Fixed grid-based radar sensing method for high dynamic railroad environment

Accurate detection of obstacles is an important condition for trains to be able to travel normally in complex environments and is also an important symbol reflecting the level of automation of rail lines. This paper proposes a fixed grid-based radar sensing method for a high dynamic railroad environment, firstly using a fixed grid-based millimeter wave radar denoising method to remove the flicker noise appearing in the millimeter wave radar sensing process; secondly classifying the effective targets into static targets and dynamic targets by clustering algorithm according to the obtained information of velocity, acceleration and position coordinates of the effective targets; finally screening the effective targets for warning level according to the pre-established radar target warning level classification table. The experiments show that the algorithm proposed in this paper is stable, can adapt to the high dynamic railroad environment, effectively removes the flash-out noise, and improves the accuracy of millimeter wave radar target recognition.