Centralized Cooperative Positioning via Joint Time-Space Processing
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Solution Overview
Problem
Conventional positioning methods, such as GNSS, are impractical for high-mobility wireless networks due to cost and power consumption considerations, and suffer from reduced accuracy in harsh environments, while existing cooperative positioning algorithms like extended Kalman filter and belief propagation are limited by linear approximations and sensitivity to initial location errors.
Innovation Solution
A centralized cooperative positioning system using joint time-space processing, which caches multi-slice measurements and performs trajectory calculations to improve positioning accuracy through joint time-space processing, incorporating a multi-slice measurement cache module, trajectory calculation module, initial state estimation module, and joint time-space processing module to estimate node locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional positioning methods (GNSS) are used, then location information can be obtained, but cost and power consumption increase significantly
Solution Approach 1:
The patent implements cooperative positioning where nodes in the wireless network assist each other in determining locations. Instead of relying on external GNSS infrastructure, nodes use their own measurements and measurements from neighboring nodes to compute positions through centralized processing, making the system self-sufficient and eliminating the need for expensive GNSS receivers at each node.
2Measurement precision
If centralized cooperative positioning algorithms are applied to large networks, then positioning accuracy improves, but communication burden and computational complexity increase
Solution Approach 1:
The patent segments the positioning problem into manageable components: nodes are divided into reference nodes (with known positions) and user nodes (needing positioning); measurements are organized by time slices; and the processing is structured with dedicated modules for caching measurements, calculating trajectories, estimating initial states, and performing joint time-space processing. This segmentation reduces the overall computational burden while maintaining accuracy.
3Ease of operation
If existing cooperative positioning algorithms (extended Kalman filter, belief propagation) are used, then positioning can be performed, but positioning accuracy degrades in high-mobility environments with large initial location errors
Solution Approach 1:
The patent performs preliminary actions by caching measurements from multiple time slices before final positioning computation, calculating trajectory information in advance based on state variables, and estimating initial states before the main joint time-space processing. These preliminary steps prepare the data in a form that reduces sensitivity to initial location errors and improves accuracy in high-mobility scenarios.
Solution Approach 2:
The patent transitions from processing measurements in a single time slice to joint time-space processing that incorporates measurements across multiple time slices. This adds the time dimension to the positioning problem, allowing the system to exploit temporal correlations and trajectory constraints to improve accuracy when nodes are moving rapidly.
Data Source
AI summary
A centralized cooperative positioning method includes: caching measurements of nodes in multiple time slices up to the current time slice; calculating location information of the nodes in the multiple time slices to obtain trajectory of the nodes in the multiple time slices; performing initial state estimations of the nodes based on measurements of a single time slice; and using the initial state estimations as initial solution values, performing the joint time-space processing on the measurements of the nodes in the multiple time slices based on trajectory constraints, to obtain a state estimation of each node at the current time slice, in which the state estimation includes an estimated value of a location of the node.