Selective RF Sensor Fusion for Localization Accuracy
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Solution Overview
Problem
Current RF localization systems in outdoor environments face challenges in efficiently processing large-scale data using machine learning and deep learning, requiring extensive training and being computationally prohibitive, while also needing to selectively apply the most appropriate localization type based on environmental conditions to reduce processing power and latency.
Innovation Solution
Implement a smart fusion of multiple measurement types (TDOA, AOA, RSSI, and fingerprinting) within specific regions, using machine learning and deep learning only where necessary, and applying weights optimized for each region to combine scores from sensors, with a system that selectively applies machine learning to reduce complexity and latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning and deep learning are applied on a large scale to RF localization systems, then localization accuracy can be improved, but computational complexity and processing power requirements increase significantly
Solution Approach 1:
The patent divides the deployment area into multiple regions and applies different localization strategies to each region. Machine learning and deep learning are applied selectively only in regions where they provide significant performance benefits, rather than uniformly across the entire area. This segmentation reduces overall computational complexity while maintaining accuracy where needed.
Solution Approach 2:
Different localization techniques are applied to different regions based on their specific characteristics. The system adapts the complexity of the localization algorithm to match the local requirements of each region, using simpler methods in areas where they suffice and more complex ML/DL methods only where necessary. This local quality approach optimizes the balance between accuracy and computational burden.
2Measurement precision
If machine learning and deep learning are applied to process sensor data, then localization accuracy improves, but processing time and latency increase
Solution Approach 1:
The system segments the processing workload by applying ML/DL only in specific regions and time periods where they are most beneficial. By limiting the scope of complex processing to essential areas, the overall processing time and latency are reduced while maintaining high accuracy where it matters most.
Solution Approach 2:
The patent applies machine learning and deep learning partially rather than completely across all sensor data processing. By using these computationally intensive methods only in specific regions and conditions where they provide the most value, the system achieves the necessary accuracy without the full computational overhead, thereby reducing processing latency.
3Measurement precision
If fingerprinting is applied across the entire deployment area, then localization accuracy improves in challenging environments, but system complexity and cost increase
Solution Approach 1:
Fingerprinting is applied selectively only in specific regions where it provides significant performance improvement, rather than uniformly across the entire deployment area. The system identifies regions where traditional localization methods struggle and applies fingerprinting only there, reducing system complexity and cost while maintaining accuracy in challenging environments.
Solution Approach 2:
The system adapts the localization approach to local conditions by applying fingerprinting only in regions where environmental characteristics make it necessary. In regions with challenging multipath effects or where traditional methods fail, fingerprinting provides the needed accuracy. In other regions, simpler and less costly methods are used, optimizing the overall system complexity-cost-performance tradeoff.
Data Source
AI summary
A method and system for selectively applying machine learning to data fusion of a plurality of localization and classification of radio frequency (RF) emitters in an area are provided herein. The system may include: a sensor array of radio frequency sensors deployed outdoors, wherein each sensor is configured to perform synchronized sensor measurements of at least three types; at least one computer processor in communication with the sensor array and configured to apply a data fusion algorithm to the sensor measurements, to yield localization and classification data of the RF emitters; a machine learning module configured to obtain over a training period, localization data collected from at least the localization measurement of the three types of sensor measurements; train a model of to provide outputs of the localization measurement of the first type based on readings of the localization measurement of at least one of the two other types.