Engineering, 3(1), 01–06. Retrieved from https://technicaljournals.org/RJCSE/index.php/journal/article/view/34
Zahernia, Azardokht, Mohammad Javad Dehghani, and Reza Javidan. "MUSIC algorithm for DOA estimation using MIMO arrays." In 2011 6th International Conference on Telecommunication Systems, Services, and Applications (TSSA), pp. 149-153. IEEE, 2011.
Wen, Fei, Qun Wan, Rong Fan, and Hewen Wei. "Improved MUSIC algorithm for multiple noncoherent subarrays." IEEE Signal Processing Letters 21, no. 5 (2014): 527-530.
Liu, Guohong, Hong Chen, Xiaoying Sun, and Robert C. Qiu. "Modified MUSIC algorithm for DOA estimation with Nyström approximation." IEEE Sensors Journal 16, no. 12 (2016): 4673-4674.
Du, Weixiu, and Rodney Lynn Kirlin. "Improved spatial smoothing techniques for DOA estimation of coherent signals." IEEE Transactions on signal processing 39, no. 5 (1991): 1208-1210.
Yan, Feng-Gang, Yi Shen, and Ming Jin. "Fast DOA estimation based on a split subspace decomposition on the array covariance matrix." Signal Processing 115 (2015): 1-8.
Yan, Feng-Gang, Liu Shuai, Jun Wang, Jun Shi, and Ming Jin. "Real-valued root-MUSIC for DOA estimation with reduced-dimension EVD/SVD computation." Signal Processing 152 (2018): 1-12.
Jacobs, M., Georgiev, I., ?or?evi?, S., Oliveira, F., & Sánchez, F. Efficient Clustering Algorithms for Big Data Analytics. Kuwait Journal of Machine Learning, 1(3). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/138
Butt, Uzma M., Shoab A. Khan, Anees Ullah, Abdul Khaliq, Pedro Reviriego, and Ali Zahir. "Towards low latency and resource-efficient FPGA implementations of the MUSIC algorithm for direction of arrival estimation." IEEE Transactions on Circuits and Systems I: Regular Papers 68, no. 8 (2021): 3351-3362.
Zheng, Wang, Xiaofei Zhang, Yunfei Wang, Jinqing Shen, and Benoit Champagne. "Padded coprime arrays for improved DOA estimation: Exploiting hole representation and filling strategies." IEEE Transactions on Signal Processing 68 (2020): 4597-4611.
Chaudhary, A. ., Sharma, A. ., & Gupta, N. . (2023). Designing A Secured Framework for the Steganography Process Using Blockchain and Machine Learning Technology. International Journal of Intelligent Systems and Applications in Engineering, 11(2s), 96–103. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2512
Ganage, Dharmendra, and Yerram Ravinder. "PCA assisted DTCWT denoising for improved DOA estimation of closely spaced and coherent signals." International Journal of Electronics and Telecommunications (2019): 111-117.
Dakulagi, Veerendra, and Jin He. "Improved direction-of-arrival estimation and its implementation for modified symmetric sensor array." IEEE Sensors Journal 21, no. 4 (2020): 5213-5220.
Ahmed Abdelaziz, Machine Learning Approaches for Predicting Stock Market Volatility , Machine Learning Applications Conference Proceedings, Vol 3 2023.