WiFi Motion Tracking via Backscatter Cancellation
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Solution Overview
Problem
Current motion detection systems using radio frequency signals face challenges in accurately tracing motion in low-light or dark environments due to limited dynamic range and bandwidth of practical transceivers, as well as the difficulty in isolating backscatter signals from moving objects amidst numerous static reflectors.
Innovation Solution
The system employs progressive backscatter cancellation techniques and compressed sensing-based estimation algorithms to measure and isolate backscatter components, using WiFi transceivers to transmit and receive radio frequency signals, and applies novel motion detection and tracing algorithms that account for indirect backscatter reflections and correlated signals from static objects to identify moving objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If progressive backscatter cancellation techniques are used to isolate moving objects from static reflectors, then motion detection precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the composite signal into multiple backscatter components through iterative cancellation. Each component corresponds to a specific reflector (static or moving), allowing the system to isolate and analyze moving objects separately from static background reflectors, thereby improving motion detection precision while managing complexity through systematic signal decomposition
Solution Approach 2:
The system performs preliminary channel estimation and backscatter component identification before actual motion detection. By pre-processing the composite signal to characterize static reflectors and their backscatter signatures, the system prepares the data structure needed for accurate motion isolation, reducing the computational burden during real-time detection
2Measurement precision
If compressed sensing-based estimation algorithms are applied to measure backscatter components, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent applies compressed sensing to estimate only the most significant backscatter components rather than attempting to fully resolve all signal details. By focusing computational resources on identifying dominant reflectors and their motion characteristics, the system achieves sufficient measurement precision for motion detection while reducing overall processing time through selective estimation
3Device complexity
If WiFi transceivers with limited dynamic range and bandwidth are used, then device complexity is reduced, but measurement precision of backscatter signals deteriorates
Solution Approach 1:
The patent introduces signal processing algorithms as intermediaries between the limited-capability WiFi transceiver and the motion detection task. The progressive backscatter cancellation and compressed sensing algorithms act as mediators that enhance the effective measurement precision by extracting motion information from the composite signal in ways that compensate for the transceiver's limited dynamic range and bandwidth
Solution Approach 2:
The system replaces hardware enhancements (such as high-dynamic-range transceivers or specialized motion sensors) with software-based signal processing techniques. By substituting mechanical/hardware solutions with algorithmic approaches, the system maintains simple transceiver hardware while achieving high measurement precision through computational methods
4Measurement precision
If algorithms account for indirect backscatter reflections and correlated signals from static objects are used, then motion detection precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the backscatter signal into direct and indirect reflection components, and further divides them into static and moving categories. This systematic segmentation allows the algorithm to handle each component type with appropriate processing strategies, improving precision by accounting for all signal sources while managing complexity through structured decomposition
Solution Approach 2:
The system converts the harmful effect of correlated signals from static objects and indirect reflections into a benefit by using them as reference signatures. These previously problematic signals provide information about the static environment and propagation paths, which the algorithm leverages to enhance motion detection precision through comparative analysis
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
This approach enables accurate detection and tracing of fine-grained motion in low-light conditions, effectively distinguishing moving objects from static ones, even in environments with multiple reflectors, and can track multiple human motions simultaneously.
Implementation Method 1
receiving a composite signal comprising a plurality of backscatter signals. The plurality of the backscatter signals correspond to a reflection of the radio frequency signal from one or more objects in the environment
Data Source
AI summary
Techniques for a motion tracing device using radio frequency signals are presented. The motion tracing device utilizes radio frequency signals, such as WiFi to identify moving objects and trace their motion. Methods and apparatus are defined that can measure multiple WiFi backscatter signals and identify the backscatter signals that correspond to moving objects. In addition, motion of a plurality of moving objects can be detected and traced for a predefined duration of time.