Real-Time RCS Extracted Features for Over-ridable Object Classification

77 GHz band FMCW radar is used, which receives a non-stationary sequence of Radar Cross-Section (RCS) for every detected object. These RCS schemes are used in automotive radar systems. Most established non-stationary sequence classification approaches use radar images or a complex signal modeling method. Due to less on-chip memory, stringent run-time requirements, and space complexity of the problem, we proposed a novel way of extracting representative features to avoid false braking events with a focus on memory and code optimization. This paper deals with a feature-based sequence classification problem wherein features are extracted by identifying patterns and trends, which are then used by the machine learning classification model. In this study, we take advantage of linear fitting, curve fitting, exploratory plot analysis, and statistical analysis to create distinguishing features. At every radar cycle, long-range radar can detect a certain number of objects from its environment. Classification of these objects is essential from an ADAS functional perspective. RCS observations of multiple objects were taken at different velocities of an ego vehicle. Data was decoupled by considering the relative range of objects concerning the ego vehicle. The probabilistic model was trained by taking extracted features into account. Detected objects are classified into obstacle class and ground object class. Model performance was compared with a varying feature set. This is an optimized and effective approach for any real-time radar sequence classification problem.