Abstract: The present invention relates to the reduction of multi-sensor data when used as input to machine-learning (ML) models. Typically, ML models use sensor data to learn characteristics of a problem domain. This data is usually input to the ML model in an end-to-end fashion: the data from sensor 1 is appended with the data from sensor 2, etc., until the entire concatenated data set forms a single input example from which the model learns. The more sensors, the more data, the larger the size of the data input to the ML model, and the longer it is likely to take to train and run the model. Disclosed is a method to combine data from multiple sensors, reducing it into a smaller input data space. The data from 2 or more sensors of the same type can be combined in the same input data space, to simplify the input data size, enabling smaller, faster machine-learning models.

Abstract: Disclosed is the networking of the radar in manners and operating utilizing methods that result in increases in the radar coverage by adding to the possible collection of locations and thus potentially increasing the data to be analyzed—ultimately increasing the accuracy of the readings.