FMCW Radar RIS Detection for Distortion-Aware Robot Navigation
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Solution Overview
Problem
RIS devices cause distortions and false readings in radar signals, impacting the ability of radar devices to accurately detect and track targets, particularly in environments where robots are performing exploration tasks.
Innovation Solution
A computer-implemented method using FMCW radar devices with an RIS detector module to process intermediate frequency signals, detecting characteristics such as sudden signal loss or amplitude variations within a confined frequency bandwidth to identify the presence of RIS devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If radar devices operate in environments with RIS devices, then the radar can detect targets, but the RIS devices cause signal distortions and false readings that reduce detection accuracy
Solution Approach 1:
The radar device performs preliminary detection of RIS devices before conducting normal target detection. By identifying the presence of RIS devices in advance through characteristic signal analysis, the system can take corrective actions or adjust detection parameters to mitigate the harmful effects of RIS-induced signal distortions, thereby preserving measurement precision.
Solution Approach 2:
The patent converts the harmful signal reflections from RIS devices into a beneficial detection opportunity. By analyzing the characteristic signals reflected from RIS devices, the system not only identifies their presence but also uses these signals to improve overall detection accuracy by compensating for distortions and eliminating false readings.
2Productivity
If the robot navigates to maximize exploration rate, then the exploration efficiency improves, but the presence of RIS devices causes false readings that lead to inaccurate mapping and path optimization
Solution Approach 1:
Before the robot performs exploration and mapping tasks, the radar device conducts preliminary detection to identify RIS devices in the environment. This advance knowledge allows the robot's navigation and mapping algorithms to account for RIS-induced distortions, ensuring reliable mapping accuracy while maintaining high exploration rates.
Solution Approach 2:
The system implements feedback by continuously monitoring radar signals for characteristic patterns indicating RIS presence. This feedback loop enables real-time adjustment of detection and navigation parameters, allowing the robot to maintain accurate mapping and path optimization even in environments with RIS devices that would otherwise cause false readings.
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
Enables accurate detection and mitigation of RIS devices, improving the reliability and accuracy of radar operations in environments with RIS, allowing robots to optimize their path and enhance exploration efficiency.
Implementation Method 1
mixing the TX signal and the analog RX signal using a mixer of the radar device to generate an intermediate frequency (IF) signal
Implementation Method 2
frequency modulated continuous wave (FMCW) radar devices
Data Source
AI summary
A computer-implemented method for detecting the presence of a reconfigurable intelligence surfaces (RIS) device using a radar device includes receiving an analog received (RX) signal using a receiver antenna of the radar device based on a transmitted (TX) signal. The method further includes mixing the TX signal and the analog RX signal to generate an intermediate frequency (IF) signal and processing the IF signal to detect a characteristic of the IF signal that indicates a presence of the RIS device. The method has applications in optimization and/or decision making associated with robots. For instance, based on performing the RIS detection, the robot can optimize its path to survey an environment (e.g., maximize exploration rate that is constrained by the battery of the robot). In some embodiments, machine learning (ML) and/or artificial intelligence (AI) techniques can be used to perform the RIS detection.