Volume-11, Issue-6, June 2025

Abstract

This paper proposes a new technique for the design of multirate filter banks with linear phase in frequency domain. To match the ideal system response, low-pass analysis prototype filter response is optimized to minimize an objective function. The objective function is formulated as a weighted sum of pass-band error and stop-band residual energy of low-pass analysis filter, the square error of the overall transfer function at the quadrature frequency and amplitude distortion of the filter bank. Quasi-Newton optimization method is used to minimize the objective function by optimizing the filter tap weights of the prototype filter. Simulation results show that the proposed method is able to perform better than other existing methods.

Keywords: Quadrature mirror filter bank, Quasi-Newton optimization method, Peak reconstruction error, Linear phase.

References

1. Vaidyanathan, P. P. (1990). Multirate digital filters, filter banks, polyphase networks and applications: A tutorial. Proceedings of the IEEE, 78(1), 56–93.
2. Vaidyanathan, P. P. (1993). Multirate systems and filter banks. Prentice Hall.
3. Petraglia, A., & Mitra, S. K. (1992). High speed A/D conversion incorporating a QMF. IEEE Transactions on Instrumentation and Measurement, 41(3), 427–431.
4. Chan, S. C., Pun, C. K. S., & Ho, K. L. (2004). New design and realization techniques for a class of perfect reconstruction two-channel FIR filter banks and wavelet bases. IEEE Transactions on Signal Processing, 52(7), 2135–2141.
5. Sablatash, M. (2008). Designs and architectures of filter bank trees for spectrally efficient multi-user communications: Review, modifications and extensions of wavelet packet filter bank trees. Signal, Image and Video Processing, 5(1), 9–37.
6. Xia, T., & Jiang, Q. (1995). Optimal multifilter banks: Design related symmetric extension transform and application to image compression. IEEE Transactions on Signal Processing, 47(7), 1878–1889.
7. Smith, M. J. T., & Eddins, S. L. (1990). Analysis/synthesis techniques for sub-band image coding. IEEE Transactions on Acoustics, Speech, and Signal Processing, 38(8), 1446–1456.
8. Bellanger, M. G., & Daguet, J. L. (1974). TDM-FDM transmultiplexer: Digital polyphase and FFT. IEEE Transactions on Communications, 22(9), 1199–1204.
9. Vetterli, M. (1984). Multidimensional sub-band coding: Some theory and algorithms. Signal Processing, 6(2), 97–112.
10. Wackersreuther, G. (1986). On two-dimensional polyphase filter banks. IEEE Transactions on Acoustics, Speech, and Signal Processing, 34(1), 192–199.
11. Chandran, S. (2003). A novel scheme for a sub-band adaptive beam forming array implementation using quadrature mirror filter banks. Electronics Letters, 39(12), 891–892.
12. Painter, T., & Spanias, A. (2000). Perceptual coding of digital audio. Proceedings of the IEEE, 88(4), 451–513.
13. Afonso, V. X., Tompkins, W. J., Nguyen, T. Q., & Luo, S. (1999). ECG beat detection using filter banks. IEEE Transactions on Biomedical Engineering, 46(2), 192–202.
14. Charafeddine, H., & Groza, V. (2013, May). Wideband adaptive LMS beamforming using QMF subband decomposition for sonar. In 8th IEEE International Symposium on Applied Computational Intelligence and Informatics (pp. 431–436). Romania.
