Microwave Sensor Radar Cross Section Motion Estimation
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Solution Overview
Problem
Existing methods for estimating the motion of a living body using microwaves face challenges in accuracy and efficiency, particularly in detecting direction, position, state, and motion, with high cost and complexity issues associated with terahertz band devices and the need for machine learning.
Innovation Solution
A sensor system with multiple transmitting and receiving antenna elements arranged in different vertical and horizontal positions, calculating complex transfer functions and radar cross-section values to estimate three-dimensional positions and motions of living bodies, using temporal changes and correspondence information to identify actions such as falling, sitting, or standing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If terahertz band devices are used to identify head and limbs of living body, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent replaces expensive terahertz band devices with microwave band devices, which are cheaper and more readily available. The microwave sensor system achieves sufficient measurement precision for motion estimation without requiring the complex and costly terahertz technology, thus resolving the contradiction between measurement precision and device complexity/cost.
Solution Approach 2:
The patent changes the operating frequency parameter from terahertz band to microwave band. This parameter change allows the system to achieve acceptable measurement precision for motion detection while significantly reducing device complexity and cost, as microwave devices are more成熟 and less complex than terahertz devices.
2Measurement precision
If machine learning is used to estimate position and state of living body, then measurement precision is improved, but device complexity and processing load increase
Solution Approach 1:
The patent extracts and utilizes specific physical characteristics (radar cross-section value and its temporal changes) from the received signals to estimate living body motion. By focusing on these extracted features rather than applying comprehensive machine learning to all signal data, the system achieves good estimation accuracy while significantly reducing processing load and device complexity.
Solution Approach 2:
Instead of applying full machine learning processing to all available data, the patent applies a simplified estimation method that processes only the essential features (RCS value temporal changes). This partial action approach achieves sufficient estimation accuracy without the excessive processing load of comprehensive machine learning.
3Measurement precision
If multiple antenna elements are used to calculate complex transfer functions, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent uses a moderate number of antenna elements (3 or more transmitting and 3 or more receiving elements) that serve multiple functions: they enable calculation of complex transfer functions for accurate position estimation, while also allowing the system to determine both horizontal and vertical positions. This multi-functional antenna configuration achieves high measurement precision without excessive device complexity.
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 and efficient estimation of living body motion in a short time, reducing processing load and improving accuracy, allowing for the identification of specific actions and positions with high precision.
Implementation Method 1
a transmitting antenna including N transmitting antenna elements, each of which transmits a transmission signal to a specified area
Implementation Method 2
a receiving antenna including M receiving antenna elements, each of which receives N received signals including a reflection signal which the living body generates by reflecting part of the N transmission signals
Data Source
AI summary
A sensor includes: a transmitting antenna with N transmitting antenna elements transmitting transmission signals; a receiving antenna with M receiving antenna elements, each receiving N received signals including a reflection signal generated by the living body reflecting part of the N transmission signals; a circuit; and a memory. The circuit extracts a second matrix corresponding to a specified frequency range from an N×M first matrix calculated from each received signal and indicating a propagation property between each transmitting antenna element and each receiving antenna element, estimates the position where the living body is present using the second matrix, calculates a radar cross-section (RCS) value of the living body based on the estimated position and the positions of the transmission and receiving antennas, and estimates the motion of the living body using the calculated RCS value and information indicating correspondence between RCS values and motions of the living body.