Radar Occupancy Grid Detection for Sparse Reflection Signals
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Solution Overview
Problem
Conventional radar object detection methods require preprocessing steps like clustering and feature extraction, which are time-consuming and inefficient, especially for sparse radar reflections, limiting their performance and real-time capabilities.
Innovation Solution
A method utilizing radar reflection signals to directly input data into a neural network, transforming them into an occupancy grid, and generating an input tensor for object detection, eliminating preprocessing steps and enabling a one-stage detection process with enhanced computational efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional preprocessing steps (clustering and feature extraction) are applied to radar data, then detection accuracy can be improved through detailed feature analysis, but detection time increases and real-time performance deteriorates
Solution Approach 1:
The patent extracts only the essential features needed for detection directly from raw radar data and feeds them into the neural network, eliminating unnecessary preprocessing steps like clustering. This extraction approach maintains detection accuracy by preserving critical signal characteristics while significantly reducing processing time and computational overhead.
Solution Approach 2:
The neural network is pre-trained with learned features that automatically capture relevant patterns from raw radar data during the training phase. This preliminary learning action enables the network to perform accurate detection directly on raw data during operation, eliminating the need for time-consuming preprocessing steps while maintaining high detection accuracy.
2Loss of information
If multiple preprocessing steps are performed on sparse radar reflections, then feature completeness is improved, but computational overhead increases and efficiency decreases
Solution Approach 1:
The neural network performs self-service by automatically learning and extracting relevant features directly from sparse radar reflections during training. This self-learning mechanism eliminates the need for external preprocessing steps that would otherwise be required to complete feature representation, thereby maintaining feature completeness while maximizing computational efficiency for sparse data.
Solution Approach 2:
The patent changes the parameter representation by transforming raw radar measurements into a format suitable for neural network input through direct mapping rather than traditional preprocessing. This parameter transformation preserves all necessary information from sparse reflections while avoiding the computational overhead of multiple preprocessing operations, thereby improving productivity.
3Reliability
If traditional CFAR detector with multiple processing stages is used, then detection reliability is maintained, but system complexity and processing time increase
Solution Approach 1:
The patent merges multiple traditional detection stages (CFAR detection, feature extraction, object classification) into a single integrated neural network model. This consolidation maintains detection reliability by preserving the essential detection logic while significantly reducing system complexity and eliminating the need for separate processing stages, thereby enabling real-time performance.
Data Source
AI summary
A method for detecting an object includes providing respective spatial coordinates relating to a multiplicity of reflection signals of a frame of a radar sensor system and providing a measurement attribute of a first portion of the multiplicity of reflection signals. The respective spatial coordinates of the multiplicity of reflection signals are transformed into an occupancy grid. An occupancy grid is generated with the multiplicity of reflection signals being spatially represented in the occupancy grid by mapping the respective spatial coordinates of the multiplicity of reflection signals in the occupancy grid and assigning the respective first measurement attribute of the multiplicity of reflection signals to the spatial representation of the multiplicity of reflection signals. An input tensor is generated using the occupancy grid for a trained neural network for detecting the at least one object. The object is detected using the input tensor and the trained neural network.