15. Hara, S., Masutani, H., & Matsuda, T. (1999). Filter bank-based adaptive interference canceler for co-existence problem of TDMA/CDMA systems. In IEEE VTS 50th Vehicular Technical Conference (Vol. 3, pp. 1658–1662).
16. Johnston, J. D. (1980, April). A filter family designed for use in quadrature mirror filter banks. In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (pp. 292–294).
17. Jain, V. K., & Crochiere, R. E. (1984). Quadrature mirror filter design in time domain. IEEE Transactions on Acoustics, Speech, and Signal Processing, 32(4), 353–361.
18. Andrew, L., Franques, V. T., & Jain, V. K. (1997). Eigen design of quadrature mirror filters. IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing, 44(9), 754–757.
19. Chen, C. K., & Lee, J. H. (1992). Design of quadrature mirror filters with linear phase in the frequency domain. IEEE Transactions on Circuits and Systems, 39(9), 593–605.
20. Xu, H., Lu, W. S., & Antoniou, A. (1998). An improved method for the design of FIR quadrature mirror image filter banks. IEEE Transactions on Signal Processing, 46(6), 1275–1281.
21. Lu, W. S., Xu, H., & Antoniou, A. (1998). A new method for the design of FIR quadrature mirror-image filter banks. IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing, 45(7), 922–927.
22. Nayebi, K., Barnwell, T. P., III, & Smith, M. J. T. (1992). Time domain filter analysis: A new design theory. IEEE Transactions on Signal Processing, 40(6), 1412–1428.
23. Sahu, O. P., Soni, M. K., & Talwar, I. M. (2006). Marquardt optimization method to design two channel quadrature mirror filter banks. Digital Signal Processing, 16(6), 870–879.
24. Sahu, O. P., Soni, M. K., & Talwar, I. M. (2006). Designing quadrature mirror filter banks using steepest descent method. Journal of Circuits, Systems and Computers, 15(2), 29–42.
25. Swaminathan, K., & Vaidyanathan, P. P. (1986). Theory and design of uniform DFT, parallel QMF banks. IEEE Transactions on Circuits and Systems, 33(12), 1170–1191.
26. Ghosh, P., Das, S., & Zafar, H. (2012). Adaptive-differential-evolution-based design of two-channel quadrature mirror filter banks for sub-band coding and data transmission. IEEE Transactions on Systems, Man, and Cybernetics—Part C: Applications and Reviews, 42(6), 1613–1623.
27. Jou, Y.-D. (2007). Design of two-channel linear-phase quadrature mirror filter banks based on neural networks. Signal Processing, 87(5), 1031–1044.
28. Agrawal, S. K., & Sahu, O. P. (2013). Two-channel quadrature mirror filter bank: An overview. ISRN Signal Processing, 2013, Article 815619. https://doi.org/10.1155/2013/815619
29. Bansal, S., & Rattan, M. (2022). Design of cognitive radio system and comparison of modified whale optimization algorithm with whale optimization algorithm. International Journal of Information Technology, 14, 999–1010.
30. Himani, & Agrawal, S. K. (2020). Modified Gbest-guided ABC algorithm approach applied to various nonlinear problems. In Optical and Wireless Technologies: Lecture Notes in Electrical Engineering (Vol. 648). Springer. https://doi.org/10.1007/978-981-15-2926-9_67
31. Agrawal, S. K., & Sahu, O. P. (2014). Artificial bee colony algorithm to design two-channel quadrature mirror filter banks. Swarm and Evolutionary Computation, 1–8.

