Multi-Radar Cabin Occupancy Localization Against Multipath Clutter
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Solution Overview
Problem
Existing radar systems for occupancy sensing in enclosed environments, such as vehicle cabins, face challenges with multi-path effects and clutter, leading to inaccurate localization of objects and movements, particularly occupants, which conventional algorithms fail to address effectively.
Innovation Solution
Utilizing a system of radar modules and a trained deep neural network to process radar signals, the system localizes movements within the vehicle cabin by preprocessing data into range-Doppler plots and employing convolutional and pooling layers to refine outputs, followed by a classifier layer for precise localization and classification of vehicle conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional radar sensors are used for occupancy sensing, then the system can detect objects in the vehicle cabin, but the localization accuracy deteriorates due to multi-path effects and clutter
Solution Approach 1:
A deep neural network is introduced as an intermediary between the radar sensor data and the localization output. The neural network processes the raw radar signals and range-Doppler plots, learning to distinguish true object reflections from multi-path artifacts and clutter through trained patterns, thereby resolving the contradiction between detection capability and localization accuracy in harsh electromagnetic environments
Solution Approach 2:
The system transforms the radar data representation by generating range-Doppler plots and processing them through multiple neural network layers with varying parameter transformations. This parameter transformation approach converts difficult-to-interpret raw radar signals into enhanced feature representations that improve localization accuracy while maintaining robustness against multi-path effects
2Measurement precision
If a deep neural network is used to process radar data, then localization accuracy is improved, but device complexity increases
Solution Approach 1:
The deep neural network is divided into distinct functional segments: convolutional layers for feature extraction from range-Doppler plots, pooling layers for dimensionality reduction, and fully connected layers for final classification. This segmentation allows each component to specialize in specific processing tasks, improving overall localization accuracy while enabling modular implementation that manages system complexity
3Measurement precision
If multiple radar modules with directional antennas are deployed, then coverage and localization capability are improved, but device complexity and cost increase
Solution Approach 1:
Each radar module is designed with multi-functionality, serving both as a transmitter and receiver, and each can independently perform occupancy detection and localization tasks. The directional antennas are oriented to cover specific zones, and the neural network processes data from multiple modules to achieve comprehensive cabin coverage, reducing the need for additional specialized sensors while maintaining high localization capability
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system accurately localizes subtle movements and occupant positions within the vehicle cabin, overcoming multi-path effects and clutter, enabling real-time monitoring and adjustment of environmental features based on occupant presence and movements.
Implementation Method 1
a plurality of radar modules each including an antenna disposed within the enclosed environment and each configured to radiate radar signals into the enclosed environment and collect radar signals reflected within the enclosed environment
Implementation Method 2
a trained deep neural network processes the preprocessed data sets groupwise to localize movements within the enclosed environment
Data Source
AI summary
Systems and methods for localizing one or more objects within an enclosed environment, and for adjusting an environmental feature associated with the enclosed environment. One or more processors receive output from three or more radar modules based on reflected radar signals as detected by a plurality of antennas. The processor(s) generate a preprocessed data set for each of the p antennas. The movements of the one or more objects within the enclosed environment are localized. Seat occupancy may be determined based on the localized movements of the one or more objects. An environmental feature of the enclosed environment may be adjusted based on the localized movement. The enclosed environment may be a vehicle cabin, and wherein the localized movement is from occupants within the vehicle cabin, or a door being opened. The localization may be performed by a trained deep neural network in which the data sets are processed groupwise.