Transmitter Location Using Selective TDOA Measurement Vectors
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Solution Overview
Problem
Existing radio transmitter localization methods using TDOA measurements are limited by the Torrieri algorithm, which only exploits N-1 TDOA measurements and may not be optimal due to irrelevant station pair combinations, leading to suboptimal localization results.
Innovation Solution
The method calculates N(N-1)/2 TDOA measurements and selects a number of these measurements to form vectors, using a quadratic criterion minimization with a covariance matrix to estimate the transmitter's location, iteratively refining the position to achieve accurate localization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If the Torrieri algorithm uses only N-1 TDOA measurements with systematic station numbering, then the localization process is simplified, but the localization accuracy deteriorates due to not exploiting all available information
Solution Approach 1:
The patent applies partial action by selectively choosing a subset of P TDOA measurements from the total N(N-1)/2 available measurements, rather than using all measurements or only N-1 measurements as in prior art. This selective approach balances computational complexity with localization accuracy by using enough measurements to improve precision while avoiding the excessive complexity of using all possible combinations.
2Ease of operation
If a unique combination of N-1 station pairs is used for TDOA measurements, then the calculation is more manageable, but the localization reliability deteriorates because the choice may not be relevant depending on geometry and signal levels
Solution Approach 1:
The patent implements dynamics by making the selection of TDOA measurements adaptive rather than static. The selection process considers geometric relationships between stations, signal levels, and dating errors, allowing the system to dynamically choose the most relevant P measurements from N(N-1)/2 available pairs based on current operating conditions, thus improving reliability while maintaining manageable complexity.
3Measurement precision
If all N(N-1)/2 TDOA measurements are calculated and used, then the localization accuracy improves by exploiting all available information, but the computational complexity increases
Solution Approach 1:
The patent applies partial action by using a selected subset P of TDOA measurements rather than all N(N-1)/2 measurements. This selective approach achieves improved localization accuracy compared to using only N-1 measurements while avoiding the excessive computational complexity of processing all possible measurement combinations.
Solution Approach 2:
The patent extracts the most relevant P TDOA measurements from the complete set of N(N-1)/2 available measurements based on criteria such as geometric configuration, signal strength, and dating error characteristics. This extraction process isolates the valuable information needed for accurate localization while discarding redundant or less useful measurements, thereby reducing computational complexity.
Data Source
Figure 1~2
AI summary
The invention relates to a method and a system for locating a transmitting device (20i) from N stations (10j), using at least one common timing reference for the N stations, comprising at least the following steps: • triggering one or more synchronized acquisitions on N stations for a given frequency, bandwidth, and time t, said values being chosen according to a transmitting device of interest, for an acquisition, at the level of a master station (10): • calculating the N(N-1)/2 arrival time delay measurements between all the N(N-1)/2 pairs of stations, • choosing a number P of measurements from the M=N(N-1)/2 measurements, and forming K measurement vectors associated with the K possible combinations, • for each of the K vectors, estimating an elementary location value of the transmitter by minimizing a given quadratic criterion and by iteration from a first value of an initial position vector X0,The position is located as close as possible to the minimum sought, and by successively estimating locations that allow the criterion to be minimized to decrease to a threshold value, • calculate a merged location value from the estimated elementary location values.