The Role of Millimeter-Waves in the Distance Measurement Accuracy of an FMCW Radar Sensor

Abstract

High-accuracy, short-range distance measurement is required in a variety of industrial applications e.g., positioning of robots in a fully automated production process, level measurement of liquids in small containers. An FMCW radar sensor is suitable for this purpose, since many of these applications involve harsh environments. Due to the progress in the field of semiconductor technology, FMCW radar sensors operating in different millimeter-wave frequency bands are available today. An important question in this context, which has not been investigated so far is how does a millimeter-wave frequency band influence the sensor accuracy, when thousands of distance measurements are performed with a sensor. This topic has been dealt with for the first time in this paper. The method used for analyzing the FMCW radar signal combines a frequency- and phase-estimation algorithm. The frequency-estimation algorithm based on the fast Fourier transform and the chirp-z transform provides a coarse estimate of the target distance. Subsequently, the phase-estimation algorithm based on a cross-correlation function provides a fine estimate of the target distance. The novel aspects of this paper are as follows. First, the estimation theory concept of Cramér-Rao lower bound (CRLB) has been used to compare the accuracy of two millimeter-wave FMCW radars operating at 60 GHz and 122 GHz. In this comparison, the measurement parameters (e.g., bandwidth, signal-to-noise ratio) as well as the signal-processing algorithm used for both the radars are the same, thus ensuring an unbiased comparison of the FMCW radars, solely based on the choice of millimeter-wave frequency band. Second, the improvement in distance measurement accuracy obtained after each step of the combined frequency- and phase-estimation algorithm has been experimentally demonstrated for both the radars. A total of 5100 short-range distance measurements are made using the 60 GHz and 122 GHz FMCW radar. The measurement results are analyzed at various stages of the frequency- and phase-estimation algorithm and the measurement error is calculated using a nanometer-precision linear motor. At every stage, the mean error values measured with the 60 GHz and 122 GHz FMCW radars are compared. The final accuracy achieved using both radars is of the order of a few micrometers. The measured standard deviation values of the 60 GHz and 122 GHz FMCW radar have been compared against the CRLB. As predicted by the CRLB, this paper experimentally validates for the first time that the 122 GHz FMCW radar provides a higher repeatability of micrometer-accuracy distance measurements than the 60 GHz FMCW radar.

Millimeter wave radar data of people walking

“…It follows that the test total duration is 16 s. This choice of the parameters allows detection of a Doppler shift in the range −8000 Hz to 8000 Hz. The range detection value R max can be derived as follows [7] . First the beat frequency f IF is calculated as where represents the speed of light.…”

A dual‐frequency continuous wave doppler lidar for velocity measurement at far distance

“…CZT algorithm is able to analyze the target spectrum segment and obtain the signal details within a short range of frequency. 20 The CZT formula is shown in Equation 6.…”

“…CZT algorithm is able to analyze the target spectrum segment and obtain the signal details within a short range of frequency 20 . The CZT formula is shown in Equation . where , .…”

A Low-Power High-Accuracy Urban Waterlogging Depth Sensor Based on Millimeter-Wave FMCW Radar

“…Several studies on the algorithms of FMCW mmWave radar systems have been proposed [ 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 ]. Most of the proposed algorithms are based on Fast Fourier Transform (FFT), but it is difficult for FFT-based estimators to distinguish between two adjacent targets, such as ground and the water surface, because of the limitation of the bin width of the FFT spectrum given by , in which and , respectively, denote the sampling frequency and the number of samples [ 20 ]. One way to increase frequency resolution is zero padding used to interpolate the FFT spectrum.…”

“…However, it also adds more computation complexity and can only achieve a resolution of 6.9 cm, which is insufficient for waterlogging monitoring. In [ 20 ] combining frequency and phase estimation, the resolution is around a few microns, but the applicable distance range is 5 mm, which is too small for our system. In [ 23 ], a novel direction of arrival (DOA) estimation algorithm is proposed employing the FFT and achieves very low computational complexity.…”