Low Computational Sensing with Goertzel Filtering for Mobile Industrial IoT Devices

Abstract

Internet-of Things (IoT) is getting connected to an increasing number of mobile devices such as autonomous vehicles, drones and robots. Termed as Mobile Industrial Internet-of Things (MI²oT) devices in this paper, a key requirement of these devices is to accurately estimate range and Doppler in various applications, in addition to data communication. Research efforts therefore include incorporating MI²-oT devices with high-data rate communications together with Frequency Modulated Continuous Wave Radar (FMCW) sensing capabilities. Range and Doppler sensing, in FMCW radars is undertaken by a twostage Fast Fourier Transform (FFT) which is computationally demanding. It is challenging to design baseband processing with FFTs that can be implemented as low computational hardware or application specific integrated circuits (ASIC) in MI²-oT devices. This paper, presents a novel range and Doppler sensing technique based on Goertzel filtering, leading to considerable reduction in computations compared to the FFT. FMCW radar with Goertzel filtering and FFT are examined in three cases viz., sensing the range and velocity of a car, vibration and respiration monitoring. Simulation results show a computation reduction of the order of 6.3×, 7.7× and 8.1× \u1d422\u1d427Giga-operations per second (GOPS) for the three cases respectively. The reduced computations increase the feasibility of implementing range and Doppler sensing in MI²oT devices which have restricted computational resources