Likelihood Map for Emitter Localization
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Solution Overview
Problem
Existing methods for locating signal emitters in indoor wireless local area networks face accuracy issues due to measurement uncertainties and noise, making it difficult to determine the precise location using methods like Time Difference of Arrival, Time of Arrival, Angle of Arrival, and Received Signal Strength, especially with limited sensor accuracy and data presentation challenges.
Innovation Solution
A system utilizing a plurality of receivers to generate receiver signals and process them into likelihood maps, which represent the probability of the emitter's location as a function of position, using signal magnitude ratios and error functions to create a combined likelihood function that peaks at the emitter's location, overcoming measurement errors and noise by blurring the signal strength data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional emitter location methods (TDOA, TOA, AOA, RSS) are used with distributed sensors, then the location can be determined, but measurement uncertainties and noise reduce the accuracy
Solution Approach 1:
The patent transforms the location problem from direct measurement to probability density estimation by changing the parameter space from raw measurements to likelihood functions. The likelihood map represents the probability density of emitter locations, transforming uncertain measurements into a probabilistic representation that can be visually interpreted and processed.
Solution Approach 2:
The patent creates a visual copy of the measurement data in the form of a likelihood map, which is a two-dimensional representation of the probability density function. This visual copy allows for intuitive analysis of location uncertainty and can be displayed to users for interpretation and decision-making.
2Measurement precision
If multiple sensors are used to increase accuracy, then location precision improves, but the complexity of data analysis and presentation increases
Solution Approach 1:
The patent merges the data from multiple sensors into a single unified likelihood map that represents the combined information. Instead of analyzing individual sensor measurements separately, the system combines all sensor data into one probabilistic representation, simplifying the analysis while maintaining the benefits of multiple sensors.
Solution Approach 2:
The patent creates a visual copy of the complex multi-sensor data in the form of a two-dimensional likelihood map. This visual representation simplifies the presentation and analysis of data from multiple sensors, making it easier to interpret coverage and interference issues without requiring complex computational analysis.
3Ease of manufacture
If existing equipment is used for emitter location, then the system can operate with available technology, but the ability to easily view and analyze relevant data is limited
Solution Approach 1:
The patent creates a visual copy of the measurement data in the form of a likelihood map that can be displayed on standard screens. This visual representation makes the data easily viewable and analyzable by users, transforming complex measurement data into an intuitive graphical format that can be interpreted without specialized equipment or complex analysis tools.
Data Source
AI summary
A system and a method for displaying an emitter location are disclosed. The system includes a plurality of receivers at different locations in a field. Each receiver generates a receiver signal that depends on the magnitude of a signal from the emitter. The system also includes a processor that receives the receiver signal and generates a likelihood map indicative of an approximation of a probability as a function of position in the field of the emitter location. The likelihood map includes a plurality of receiver maps. Each receiver map includes a probability as a function of position in the field of the emitter location based on the signal magnitude for at least one of the receiver signals. Each receiver map may depend on a ratio of the signal magnitudes from a corresponding pair of the receivers, or on one of the signal magnitudes from a corresponding one of the receivers.