Optimized Artifact Removal Algorithm for Microwave Imaging in Industrial Wireless Sensor Networks
Ahmadreza Zamani, Mohammad Ali Pourmina, Ramin Shaghaghi Kandovan, Parviz Aali, Javad Nouri PourAbstract
Background:
Microwave imaging using ultra-wideband (UWB) radar systems has emerged as a promising noninvasive technique for breast tumor detection. However, skin artifacts significantly reduce imaging accuracy and tumor localization performance.
Methods:
An optimized artifact removal algorithm based on autocorrelation analysis was developed for industrial wireless sensor networks (IWSNs) in microwave imaging. The transmitted signal utilized linear frequency modulation with a pulse width of 2 ns and a bandwidth of 20 MHz. Skin artifacts were identified through autocorrelation and subsequently removed from received signals while preserving tumor reflections. The proposed method was evaluated under different receiver antenna configurations and compared with Wiener filtering and singular value decomposition (SVD) methods.
Results:
Simulation results demonstrated that the proposed algorithm achieved lower root mean square error (RMSE), higher damping speed, improved image quality, and reduced computational complexity compared with conventional methods. The proposed approach also showed superior detection accuracy and robustness under different signal-to-noise ratios and antenna arrangements.
Conclusion:
The proposed autocorrelation-based artifact removal algorithm effectively suppresses skin artifacts while preserving diagnostically relevant tumor signals. The method improves microwave imaging performance and has strong potential for reliable and accurate breast cancer detection in clinical applications.