LTE-IMU Indoor Localization with Carrier Phase and EKF
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Solution Overview
Problem
Current indoor localization methods face challenges in accurately determining position indoors due to signal attenuation and multipath propagation of GNSS signals, and existing solutions like Wi-Fi and cellular signals are limited by multipath errors and unknown clock biases of LTE base stations.
Innovation Solution
A framework that utilizes LTE-IMU systems, combining cellular long term evolution (LTE) signals with inertial measurement unit (IMU) data to estimate position, employing code and carrier phase-based receivers to correct for clock biases and multipath errors, and an extended Kalman filter to fuse data for precise indoor localization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If GNSS signals are used for outdoor localization, then positioning accuracy is sufficient for many applications, but signal attenuation makes them unusable indoors
Solution Approach 1:
The system transitions from using GNSS signals (designed for outdoor use) to exploiting cellular communication signals (designed for indoor penetration). By changing the signal type parameter and using signals optimized for indoor environments, the system achieves reliable indoor localization where GNSS fails due to attenuation.
2Use of energy by moving object
If Wi-Fi signals are used for indoor positioning, then received power is high, but geometric diversity of access points is poor limiting TOA-based approaches
Solution Approach 1:
The system uses cellular signals that serve dual purposes: they provide high received power like Wi-Fi for indoor penetration, and simultaneously offer favorable geometric diversity by construction of cellular infrastructure. This multi-functionality resolves the contradiction between signal strength and geometric diversity.
3Measurement precision
If cellular signals are used for accurate positioning, then geometric diversity is favorable, but unknown clock biases of eNodeBs must be addressed
Solution Approach 1:
The system introduces an intermediary base receiver that acts as a mediator between the navigator receiver and the cellular network. This base receiver measures pseudoranges to the same eNodeBs and transmits this data to the navigator, enabling the elimination of unknown clock biases through differential measurements without requiring direct access to base station clock information.
4Ease of operation
If code phase measurements are used with cellular signals, then positioning can be achieved, but multipath propagation introduces large errors
Solution Approach 1:
The system merges multiple measurement techniques (code phase and carrier phase measurements) and combines them with IMU data through an extended Kalman filter. This combination allows the system to achieve accurate positioning by compensating for multipath errors in code phase measurements using the complementary information from carrier phase measurements and inertial data.
Data Source
AI summary
Systems, device configurations and methods are provided for indoor localization for a navigator receiver based on broadband communication signals such as LTE. In one embodiment, an LTE-IMU framework determines receiver position indoors. Two different designs of LTE receivers are provided based on code phase and carrier phase determinations of the received signal. A base/navigator framework is presented to correct unknown clock biases of the LTE eNodeBs. In this framework, the base receiver is placed outdoors, has knowledge of its own position, and makes pseudorange measurements to eNodeBs in the environment whose positions are known. The base transmits these pseudoranges to the indoor navigating receiver, which is also making pseudorange measurements to the same eNodeBs. The navigating receiver differences the base and navigator pseudoranges. The navigator receiver is equipped with an extended Kalman filter (EKF) to fuse LTE and IMU measurements in a tightly-coupled fashion and estimate navigating receiver states.