State Space Estimator Sub-Propagation for Indoor Location Accuracy
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Solution Overview
Problem
Conventional mobile devices struggle to determine location accurately in venues with hard limit constraints, such as buildings or tunnels, due to GPS signal limitations and the risk of particle depletion in state space models.
Innovation Solution
Implementing a state space model with reduced impact of hard limit constraints by dividing the propagation step into sub-propagation steps and using a stochastic process to explore potential routes around constraints, allowing for more accurate location estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a state space model uses hard limit constraints to restrict candidate locations within venue boundaries, then location estimation reliability is improved, but candidate locations may get stuck in dead-end situations and fail to explore alternative routes
Solution Approach 1:
The patent applies dynamics by making the constraint application dynamic rather than static. Candidate locations are evaluated against hard limit constraints (venue boundaries) dynamically during the propagation process, allowing the system to adaptively adjust which constraints are applied at each propagation step based on the current candidate location distribution and observed environmental variables.
Solution Approach 2:
The patent changes parameters by adjusting the propagation time interval and the stochastic process parameters. By modifying the time interval between propagations and the parameters of the stochastic process (such as diffusion coefficient), the system can balance between respecting hard limit constraints and exploring alternative routes around obstacles.
2Productivity
If the propagation step uses a large time interval to reduce computation, then processing speed is improved, but the model cannot adequately explore alternative routes around constraints
Solution Approach 1:
The patent segments the propagation process by dividing it into multiple sub-propagation steps, each with a smaller time interval. This segmentation allows the system to explore alternative routes more thoroughly around constraints while maintaining overall processing efficiency through parallel computation of multiple candidate locations.
Solution Approach 2:
The patent applies partial action by performing propagation only for a fraction of the total time interval in each sub-step, using a stochastic process that explores space proportionally to the reduced time interval. This allows adequate exploration of alternative routes without requiring the full computational resources of a single large-step propagation.
3Measurement precision
If GPS signals are used for location determination, then location accuracy is improved in open areas, but the system fails in buildings or tunnels where GPS signals are weak or blocked
Solution Approach 1:
The patent implements universality by creating a location determination system that works across multiple environments. The state space model with stochastic propagation can operate both in open areas (complementary to GPS) and in GPS-denied environments (buildings, tunnels), making the system universally applicable across diverse operational conditions.
Solution Approach 2:
The patent uses environmental variables (such as Wi-Fi signals, Bluetooth beacons, or other detectable environmental features) as intermediaries for location determination when GPS is unavailable. These intermediary signals allow the system to estimate location in buildings and tunnels by observing changes in the environmental variable patterns as the device moves through space.
Data Source
AI summary
A location of a mobile device in a venue can be estimated by using a state space estimator to determine candidate locations of the mobile device at a first time point based on previous candidate positions conditioned upon an observation of one or more environmental variables. A second observation is received at a second time point, and the state space estimator performs a propagation step to determine the candidate locations at the second time point based on the candidate locations at the first time point and the second observation. The propagation step includes a plurality of sub-propagation steps in which a time length between the sub-propagation steps is a fraction of the time length between the first and second time points, and at each sub-propagation step each candidate location is propagated according to a stochastic process. The location of the mobile device at the second time point is determined based on the candidate locations at the second time point.