High Dimension Copresence Estimation Using Multi-Channel Signal Vectors
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Solution Overview
Problem
Conventional GPS systems are not reliable or accurate enough, especially in urban environments, making it difficult to determine if two user devices are co-located or within a close proximity, which is crucial for services like ride-sharing and delivery.
Innovation Solution
A network system using multiple signals such as WiFi, Bluetooth, cellular, and ultra-wideband to provide high-dimensional multichannel copresence estimation, allowing for precise determination of device co-location without relying on GPS, and is flexible enough to work across different levels and environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS is used to detect locations of user devices, then the system is simple and easy to implement, but the measurement precision and reliability are insufficient in urban environments
Solution Approach 1:
The patent segments the location detection task into multiple independent signal channels (WiFi, Bluetooth, cellular, ultra-wideband) instead of relying on a single GPS system. Each signal type provides independent measurements that are combined to achieve higher precision location detection in urban environments where GPS fails.
Solution Approach 2:
The patent transitions from two-dimensional GPS coordinates to multi-dimensional signal space by incorporating multiple signal types with different characteristics. This dimensional expansion allows the system to resolve location ambiguity in urban canyons by utilizing vertical layering of signals and multiple propagation paths.
2Reliability
If multiple signal channels are used for copresence estimation, then the measurement precision and reliability improve, but the device complexity and data processing requirements increase
Solution Approach 1:
The patent merges multiple signal channels (WiFi, Bluetooth, cellular, ultra-wideband) into a unified copresence estimation framework. By combining these diverse signal sources and their respective strength indicators, the system achieves reliable copresence determination that overcomes the limitations of any single signal type.
Solution Approach 2:
The patent changes the measurement parameters from simple GPS coordinates to multi-parameter signal strength indicators (RSSI values) across different frequency bands and signal types. This parameter transformation enables more reliable copresence detection by capturing the nuanced variations in signal propagation that occur in urban environments.
3Measurement precision
If traditional GPS-based methods are used, then the system requires minimal infrastructure, but it cannot accurately determine device co-location in urban canyons
Solution Approach 1:
The patent creates a universal copresence estimation system that functions across multiple signal domains and environmental conditions. The same framework processes WiFi, Bluetooth, cellular, and ultra-wideband signals uniformly, enabling accurate device co-location detection in diverse settings including urban canyons, indoor environments, and outdoor areas where GPS is unavailable.
Data Source
AI summary
A network system receives, from each of a plurality of client devices, a transmission of a scan of signals from signal broadcasting devices. A vector is created from each scan, whereby the vector comprises a received signal strength indicator (RSSI) to each unique signal broadcasting device. Based on the vector created from each scan, the network system determines a probability that the client devices are co-present. The probability is determined based on applying the vectors to a copresence estimation function that uses an angular similarity, a magnitude similarity, and a number of signal broadcasting devices. Based on the probability, the network system triggers a component to perform a corresponding operation.