Multi-Vehicle Signal Source Localization With Iterative Offset Search
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems and methods for passively locating a source of an electromagnetic signal, such as a radio signal, face challenges in accurately determining time, frequency, and Doppler offsets between sample streams from different vehicles, leading to high computing power consumption, long processing times, and inaccurate location estimation.
Innovation Solution
An iterative search process is employed where a maximum of two offsets are varied in each iteration, reducing search effort and computing resources, and the system of equations is manipulated to enhance convergence and accuracy, using techniques like multilateration and correlation functions to align sample streams.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing systems use conventional methods to determine time, frequency, and Doppler offsets between sample streams, then location estimation can be performed, but computing power consumption is high and processing time is long
Solution Approach 1:
The patent segments the offset determination process into distinct stages: time offset determination using cross-correlation, frequency offset determination using spectral analysis, and Doppler offset determination using rate of change analysis. This segmentation allows each parameter to be processed independently with optimized algorithms, reducing overall computing power consumption while maintaining location estimation accuracy.
Solution Approach 2:
The patent performs preliminary alignment and coarse estimation of offsets before final location calculation. By pre-determining time, frequency, and Doppler offsets using correlation and spectral methods, the system prepares the data in advance, reducing the computational burden during the actual location estimation process and decreasing overall processing time.
2Measurement precision
If existing systems use conventional offset determination methods, then location estimation can be achieved, but processing time is excessive
Solution Approach 1:
The patent replaces conventional mechanical search methods for offset determination with signal processing-based methods. Instead of brute-force time and frequency searches, the system uses cross-correlation functions and spectral analysis to directly compute offsets, dramatically reducing processing time while maintaining or improving accuracy.
Solution Approach 2:
The patent changes the approach from searching for offsets in the time-frequency domain to computing them directly through correlation and spectral parameters. By transforming the problem into the frequency domain using FFT-based methods, the system achieves faster computation with reduced processing time.
3Reliability
If existing systems attempt to determine multiple offsets simultaneously, then comprehensive alignment may be achieved, but computing resources are excessively consumed
Solution Approach 1:
The patent divides the complex task of determining multiple offsets into separate, independent determination steps. Time offset is determined first using cross-correlation, then frequency offset using spectral analysis, and finally Doppler offset using rate of change. This segmentation reduces computing resource requirements by avoiding the need to search all parameters simultaneously.
Solution Approach 2:
The patent performs preliminary determination of time and frequency offsets before final location estimation. By pre-computing these parameters using correlation and spectral methods, the system reduces the complexity of the final location calculation, maintaining reliability while decreasing overall computing resource consumption.
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
This approach reduces computing power consumption, equipment costs, and processing time while improving the accuracy and speed of signal source location estimation.
Implementation Method 1
comparing the first dataset and the second dataset using a correlation function, wherein the correlation function relates each of a plurality of candidate adjustment factors to a respective degree of correlation between (i) the first dataset and (ii) the second dataset as adjusted by the respective candidate adjustment factor
Data Source
Figure 1
Figure 2
Figure 3~4
AI summary
The application relates to a method of processing data for estimating a location of a source of an electromagnetic signal, said data comprising a plurality of datasets, each dataset being representative of the signal as received at a respective one of a plurality of vehicles. Also disclosed is a system comprising apparatus for carrying out the method, and a computer-readable medium storing a computer program for carrying out the method. The method includes: obtaining a first dataset of the plurality of datasets, the first dataset comprising successive data samples representative of the signal as received at a first one of the plurality of vehicles; obtaining a second dataset of the plurality of datasets, the second dataset comprising successive data samples representative of the signal as received at a second one of the plurality of vehicles; comparing the first dataset and the second dataset using a correlation function, wherein the correlation function relates each of a plurality of candidate adjustment factors to a respective degree of correlation between (i) the first dataset and (ii) the second dataset as adjusted by the respective candidate adjustment factor, wherein each candidate adjustment factor includes respective values for alignment of the datasets in terms of at least three differential measurements of the received signal; and selecting an adjustment factor from the plurality of candidate adjustment factors, wherein selecting the adjustment factor comprises searching for an adjustment factor that at least locally maximises the correlation function, and wherein the searching comprises multiple search iterations, and in each search iteration a maximum of two of the respective values are varied.