Determination of Fast-Moving Objects’ Speed and Range with Linear Frequency Modulation Continuous Wave Radar Using Autocorrelation Scheme

Introduction. A hardware basis of modern Advanced Driver Assistance Systems (ADAS) consists of millimeterrange radars, characterized by a relatively short range (meters – tens of meters). At the same time, improving of traffic safety requires to increase the range at least to several hundred meters. The one way to achieve such values is to increase wavelength of a probing signal, to use the centimeter range of wavelengths, for example. The paper represents a detailed description of main steps of signal processing algorithm in the model of the ADAS low-power centimeter range radar, which provides fast-moving objects speed and range definition.Aim. Development of an algorithm for estimating the range and the speed of targets by an autocorrelation radar with a wide-band continuous linear frequency modulation (linear FM) signal in order to increase the rate of the ADAS system estimates formation.Materials and methods. The proposed algorithm is based on the methods of primary and secondary digital processing of radar signals. The model of a centimeter-range autocorrelation radar with a broadband continuous linear FM probing signal was used for practical researches. MATLAB software was used to process the received signal samples.Results. The algorithm has been developed to determine the speed and the range of fast-moving objects in conditions when their movement during the evaluation interval significantly exceeds the radar range resolution. The use of simplified Kalman filtering for inter-period secondary signal processing allowed to increase significantly the stability of the algorithm. In a full-scale experiment using the low-power radar model with continuous radiation of the centimeter range, it was shown that a stable assessment of a real car speed and range was provided at a distance of at least about one kilometer.Conclusion. The results of the field experiment make it possible to draw conclusions that the proposed algorithm is highly robust even in the absence of inter-period secondary processing. Its usage allows one to improve the stability of the algorithm without considerable additional computational costs. It is possible because near-linear dynamics of the observation object and of the radar carrier makes it sufficient to use a simplified implementation of Kalman filter in the form α-β-algorithm.