Indoor Positioning via Sensor Pattern Correlation
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Solution Overview
Problem
Existing indoor positioning technologies face challenges in accurately determining the location of mobile terminals indoors due to signal attenuation and the need for extensive mapping, which is costly and impractical in many cases, leading to errors in positioning.
Innovation Solution
A method that uses inertial and non-inertial sensors to identify patterns in indoor environments, allowing for accurate positioning without prior knowledge of radio-field or magnetic field distributions, and enables data storage associated with specific positions, allowing for statistical averaging of data from multiple users to reduce error accumulation and eliminate the need for a building layout.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If satellite signals are used for positioning, then positioning works well in open localities, but it becomes practically incapable indoors due to signal attenuation through walls and ceilings
Solution Approach 1:
The patent introduces intermediate reference points (beacons) deployed throughout the indoor environment that relay positioning information. These beacons act as mediators between the satellite signals (which cannot directly penetrate walls) and the mobile terminal, enabling indirect positioning through multiple hops of signal transmission.
Solution Approach 2:
The patent replaces the direct satellite-to-terminal signal path with an alternative positioning mechanism using local beacons and trilateration mathematics. This substitution eliminates dependence on satellite signal penetration through building structures by using locally-generated reference signals that can be reliably received indoors.
2Measurement precision
If fingerprinting technique is used with non-inertial sensors, then positioning accuracy can be increased, but full mapping of indoor environments is required which is expensive and not always practicable
Solution Approach 1:
The patent divides the indoor environment into discrete zones, each containing one or more reference beacons. Instead of requiring complete environmental mapping, the system segments the space into manageable units that can be independently characterized and used for positioning, reducing the overall complexity requirement.
Solution Approach 2:
The patent implements partial mapping by deploying beacons only at strategic reference points rather than requiring complete coverage of the entire environment. This partial action approach provides sufficient positioning capability for many applications without the expense and complexity of full environmental mapping.
3Ease of manufacture
If dead reckoning with inertial sensors is used, then positioning can be performed without mapping, but errors accumulate over time reducing accuracy
Solution Approach 1:
The patent implements feedback by periodically correcting inertial sensor drift using measurements from the beacon-based trilateration system. When the terminal detects beacons, it receives position corrections that reset accumulation errors, allowing the system to maintain both simplicity and accuracy over extended periods.
Solution Approach 2:
The patent merges two positioning approaches: inertial dead reckoning for continuous positioning between beacon detections, and beacon-based trilateration for periodic accuracy correction. This combination allows the system to maintain implementation simplicity while preventing error accumulation through periodic resets.
4Extent of automation
If crowd-sourcing mapping is implemented by people during everyday movement, then mapping can be automated without additional equipment, but accuracy degrades as non-inertial data with inaccurate positions get entered into the map
Solution Approach 1:
The patent performs preliminary action by first establishing an accurate reference framework using precisely positioned beacons before allowing crowd-sourced data collection. This preliminary setup creates a trusted coordinate system into which subsequent user-generated data can be accurately integrated, preventing propagation of positioning errors.
Solution Approach 2:
The patent introduces beacon-based trilateration as an intermediary layer between raw sensor data and the final map. This intermediary processing step filters and corrects inaccurate positions before they enter the crowd-sourced map, maintaining map accuracy while preserving automation benefits.
5Measurement precision
If statistical averaging of data from multiple users is performed, then error accumulation is reduced and positioning accuracy is improved, but more data storage and processing is required
Solution Approach 1:
The patent merges positioning observations from multiple users and multiple beacon detections into a single statistical estimate using trilateration mathematics. By combining these independent measurements, the system reduces random errors and achieves higher accuracy without requiring complex individual user tracking.
Solution Approach 2:
The patent changes the parameter aggregation approach by using statistical averaging of positional estimates rather than simple averaging of raw sensor data. This parameter transformation at the estimation level achieves error reduction while keeping data processing requirements manageable through efficient mathematical operations.
Data Source
AI summary
The technique and the system may be used for indoor positioning, where signals of navigation satellites are not available. In accordance with the technique patterns identifying location of the mobile terminal in a specific position may be detected, on the basis of data acquired from at least one inertial and non-inertial sensors in the process of movement of at least one mobile terminal; the path of movement of the above mobile terminal may be detected and saved in the local coordinate system associated with the above position, as well as data acquired from non-inertial sensors; statistically averaged parameters of conversion of local coordinate system of the mobile terminal may be generated in the positions detected in the process of terminal movement; at least one map of distribution of output values of non-inertial sensors may be prepared on the basis of data acquired at the previous step; the position of the above mobile terminal may be detected on the basis of data acquired at the previous step. The system may include a set of sensors of mobile terminal, a computer, a probability computation module, a module for selection of patterns, a data storage package and a coordinate converter.