Iterative Target Location via Adaptive Windowing
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Solution Overview
Problem
Existing radio frequency locating systems face challenges in achieving high-resolution time-of-arrival (TOA) accuracy due to multipath interference and low signal-to-noise ratio (SNR) conditions, leading to erratic location estimates and errors in target location determination.
Innovation Solution
The implementation of an iterative and adaptive windowing function in multiple receiver systems that discards outlier receivers and recalculates target location estimates based on TOA error measurements, reducing the impact of multipath interference and noise by identifying and removing incorrect TOA data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional TOA measurement methods are used in multiple receiver systems, then target location can be determined, but measurement precision deteriorates due to multipath interference and low SNR conditions
Solution Approach 1:
The patent changes the evaluation parameter from simple TOA measurement to a quality metric that assesses waveform characteristics. By evaluating signal quality parameters such as correlation peak sharpness and energy distribution, the system identifies and excludes low-quality TOA measurements caused by multipath interference, thereby improving both measurement precision and reliability
Solution Approach 2:
The patent introduces an intermediary quality assessment mechanism between TOA measurement and location calculation. This intermediary layer evaluates the reliability of each TOA measurement using waveform analysis, and only high-quality measurements are passed to the location algorithm, preventing erroneous data from degrading overall system performance
2Quantity of substance
If all received TOA data is used for location calculation, then more data is available for computation, but measurement precision deteriorates due to inclusion of erroneous TOA measurements from multipath interference
Solution Approach 1:
The patent systematically discards low-quality TOA measurements identified through waveform quality assessment. By removing erroneous data points caused by multipath interference while retaining high-quality measurements, the system achieves more accurate location estimates. The process recovers useful information from the data set by selective filtration rather than uniform acceptance or rejection
3Measurement precision
If iterative recalculation with outlier removal is performed, then TOA accuracy improves to less than one-nanosecond, but device complexity increases
Solution Approach 1:
The patent segments the TOA measurement process into distinct quality assessment and calculation phases. By separating waveform quality evaluation from location computation, and by processing receivers in iterations rather than simultaneously, the system manages complexity while achieving high precision. Each iteration focuses on a subset of receivers, reducing the computational burden per step
Data Source
AI summary
An disclosed method includes receiving, from receivers, TOA data associated with location tag transmissions; determining a first set of the receivers based on the received TOA data; calculating a first tag location estimate for the first set of the receivers by applying a minimizing function to a first set of the TOA data corresponding to the first set of the receivers; determining a first data quality indicator (DQI) for the first tag location estimate; and when the first DQI for the first tag location estimate does not meet a threshold: determining, for the first set of the TOA data, impacts of respective delays on the minimizing function; determining, based on the impacts of the respective delays on the minimizing function, a second set the receivers different from the first set of the receivers; and calculating a second tag location estimate for the second set of the receivers.