Signal Source Localization Using Bearing and Residence Probability
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Solution Overview
Problem
Existing methods for localizing signal sources, such as electromagnetic, thermal, and acoustic waves, are inaccurate due to the reliance on passive signal evaluation without active signal emission, leading to inefficient and imprecise positioning.
Innovation Solution
A method that combines location-dependent bearing measurements with location-dependent residence probabilities, utilizing a-priori information like geographical data, signal type identification, and image processing to enhance accuracy and reduce computational requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If passive signal source localization is performed using only signal evaluation, then no active signal emission is required, but localization accuracy is very poor
Solution Approach 1:
The patent combines passive signal evaluation with active sensor systems (radar, lidar, ultrasonic sensors) to create a hybrid localization approach. The bearing measurements from active sensors are merged with a-priori information from databases to achieve accurate localization of passive signal sources, resolving the contradiction between maintaining passivity and achieving accuracy.
Solution Approach 2:
The patent introduces an intermediary processing system that receives bearing measurements from sensors and a-priori information from databases, then computes the final position. This intermediary layer integrates multiple data sources and resolves the contradiction by adding processing complexity to achieve the desired accuracy without requiring the signal source itself to be active.
2Measurement precision
If a-priori information is integrated with bearing measurements, then localization accuracy increases, but computation time increases
Solution Approach 1:
The patent performs preliminary action by pre-processing and storing a-priori information (terrain data, object characteristics, probability distributions) in databases before the actual localization occurs. During localization, this pre-prepared information is quickly retrieved and combined with bearing measurements, significantly reducing computation time while maintaining high accuracy.
Solution Approach 2:
The patent applies local quality by using location-dependent bearing measurements combined with locally relevant a-priori information (such as terrain-specific probability distributions and object characteristics). This targeted approach processes only the necessary local information rather than the entire dataset, reducing computation time while improving precision for the specific localization task.
3Measurement precision
If weak bearing measurements are amplified using a-priori information, then positioning accuracy improves, but the system becomes more complex
Solution Approach 1:
The patent applies parameter changes by transforming the weak bearing measurements through mathematical operations with a-priori information (such as probability distributions and weightings). The system changes the parameters of the measurements by combining them with terrain data and object characteristics, amplifying the accuracy without requiring fundamentally new hardware - thus managing complexity through software-based parameter transformation.
Data Source
AI summary
A method for localizing a signal source which emits a signal, including determining a location-dependent bearing measurement of the signal source, receiving a type identification of the signal source, ascertaining a location-dependent residence probability (lthreat({circumflex over (p)}), lshoot({circumflex over (p)}), lalt ({circumflex over (p)})) of the signal source depending on the received type identification of the signal source, superposing the location-dependent bearing measurement of the signal source with the location-dependent residence probability (lthreat({circumflex over (p)}), lshoot({circumflex over (p)}), lalt({circumflex over (p)})).