Multi-Technique Geo-Location System for Signal Integrity
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Solution Overview
Problem
Existing location determination methods using global navigation satellite systems (GNSS) face challenges such as power consumption, transmission integrity issues in urban and rural areas, and imprecision in determining specific locations within buildings or tunnels, leading to inaccurate tagging of content items.
Innovation Solution
The use of a combination of geo-location techniques including GNSS, simultaneous localization and mapping (SLAM), inertial measurement units (IMUs), and image processing to gather and analyze data for precise location determination, utilizing a 'full sensor' approach that integrates multiple data sources like GPS, IMU, and image information to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If GNSS is used for location determination, then location information can be obtained, but transmission integrity deteriorates in urban and rural areas
Solution Approach 1:
The patent combines multiple location determination techniques (GNSS, Wi-Fi positioning, cellular tower triangulation, and inertial sensors) into a unified system. When GNSS signals are unavailable or degraded in urban canyons or rural areas, the system seamlessly integrates alternative methods to maintain continuous and accurate location tracking, thereby resolving the transmission integrity issue while preserving reliability.
Solution Approach 2:
The system dynamically adjusts the weighting and priority of different location determination parameters based on environmental conditions. In areas with poor GNSS reception, the system increases reliance on Wi-Fi fingerprinting and cellular data, effectively changing the operational parameters to compensate for signal loss and maintain location accuracy.
2Measurement precision
If multiple geo-location techniques are combined, then location accuracy improves, but device complexity increases
Solution Approach 1:
The patent implements a dynamic architecture where the system selectively activates different location determination techniques based on real-time conditions. Rather than continuously running all sensors and algorithms, the system adapts its complexity by enabling only the necessary techniques for the current environment, thus improving accuracy when needed while managing device complexity through intelligent resource allocation.
Solution Approach 2:
The system includes automated algorithms that independently evaluate the quality and availability of different location data sources, selecting and weighting them without user intervention. This self-service mechanism manages the complexity of multiple techniques by automatically optimizing their integration, reducing the burden on users while maintaining high precision.
3Reliability
If continuous location tracking is performed, then location information is always available, but power consumption increases
Solution Approach 1:
The patent implements periodic location updates rather than continuous tracking, adjusting the update frequency based on motion detection and environmental factors. When the device is stationary or in environments where location changes are unlikely, the system reduces update frequency to conserve battery power while maintaining reliable location information through predictive algorithms and cached data.
Solution Approach 2:
The system dynamically changes operational parameters such as sensor sampling rates, GPS update frequencies, and processing intensity based on battery charge levels and usage patterns. This allows the system to maintain location availability when needed while significantly reducing power consumption during normal operation through adaptive parameter adjustment.
Data Source
AI summary
According to examples, a system for determining a location using a plurality of geo-location techniques is described. The system may include a processor and a memory storing instructions. The processor, when executing the instructions, may cause the system to receive sensor data associated with the location, receive image information associated with the location, analyze the image information associated with the location, and provide a localization and mapping analysis for the location. The processor, when executing the instructions, may then determine an analyzed list of features and a primary landmark associated with the location, and determine location information for the location based on the analyzed list of features and the primary landmark.