Abstract

In this paper, a novel self-localization procedure for wireless sensor networks is presented. Due to errors in calculating universal coordinates and inappropriate relative pairwise distance assessment, the errors in the appraisal of localization may occur. Minimization of these errors is necessary for effective localization arrangements. In irregular or sparse networks, the earlier proposed DV-Hop positioning procedure shows poor accuracy and is not effective. To avoid the weaknesses of this procedure, in this work an enhanced DV-Hop procedure for self-localization (EDVHPSL) is proposed. The anticipated EDVHPSL method delivers improvement in results in terms of localization error. MATLAB simulations have been performed to implement the proposed EDVHPSL by changing the benchmark parameters during simulation process. The parameters are number of beacon nodes, communication range, number of sensor nodes and hop count.

Keywords: Communication range, Wireless Sensor Network (WSN), Sensor Node, Beacon nodes, Localization error minimization.

References

1. Liu, H., Bolic, M., Nayak, A., & Stojmenović, I. (2008). Taxonomy and challenges of the integration of RFID and wireless sensor networks. IEEE Network, 22(6), 26–35.
2. Bulusu, N., Heidemann, J., & Estrin, D. (2000). GPS-less low cost outdoor localization for very small devices. IEEE Personal Communications, 7(5), 28–34.
3. Savarese, C., Rabaey, J., & Beutel, J. (2001). Location in distributed ad-hoc wireless sensor networks. In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (Vol. 4, pp. 2037–2040).
4. Singh, S. P., & Sharma, S. C. (2018). A modified DV-Hop algorithm for wireless sensor network localization. International Journal of Communication Networks and Distributed Systems, 21(2), 143–158.
5. Han, F., Abdelaziz, I. I. M., Liu, X., & Ghazali, K. H. (2020). An enhanced distance vector-hop algorithm using new weighted location method for wireless sensor networks. International Journal of Advanced Computer Science and Applications, 11(5), 486–495.
6. Tao, Q., & Zhang, L. (2016). Enhancement of DV-Hop by weighted hop distance. In IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC) (pp. 1577–1580).
7. Wu, M., Zhong, L., Tan, L., & Xiong, N. (2020). The sequential fusion estimation algorithms based on Gauss-Newton method over multi-agent networked systems. IEEE Access, 8, 114315–114329.
8. Chang, C. Y., Lin, C. Y., & Chang, C. T. (2012). Tone-based localization for distinguishing relative locations in wireless sensor networks. IEEE Sensors Journal, 12(5), 1058–1070.
9. Yin, W. Z. Q., Chen, H., Gao, F., & Ansari, N. (2013). Distributed angle estimation for localization in wireless sensor networks. IEEE Transactions on Wireless Communications, 12(2), 527–537.
10. Kantarci, M. E., Mouftah, H. T., & Oktug, S. (2011). A survey of architectures and localization techniques for underwater acoustic sensor networks. IEEE Communications Surveys & Tutorials, 13(3), 487–502.
11. He, Q. S. C., Chen, H., & Jiang, L. (2010). Distributed wireless sensor network localization via sequential greedy optimization algorithm. IEEE Transactions on Signal Processing, 58(6), 3328–3340.
12. Wang, P., & Akyildiz, I. F. (2011). Spatial correlation and mobility-aware traffic modeling for wireless sensor networks. *IEEE/ACM Transactions on Networking, 19*(6), 1860–1873.
13. Salman, N., Ghogho, M., & Kemp, A. H. (2013). Optimized low complexity sensor node positioning in wireless sensor networks. IEEE Sensors Journal, 14(1), 39–46.
14. Estrin, D., Girod, L., Pottie, G., & Srivastava, M. (2001). Instrumenting the world with wireless sensor networks. In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (Vol. 4, pp. 2033–2036).
15. Stephen, R. J., Rajanna, K., Dhar, V., Kumar, K. G. K., & Nagabushanam, S. (2004). Thin film strain gauge sensors for ion thrust measurement. IEEE Sensors Journal, 4(3), 373–377.
16. Al Karaki, J. N., & Kamal, A. E. (2004). Routing techniques in wireless sensor networks: A survey. IEEE Wireless Communications, 11(6), 6–28.
17. Puccinelli, D., & Haenggi, M. (2005). Wireless sensor networks: Applications and challenges. IEEE Circuits and Systems Magazine, 5(3), 19–29.
18. Wang, F., & Liu, J. (2011). Networked wireless sensor data collection: Issues, challenges, and approaches. IEEE Communications Surveys & Tutorials, 13(4), 673–687.
19. Gungor, V. C., Lu, B., & Hancke, G. P. (2010). Opportunities and challenges of wireless sensor networks in smart grid. IEEE Transactions on Industrial Electronics, 57(10), 3557–3564.
20. Dong, W., Chen, C., Liu, X., & Bu, J. (2010). Providing OS support for wireless sensor networks: Challenges and approaches. IEEE Communications Surveys & Tutorials, 12(4), 519–530.
21. Guolin, S., Jie, C., Wei, G., & Liu, K. J. R. (2005). Signal processing techniques in network-aided positioning: A survey of state-of-the-art positioning designs. IEEE Signal Processing Magazine, 22(4), 12–23.
22. Charfi, Y., Wakamiya, N., & Murata, M. (2009). Challenging issues in visual sensor networks. IEEE Wireless Communications, 16(2), 44–49.
23. Conti, M., Willemsen, J., & Crispo, B. (2013). Providing source location privacy in wireless sensor networks: A survey. IEEE Communications Surveys & Tutorials, 15(3), 1238–1280.
24. Lazos, L., & Poovendran, R. (2000). HiRLoc: High resolution robust localization for wireless sensor networks. IEEE Journal on Selected Areas in Communications, 7(5), 28–34.