Geo-location Accuracy via Squared Residual Minimization
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Solution Overview
Problem
Existing methods for geo-locating wireless devices using round trip time (RTT) and angle of arrival (AOA) measurements are susceptible to inaccuracies due to noise, multipath, and other environmental factors, especially at short or long distances.
Innovation Solution
A method that combines RTT and AOA measurements by calculating location vectors, determining location parameters, and generating squared residual vectors to identify best-fit location parameters for the target station, ultimately determining its geo-location using a circular error probability (CEP) ellipse.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If RTT and AOA measurements are used for geo-location, then location determination can be achieved, but accuracy deteriorates due to noise, multipath, and environmental factors
Solution Approach 1:
The patent combines RTT and AOA measurements into a unified geo-location system. Multiple location vectors are calculated from both RTT and AOA data, then merged through a squared residual minimization process to produce a single best-fit location estimate. This merging allows the system to leverage complementary information from both measurement types while compensating for their individual weaknesses through statistical optimization.
Solution Approach 2:
The patent transforms raw RTT and AOA measurements into location vectors with specific mathematical parameters. By calculating squared residuals and minimizing the sum of these residuals, the system changes the parameter representation from direct measurements to optimized location estimates. This parameter transformation enables the system to achieve better accuracy by finding the optimal fit rather than using raw measurements directly.
2Measurement precision
If RTT measurements are used for geo-location, then distance information can be obtained, but accuracy deteriorates at short and long distances
Solution Approach 1:
The patent merges RTT-based distance information with AOA-based directional information to create location vectors that are valid across all distance ranges. By combining these two measurement types, the system overcomes the distance limitations of RTT alone, as AOA provides complementary angular information that remains useful regardless of distance.
Solution Approach 2:
The patent creates a composite location estimation by combining RTT and AOA measurements into location vectors. This composite approach is analogous to composite materials, where two different measurement types are combined to create a more robust and versatile estimation system that performs well across varying conditions including different distance ranges.
3Measurement precision
If AOA measurements are used for geo-location, then directional information can be obtained, but accuracy deteriorates due to environmental factors
Solution Approach 1:
The patent merges AOA directional information with RTT distance information through the location vector framework. By combining these measurements and minimizing squared residuals, the system produces location estimates that are more reliable than AOA alone, as the RTT component provides independent verification and compensation for AOA measurement errors.
Solution Approach 2:
The patent implements a feedback mechanism through the squared residual minimization process. The system calculates location vectors from both RTT and AOA, computes the residuals between these vectors and the actual measurements, and uses this feedback to iteratively refine the location estimate. This feedback loop improves reliability by continuously adjusting the estimate to better match the actual measurements.
4Measurement precision
If multiple location vectors are calculated from RTT and AOA, then geo-location accuracy can be improved, but computational complexity increases
Solution Approach 1:
The patent changes the mathematical parameters by calculating squared residuals instead of directly combining location vectors. This parameter transformation simplifies the optimization problem to a standard least-squares minimization, which can be solved efficiently using well-established mathematical techniques. The squared residual formulation converts a complex multi-parameter optimization into a more tractable mathematical problem.
Data Source
AI summary
A method for determining a geo-location of a target station includes transmitting a plurality of ranging packets to a target station and receiving a plurality of response packets transmitted by the target station. A plurality of round-trip times (RTTs) are determined based on the ranging packets and the response packets. A plurality of angles of arrival (AOAs) are determined based on the response packets. First location vectors are determined based on the pluralities of RTTs and AOAs. Second location vectors are determined based on location parameters of the measuring station and the target station. Squared residual vectors are generated based on the first and second pluralities. A minimum of a sum of the squared residual vectors is calculated to identify best-fit location parameters for the target station. A circular error probability (CEP) ellipse is generated using the best-fit location parameters and a geo-location of the target station is determined.