RF Crowd Analytics With Neural Radar Map Denoising
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Solution Overview
Problem
Existing crowd analytics systems in public indoor spaces face limitations such as line-of-sight constraints, inaccurate distance measurements, and privacy concerns due to camera-based solutions, which affect coverage and accuracy.
Innovation Solution
A radio frequency (RF) based crowd analytics system that uses RF signals to penetrate obstacles, measure distances accurately, and preserve privacy by collecting coarse-grained reflections off human bodies, employing RF sensors and neural networks to generate heatmaps of human locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If camera-based solutions are used for crowd analytics, then visual information can be captured, but line-of-sight constraints and visual occlusions result in limited coverage and loss of targets
Solution Approach 1:
The patent replaces camera-based optical detection with radio frequency (RF) sensing technology. RF signals can penetrate obstacles and are reflected off human bodies, enabling detection without line-of-sight requirements. This substitution resolves the contradiction by providing both comprehensive coverage (RF signals propagate through obstacles) and reliable target detection (reflections are consistently captured regardless of occlusions).
Solution Approach 2:
The patent introduces RF signals as an intermediary medium for detection. Instead of directly capturing visual information with cameras, the system uses RF signals that reflect off human bodies as intermediaries to convey presence and location information. This intermediary approach eliminates line-of-sight constraints while maintaining reliable detection capability.
2Loss of information
If camera-based solutions are used for crowd analytics, then individual identification can be attempted, but privacy concerns arise due to potential identification of individuals
Solution Approach 1:
The patent extracts only the essential detection information (presence, location, movement patterns) from RF reflections while deliberately excluding identifying features. The system processes RF signal reflections to generate heatmaps showing crowd density and movement without capturing facial or other personally identifiable information. This extraction approach preserves privacy while maintaining sufficient detection accuracy for crowd analytics purposes.
3Area of stationary object
If RF signals are used to penetrate obstacles and provide non-line-of-sight coverage, then coverage area is improved, but signal accuracy and distance measurement precision may be affected
Solution Approach 1:
The patent employs feedback mechanisms where RF sensors continuously monitor reflected signals and adjust processing parameters based on received signal characteristics. The system analyzes reflection patterns, signal strength, and phase information to compensate for obstacles and maintain accurate distance measurements even when signals penetrate through obstacles. This feedback-driven approach preserves measurement precision while achieving non-line-of-sight coverage.
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 RF-based system provides non-line-of-sight coverage, accurate distance measurements, and privacy preservation by generating precise heatmaps of human locations, overcoming the limitations of camera-based systems.
Implementation Method 1
a device emits RF signals. These signals can penetrate obstacles and are reflected off human bodies.
Data Source
AI summary
A deployment of sensors transmit radio frequency (RF) signals into an area of interest. The radar maps are generated from the reflected signals, including a static radar map and a dynamic radar map. Multipath and radar sidelobes are removed from the radar maps using a neural network to produce a density map. The neural network can be trained in two phases: a training phase that uses training data from a training site and a transfer learning phase that uses training data from the area of interest.