Inertial Sensor Location Estimation in Wireless Systems
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Solution Overview
Problem
Current location estimation methods in wireless communication systems, particularly in interior environments, face challenges with low accuracy and high complexity, especially in environments with weak GPS signals, where fingerprint-based schemes require extensive manpower and data transmission, while parametric approaches offer lower accuracy.
Innovation Solution
The proposed method utilizes inertial sensors, such as acceleration and angular velocity sensors, to estimate location by detecting movement units and interpolating between scan periods, reducing the complexity of the training phase and enhancing accuracy through reliability testing and sensor-based refinement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fingerprint-based interior location estimating scheme is used, then location estimation accuracy is improved, but training phase complexity and data transmission requirements increase
Solution Approach 1:
The patent extracts and utilizes inertial sensor data (acceleration, angular velocity) as a separate information source independent from the fingerprint database. This extracted sensor information is integrated with WLAN measurements to estimate location, thereby reducing dependence on the complex fingerprint training phase while maintaining accuracy in interior environments.
Solution Approach 2:
The patent combines multiple information sources (WLAN RSSI measurements, inertial sensor data, and fingerprint database information) into a composite location estimation approach. This hybrid method leverages the strengths of each source to achieve accurate positioning without requiring exhaustive fingerprint training.
2Measurement precision
If fingerprint-based interior location estimating scheme is used, then location estimation accuracy is improved, but data transmission requirements increase
Solution Approach 1:
The patent extracts location information from inertial sensors locally at the mobile terminal without requiring transmission of extensive fingerprint database data. This extraction approach significantly reduces the volume of data that needs to be transmitted between the server and mobile terminal while maintaining positioning accuracy.
Solution Approach 2:
The mobile terminal uses its own inertial sensors to self-determine movement information and integrates this with WLAN measurements locally. This self-service approach reduces reliance on server-side data transmission, thereby decreasing the quantity of data that must be transmitted over the network.
3Device complexity
If parametric interior location estimating scheme is used, then training phase complexity is reduced, but location estimation accuracy decreases
Solution Approach 1:
The patent creates a composite estimation method that combines parametric approaches (using inertial sensors and path loss models) with selective fingerprint information. This composite approach maintains low training complexity while improving accuracy by integrating multiple information sources rather than relying solely on simple parametric models.
Solution Approach 2:
The patent changes the parameters used for location estimation by incorporating inertial sensor measurements (acceleration, angular velocity) alongside traditional WLAN RSSI measurements. This parameter expansion allows the system to achieve better accuracy without requiring the extensive training data needed by conventional fingerprint methods.
Data Source
AI summary
Apparatuses, systems, and methods for estimating a location of a communication device in an internal environment are described. In one method, a number of movement units made while a mobile terminal moves from a first location to a second location is detected and the location of the mobile terminal is estimated based on the number of detected movement units and a location interpolation scheme.