Interview

Abstract

Prof. Zhike Peng from Shanghai Jiao Tong University, talks to Electronics Letters about the background to his paper ‘Static clutter elimination for FMCW radar displacement measurement based on phasor offset compensation’, page 1491. Prof. Zhike Peng Currently, part of my research focuses on signal processing, microwave detection, and micro-Doppler analysis, in particular for microwave/RF radar vibration monitoring, biomedical, and some other industrial applications. To realise non-contact vibration and movement monitoring, we found microwave radar to be a good approach, as it is low-cost and has high robustness against harsh environments. Fortunately, benefiting from the technical advances in high-frequency printed circuit board and hardware miniaturisation technology, microwave radar modules are widely used in various applications. Therefore, we try to use microwave radar, as an effective short-range sensor to perform non-contact displacement tracking. Specifically, we use frequency-modulated continuous-wave (FMCW) radar to measure the displacement variation in vibration motion. Currently, using phase-based radar interferometry technique, FMCW radar has excellent capacity to measure small displacement variation. However, since the radar antenna has a finite radiation beam-width, i.e. it cannot be a point, all targets within the radar radiation coverage reflect echoes and the radar baseband signal is the sum of multiple components. Generally, FMCW radar can isolate different targets with a different range bin, but struggles to isolate components generated by targets in the same bin. Reflections from stationary objects (i.e. static clutter) near the desired target interfere with the wanted signals backscattering from the moving scatterers at the same range, causing the estimated displacement time series to be smaller than the truth. When the stationary object and the desired target have comparable reflective performance, the static clutter interference has a significant impact on the estimation accuracy. In our Letter, a post-processing procedure based on phasor offset compensation (POC) is proposed to eliminate the static clutter interference. It can be implemented easily by using an updated signal processing algorithm and achieves accurate displacement measurement. Previously, for this problem existing works mainly focus on the hardware side, such as installing a strong reflection point, or using frequency-shift transponders, which increase hardware complexity and is limited in real-life applications. Comparatively, our POC technique is a novel way to solve the static clutter interference problem from a signal processing side. This work can be directly applied in FMCW radar displacement measurement, e.g. vibration monitoring, large structural health monitoring, slow movement detection and vital sign detection. In the long run, the POC technique can be extended in other phase-based measurement system for a wide variety of applications. Currently, one portion of our research group is working on radar signal processing algorithms to achieve good performance in related applications. Specifically, we are now working on the research of non-contact vibration monitoring and structural health monitoring based on FMCW radar, and micro-Doppler analysis for pattern recognition. In the near future, we would like to extend our research in radar imaging and environmental perception. Then, we would like to investigate the information fusion technique combined with the camera, to obtain better and more intelligent environmental imaging and recognition. In the next 10 years, we would like to see more integrated software defined radar developing platforms and advanced radar signal processing algorithms for a variety of applications. Moreover, we expect to see intelligent radar detection systems with multiple type radars. Various sensors have their own advantages and drawbacks, therefore we need to see intelligent information fusion techniques to achieve better environmental imaging and recognition. This includes object recognition and physical quantity measurement, which is greatly beneficial to autopilot, robot vision and other fields. We would like to develop advanced signal processing methods, intelligent information fusion techniques and several prototypes of intelligent sensor to boost the development in this field.