Sparse Micro-Doppler Reconstruction in Joint Communication Sensing
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Solution Overview
Problem
Existing communication systems face challenges in integrating human sensing capabilities without significant overhead and channel utilization, as they require regular and dense sampling of the Channel Impulse Response (CIR) for micro-Doppler estimation, which is not feasible with irregular communication traffic patterns.
Innovation Solution
A method that reconstructs high-quality micro-Doppler signatures from irregular and sparse channel measurements by leveraging the intrinsic sparsity of the channel, using sparse recovery techniques and dynamically injecting sensing units when necessary, thereby reducing overhead and maintaining accurate human activity recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If dedicated radar sensors are used to detect and classify human movements with accurate micro-Doppler estimation, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent repurposes existing communication devices to perform both communication and sensing functions. The communication transceivers, originally designed for data transmission, are made universal by enabling them to also detect and classify human movements through micro-Doppler analysis, eliminating the need for dedicated radar hardware.
Solution Approach 2:
The communication system serves itself by utilizing its own transmitted signals for sensing purposes. The same communication transceivers that transmit data signals use the reflected or scattered versions of these signals to detect human movements, making the system self-sufficient and avoiding additional hardware costs.
2Device complexity
If communication devices are repurposed for sensing to avoid dedicated radar hardware, then device complexity is reduced, but measurement precision deteriorates due to irregular communication traffic patterns
Solution Approach 1:
The patent transforms the irregularly sampled communication signals into regularly spaced pseudo-range profiles through parameter changes in the signal processing domain. By applying autocorrelation operations and resampling techniques, the system converts the variable timing of communication packets into uniformly spaced range bins, enabling accurate micro-Doppler spectrum estimation despite the irregular sampling nature of communication traffic.
3Measurement precision
If dense and regular sampling of Channel Impulse Response is performed to capture fine-grained human motion effects, then measurement precision is improved, but channel occupation and overhead increase
Solution Approach 1:
The patent applies partial action by using only the necessary portion of communication signals for sensing purposes. Instead of requiring dedicated dense sampling sequences, the system extracts micro-Doppler information from a subset of communication packets, performing partial correlation operations and selective processing to achieve adequate measurement precision without occupying the entire communication channel.
4Measurement precision
If communication and sensing are performed in time-division manner with dedicated sensing phases, then measurement precision is improved, but productivity deteriorates due to reduced communication data rates
Solution Approach 1:
The patent merges communication and sensing operations into a unified process. The same communication packets that carry data are simultaneously used for sensing purposes. The system processes communication signals to extract both data information and micro-Doppler features, combining two functions into one integrated operation rather than alternating between them in time-division manner.
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 method achieves better human activity recognition with seven times lower overhead compared to existing methods while maintaining high recognition performance, by effectively utilizing existing communication traffic and minimizing additional sensing overhead.
Implementation Method 1
A well established method to detect and classify human movements using radiofrequency transmitting and receiving devices is the time-frequency analysis of the small-scale Doppler effect (termed micro-Doppler)
Implementation Method 2
the time-frequency analysis of the small-scale Doppler effect (termed micro-Doppler) of the different body parts
Data Source
Figure 1
Figure 2
Figure 3~4
AI summary
The present invention refers to a method and system for joint communication and reconstruction of the micro-doppler time-frequency spectrum from sparse channel measurements.