MAC-Aware GNSS–RSSI Positioning for Dense Urban Areas
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Solution Overview
Problem
Existing positioning technologies struggle to achieve high-precision navigation and positioning in dense urban areas due to satellite signal blocking by tall buildings, and single RSSI methods fail to meet accuracy requirements, necessitating improved integration of GNSS and RSSI data.
Innovation Solution
A GNSS and RSSI integrated positioning method that classifies MAC addresses of transmitting nodes and selects appropriate RSSI positioning methods based on these categories, followed by weighted fusion of GNSS and RSSI results to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GNSS positioning is used in dense urban areas, then high-precision location information can be provided in open environments, but satellite signals are severely blocked by tall buildings, resulting in low or impossible positioning accuracy
Solution Approach 1:
The patent combines GNSS positioning with RSSI-based positioning to create an integrated positioning system. When GNSS signals are available, it uses GNSS positioning; when GNSS signals are blocked in dense urban areas, it switches to or supplements with RSSI positioning using deployed beacons. This merging allows the system to maintain positioning capability across different environmental conditions, resolving the contradiction between GNSS accuracy in open environments and signal blocking in dense urban areas.
2Measurement precision
If RSSI positioning is used in dense urban areas, then positioning can be achieved with deployed beacons, but a single RSSI positioning method has significant limitations and cannot meet positioning accuracy requirements
Solution Approach 1:
The patent dynamically selects different RSSI positioning methods (trilateration, two-point positioning, or proximity positioning) based on the MAC address category of the transmitting node. The system adapts the positioning method according to the specific characteristics and location of each beacon, rather than using a fixed single method. This dynamic adaptation allows the system to optimize positioning accuracy for different beacon configurations and environmental conditions in dense urban areas.
Solution Approach 2:
The patent applies different RSSI positioning methods to different MAC address categories, effectively treating different beacons with different positioning approaches based on their local characteristics. By classifying beacons into different categories (e.g., based on transmission power, location, or other attributes encoded in MAC addresses) and applying specialized positioning methods to each category, the system optimizes positioning accuracy for each local scenario rather than using a uniform approach.
3Measurement precision
If multiple RSSI positioning methods are applied with MAC address classification, then positioning accuracy is improved, but the system complexity increases
Solution Approach 1:
The patent segments the beacon population into different MAC address categories and applies specific positioning methods to each segment. By organizing beacons into distinct groups based on their characteristics (encoded in MAC addresses) and treating each group with its optimal positioning method, the system manages complexity through structured classification rather than attempting to handle all beacons uniformly. This segmentation approach makes the multi-method system more manageable and maintainable.
Data Source
AI summary
The present disclosure relates to the technical field of dense urban area positioning, and specifically discloses a GNSS and RSSI integrated positioning method considering MAC addresses. The method includes the following steps: arranging transmitting nodes and classifying MAC addresses of the transmitting nodes, collecting RSSI raw data; receiving RSSI data and MAC address data by the receiving nodes, and selecting one of the trilateration, two-point positioning, and proximity positioning for positioning based on the MAC address category; collecting GNSS data by the receiving nodes, and uses a differential positioning model to obtain GNSS positioning results; performing weighted fusion of GNSS and RSSI positioning results to ensure positioning accuracy in dense urban areas.