Affine Invariant Data Fusion for Heterogeneous Sensors
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current data fusion technologies are inadequate for fusing heterogeneous sensors in real-time, especially in complex interference environments, and lack the ability to emulate human-like sensor fusion for autonomous detection and tracking of targets across various domains.
Innovation Solution
A nonparametric method for data fusion using affine invariant transformations to elevate lower-dimensional sensor data into a higher-dimensional fused data operator, enabling efficient processing and exploitation in distributed and layered sensing systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If post-detection data fusion is used with federated algorithms, then data from multiple sensors can be processed, but the system cannot achieve real-time autonomous detection and the processing latency is high
Solution Approach 1:
The patent performs data fusion at the pre-detection stage rather than post-detection, transforming and fusing raw sensor data before detection algorithms are applied. This preliminary fusion eliminates the need for separate post-processing steps, thereby reducing overall processing latency and enabling real-time autonomous detection.
Solution Approach 2:
The patent segments the data fusion process into distinct transformation stages using affine invariant transformations, where each sensor's data is independently transformed into a common feature space before fusion. This segmentation allows for efficient parallel processing of multiple sensor inputs, improving productivity while maintaining low latency.
2Adaptability or versatility
If heterogeneous sensors are fused using traditional methods, then multiple sensor types can be integrated, but the system cannot emulate human-like sensor fusion for autonomous target detection
Solution Approach 1:
The patent creates a universal fusion framework using affine invariant transformations that can process data from any type of sensor (acoustic, electromagnetic, chemical, tactile) by transforming all inputs into a common mathematical space. This universality enables the system to emulate human-like multi-sensor integration without requiring separate processing paths for each sensor type, managing complexity through a unified approach.
Solution Approach 2:
The patent changes the parameter space of heterogeneous sensor data by applying affine transformations that map diverse sensor measurements into a standardized feature space with consistent dimensional properties. This parameter transformation allows the system to handle heterogeneous inputs uniformly, achieving adaptability while controlling complexity through mathematical standardization.
3Reliability
If lower-dimensional sensor data is processed directly, then computational efficiency is maintained, but the system cannot achieve robust target detection in complex interference environments
Solution Approach 1:
The patent transforms lower-dimensional sensor data into higher-dimensional feature spaces using affine invariant transformations. This dimensional elevation creates additional degrees of freedom in the data representation, allowing the system to separate target signals from interference more effectively and achieve robust detection without excessive processing complexity.
4Adaptability or versatility
If distributed and layered sensors are deployed for multi-domain detection, then coverage and detection capability are improved, but the data fusion becomes computationally intractable
Solution Approach 1:
The patent applies affine invariant transformations that change the parameter representation of sensor data from diverse domains into a unified mathematical space. This parameter transformation reduces computational complexity by eliminating the need for domain-specific processing, allowing efficient fusion of distributed sensors across multiple domains while maintaining adaptability.
Data Source
AI summary
Various deficiencies in the prior art are addressed by systems, methods, architectures, mechanisms and/or apparatus configured to fuse data received from a plurality of sensor sources on a network. The fusing data includes forming an empirical distribution for each of the sensor sources, reformatting the data from each of the sensor sources into pre-rotational alpha-trimmed depth regions, applying an affine transformation rotation to each of the reformatted data to form post-rotational pre-rotational alpha-trimmed depth regions, and reformatting each affine transformation into a new data fusion operator.