Wireless Location Accuracy Estimation via Data Aggregation
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Solution Overview
Problem
Empirical testing for wireless location accuracy in mobile telecommunications is time-consuming and expensive due to the need for frequent and extensive placement of test calls across geographic regions to ensure compliance with E911 requirements and maintain accuracy in the face of changing conditions.
Innovation Solution
The method involves segmenting geographic regions into smaller areas based on land use characteristics, normalizing call data samples, and combining subscriber call data according to the identified ratio of wireless locating techniques to estimate location determination accuracy, thereby reducing the need for frequent empirical testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If empirical testing is performed throughout a geographic region to verify locating accuracy, then measurement precision is improved, but loss of time and productivity deteriorate due to the need to place thousands of test calls repeatedly
Solution Approach 1:
The system performs preliminary empirical testing to establish baseline locating accuracy characteristics for different geographic areas and locating techniques. These baseline measurements are stored and used to predict future accuracy without requiring repeated full-scale testing. This preliminary action captures the essential accuracy data needed to resolve the contradiction between measurement precision and time loss.
Solution Approach 2:
Instead of performing actual empirical testing repeatedly, the system creates and uses predictive models that copy the essential relationships between locating techniques, geographic areas, and accuracy outcomes. These models replicate the behavior of the physical testing system, allowing accuracy estimation without the time-consuming process of placing thousands of test calls.
2Measurement precision
If empirical testing is performed throughout a geographic region to verify locating accuracy, then measurement precision is improved, but productivity deteriorates due to the extensive number of test calls required
Solution Approach 1:
The system performs preliminary empirical testing to establish baseline locating accuracy characteristics for different geographic areas and locating techniques. These baseline measurements are stored and used to predict future accuracy without requiring repeated full-scale testing. This preliminary action captures the essential accuracy data needed to resolve the contradiction between measurement precision and time loss.
Solution Approach 2:
Instead of performing actual empirical testing repeatedly, the system creates and uses predictive models that copy the essential relationships between locating techniques, geographic areas, and accuracy outcomes. These models replicate the behavior of the physical testing system, allowing accuracy estimation without the time-consuming process of placing thousands of test calls.
3Reliability
If empirical testing is repeated frequently to ensure continued accuracy, then reliability is improved, but loss of time and cost increase
Solution Approach 1:
The system implements a feedback mechanism where actual locating accuracy data from subscriber calls is continuously collected and compared against predictive model estimates. When discrepancies exceed predetermined thresholds, the system triggers selective re-testing to update the predictive models. This feedback loop maintains reliability by ensuring models remain accurate while minimizing unnecessary repeated testing.
Solution Approach 2:
Instead of continuous or frequent empirical testing, the system employs periodic validation where predictive model estimates are compared against actual subscriber call data at predetermined intervals or when triggered by threshold exceedances. This periodic action maintains reliability while significantly reducing the frequency of full empirical testing campaigns.
4Measurement precision
If comprehensive empirical testing is performed across all locating techniques, then measurement precision is improved, but device complexity increases due to multiple locating techniques requiring different test protocols
Solution Approach 1:
The system implements a universal predictive modeling framework that can estimate accuracy for multiple different locating techniques (network-based, handset-based, hybrid) using a common set of geographic area characteristics and baseline measurements. This multi-functional approach allows the same system to handle diverse locating techniques without requiring separate complex testing protocols for each, thereby reducing overall system complexity while maintaining measurement precision.
Data Source
AI summary
Location determination accuracy in a mobile telecommunications environment can be estimated based on location determination accuracy data for multiple empirical test calls in a geographic region. A location of a wireless station for each test call is determined using one of multiple wireless locating techniques, and an approximate ratio of wireless locating techniques for the empirical test calls is identified. Subscriber call data relating to location estimates determined using one of the wireless locating techniques is received. Location determination accuracy in the geographic region is estimated by combining the subscriber call data according to the identified ratio. To facilitate determinations of locating accuracy, the geographic region is logically segmented into multiple areas. Each area is associated with a performance profile that relates to a locating accuracy performance. Location determination accuracy is estimated based on one or more areas associated with each wireless call in the geographic region.