IoT Asset Tracking via TDOA and RSSI Fusion
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Solution Overview
Problem
Current systems for monitoring mobile assets in geographical areas face challenges in accurately determining the location and quality of geolocation data due to limitations in time-of-arrival (TOA) and received-signal-strength-indication (RSSI) measurements, particularly in scenarios where the number of receiving gateways is limited or when singularities occur, affecting the reliability of location tracking.
Innovation Solution
A monitoring system comprising network nodes equipped with IoT receivers, TOA modules, and processing engines that generate time-difference-of-arrival (TDOA) and RSSI data, which are then aggregated to produce a geolocation of mobile assets, with a quality assessment ensuring accuracy by comparing TDOA and supplemental geolocation generated from RSSI data, ensuring reliable location tracking even with limited gateway configurations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If TDOA geolocation is used based on TOA measurements from limited gateways, then location tracking is enabled, but measurement precision deteriorates due to singularities and limited gateway configurations
Solution Approach 1:
The patent combines TDOA geolocation results with supplemental geolocation results from RSSI measurements to produce a fused location estimate. This merging of multiple geolocation methods compensates for the limitations of TDOA alone when gateways are limited or singularities occur, thereby maintaining both reliability and precision.
Solution Approach 2:
The system implements a quality assessment mechanism that evaluates the reliability of TDOA geolocation results. When the quality assessment indicates low confidence (due to singularities or limited gateways), the system feedbacks this information and switches to or weights the supplemental RSSI-based geolocation more heavily, ensuring continuous reliable tracking.
2Measurement precision
If multiple geolocation methods are combined to improve precision, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent applies different geolocation methods locally based on their suitability for specific conditions. TDOA is used when gateway configurations are optimal, while supplemental RSSI-based methods are applied when TDOA quality is poor. This local adaptation improves precision without requiring all methods to run simultaneously, thus controlling complexity.
Solution Approach 2:
The system dynamically changes the weighting or selection of geolocation methods based on quality assessment parameters. When TDOA quality is high, it is weighted more heavily; when quality deteriorates, the system transitions to supplemental methods. This parameter-based adaptation allows precision improvement while managing system complexity through conditional logic.
Data Source
Figure 1A
Figure 1B~1D
Figure 2
AI summary
A network node for monitoring a mobile asset in a geographical area. The network node is among a number of network nodes disposed in the geographical area that are configured to receive an Internet-of-things (IoT) signal from the mobile asset, and send respective time stamps, each representing a time-of-arrival (TOA) of the IoT signal, to a network aggregation system to generate a time-difference-of-arrival (TDOA) from which a geolocation of the mobile asset is generated.