Maximum Likelihood Code Phase Discriminator for TOA Estimation
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Solution Overview
Problem
Existing location determination methods for mobile devices, such as those using time of arrival (TOA) estimates of wireless signals, are imprecise, especially in urban environments, leading to potential delays in emergency responses and navigation inaccuracies.
Innovation Solution
A Maximum Likelihood code phase discriminator method that improves the accuracy of TOA estimation by efficiently converting Early, Prompt, and Late correlators into a code phase estimate, accounting for noise and distortions, and adjusting for timing drift and Doppler effects, using a limited number of correlators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional TOA estimation methods are used, then the system is simple to implement, but the location determination accuracy is insufficient
Solution Approach 1:
The patent segments the TOA estimation process into distinct stages: coarse estimation using initial correlators, followed by fine estimation using a limited set of refined correlators (e.g., 3-5 correlators) around the peak region. This segmentation allows the system to achieve high accuracy without requiring a large number of correlators across the entire search space, thus resolving the contradiction between measurement precision and device complexity.
Solution Approach 2:
The patent applies preliminary coarse estimation to identify the approximate TOA region before performing fine estimation. By first obtaining a rough estimate and then focusing computational resources on a limited window around this estimate, the system achieves high precision without the need for exhaustive search, thereby reducing overall computational complexity while maintaining accuracy.
2Measurement precision
If more correlators are used to improve TOA estimation accuracy, then the measurement precision improves, but the computational burden increases
Solution Approach 1:
The patent applies local quality by concentrating computational resources (correlator calculations) specifically in the critical region around the estimated TOA peak, rather than uniformly across all possible time delays. By using a limited number of correlators (e.g., 3-5) focused on the peak region with higher weighting, the system achieves high code phase estimation accuracy while minimizing overall computational power consumption.
Solution Approach 2:
The patent employs partial action by using only the necessary number of correlators (a limited subset) rather than computing all possible correlators. The method strategically selects and weights only those correlators that contribute most significantly to the TOA estimation, thereby achieving sufficient precision with reduced computational burden and power consumption.
3Adaptability or versatility
If the system adapts to environmental changes rapidly, then the reactivity improves, but the computational complexity increases
Solution Approach 1:
The patent implements dynamics by making the correlator set and weighting scheme adaptive rather than fixed. The system dynamically adjusts which correlators to compute and how to weight them based on the current signal environment and estimated TOA region. This dynamic adaptation allows rapid response to environmental changes while keeping the processing algorithm complexity manageable through intelligent resource allocation.
Data Source
AI summary
A method involves receiving a ranging signal. A filtered ranging signal is generated using the ranging signal and used to determine a first estimated time of arrival (TOA) of the ranging signal. Multiple first time delay hypotheses of an actual TOA of the ranging signal are determined. A correlator vector is generated using the filtered ranging signal, a filtered local replica of the ranging signal, and the first time delay hypotheses. Multiple code phase discriminator vectors corresponding to second time delay hypotheses are generated, each code phase discriminator vector being based on estimated signal processing, filtering, and noise characteristics of the ranging signal for a respective second time delay hypothesis. A second estimated TOA of the ranging signal is generated using the correlator vector and the code phase discriminator vectors.