Distributed Target Localization With Probability Maps
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Solution Overview
Problem
Current surveillance systems fail to address issues of universality, redundant distributed processing, likelihood of accuracy, maintenance, and optimality in calculating the real-time simultaneous locations of multiple targets, particularly in contexts involving both friend and foe targets, and are prone to single-point failures and data corruption.
Innovation Solution
A surveillance system with redundant distributed computing capabilities, moving sensor functionality, uncertainty quantification, and analytical optimization methods to calculate target positions using a network of moving detection units, employing error-bounding techniques and probability maps to ensure accuracy and reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single centralized processing subsystem is used to calculate target positions, then the system structure is simple, but the system is prone to single-point failures and data corruption
Solution Approach 1:
The patent divides the centralized processing subsystem into multiple distributed processing units, each capable of independently calculating target positions. This segmentation eliminates the single-point failure risk while maintaining computational functionality through distributed architecture.
Solution Approach 2:
Each processing unit in the distributed system performs specialized local calculations for target position determination using sensor data from its vicinity. This local processing capability improves reliability while the overall system structure remains manageable through modular design.
2Ease of manufacture
If fixed sensor positions are used for multilateration, then the system implementation is straightforward, but the system lacks adaptability to different operational contexts
Solution Approach 1:
The patent enables sensors to transition from fixed positions to mobile platforms, allowing the sensor array to dynamically reposition itself based on operational requirements. This dynamic capability provides context adaptability while maintaining implementation feasibility through standardized sensor modules.
Solution Approach 2:
The distributed sensor array is designed to function across multiple operational contexts (friend/foe target identification, aerial/ground surveillance, various environmental conditions) through universal sensor modules that can be deployed in diverse configurations without requiring context-specific customization.
3Device complexity
If position calculations are performed without uncertainty quantification, then the calculation process is simpler, but the accuracy and reliability of position reports cannot be guaranteed
Solution Approach 1:
The patent incorporates uncertainty quantification that provides feedback on the reliability of each position calculation. This feedback mechanism uses statistical analysis of multilateration results to generate confidence intervals, enabling the system to assess and report position accuracy without fundamentally changing the core calculation process.
Solution Approach 2:
The system performs preliminary statistical analysis and error bounding calculations before final position reporting. This preliminary action establishes confidence intervals and accuracy metrics in advance, ensuring measurement precision is guaranteed before results are presented to users.
4Productivity
If sensor data is processed without corruption detection, then the processing speed is faster, but the system cannot detect or prevent data corruption from affecting position calculations
Solution Approach 1:
The patent implements rapid corruption detection that skips detailed analysis of obviously valid data while quickly identifying and flagging corrupted signals. This approach maintains high processing speed for legitimate data while efficiently detecting corruption through statistical anomaly detection and consistency checks.
Solution Approach 2:
The system applies preliminary anti-action by implementing corruption detection algorithms that identify and isolate corrupted data before it can affect position calculations. This preventive measure uses consistency checks and statistical validation to block corrupted data streams, ensuring data integrity without significantly impacting processing throughput.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system provides real-time, accurate, and reliable position reports for multiple targets by quantifying accuracy, detecting data corruption, and preventing single-point failures, ensuring consistent performance across various contexts and environments.
Implementation Method 1
the arrival time of each target is based on an absolute timing schedule
Implementation Method 2
each theater detection unit has a central processing unit configured to (a) transmit and receive electromagnetic energy to/from each target
Data Source
AI summary
A surveillance system for the optimal calculation of the real-time simultaneous locations of multiple targets. The surveillance system uses the known locations in a given 2- or 3-dimensional frame of reference of possibly moving in a known path or statically located detection units for the simultaneous calculation in real-time of the locations of multiple targets that are either transmitting a coded identification signal at regular time intervals or are detected through the return of energy of unidentified targets. The calculated position for each target includes a point in the frame of reference most likely to be the actual position of the target based on the expected variability of the coded identification signals, along with a probability map, such as a 2-dimensional ellipse or 3-dimensional ellipsoid, separately for each target, presenting the likelihood of the actual position of the target being within a particular region of the frame of reference.