Indoor Positioning via IMM Kalman Filters for State Transition Accuracy
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Solution Overview
Problem
Existing indoor positioning systems face inaccuracies when tracking objects that transition between stationary and moving states, as traditional Kalman filter models are not adaptable to such changes, leading to poor tracking performance.
Innovation Solution
An electronic device with an indoor positioner and positioning engine server using an array antenna to calculate the angle of arrival of wireless signals, which feeds into an IMM module comprising multiple Kalman filters to adjust weighting values based on the object's status data, enabling smoother and more precise positioning by switching between stationary and moving state models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a traditional Kalman filter model is used for positioning, then the positioning can be smoother and more accurate for single-state objects, but it cannot adapt to objects that transition between stationary and moving states
Solution Approach 1:
The positioning system is segmented into multiple independent Kalman filter models, each specialized for a specific state (stationary or moving). The IMM module selectively activates appropriate models based on detected state transitions, allowing each model to optimize for its designated state while the system as a whole handles multiple states effectively.
Solution Approach 2:
The system dynamically switches between different Kalman filter models based on real-time state detection. The IMM module continuously monitors object state and adjusts which model is active, making the positioning system adaptive to changing conditions rather than relying on a fixed single-state model.
2Adaptability or versatility
If multiple Kalman filter models are used to handle state transitions, then adaptability improves, but system complexity increases
Solution Approach 1:
The IMM module serves as an intermediary that manages the complexity of multiple Kalman filter models. It handles model selection, switching, and coordination, allowing the complex multi-model system to operate seamlessly while presenting a unified interface for positioning operations.
Solution Approach 2:
The IMM module provides universal functionality by managing multiple specialized Kalman filter models through a single unified interface. This multi-functional component handles both stationary and moving state positioning, reducing the need for separate complex systems for each state.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The IMM module improves tracking accuracy and adaptability by iteratively updating probabilities and weighting values, providing more effective tracking of objects transitioning between stationary and moving states, thereby enhancing the precision and smoothness of positioning.
Implementation Method 1
calculate the angle of arrival (AOA) of the wireless signal according to the phase difference and the time difference
Implementation Method 2
calculate the angle of arrival (AOA) of the wireless signal according to the phase difference and the time difference
Data Source
AI summary
An electronic device includes an indoor positioner and a positioning engine server. The indoor positioner has an array antenna to receive a wireless signal from user equipment, and to calculate the angle of arrival (AOA) of the wireless signal according to the phase difference and the time difference. The wireless signal includes status data of the user equipment. The positioning engine server converts the angle of arrival into a set of coordinates that correspond to a position of the user equipment, and inputs the set of coordinates to an IMM module. The IMM module includes a first state module and a second state module. The IMM module calculates weighting values for the first state module and the second state module according to the status data of the user equipment, and outputs an estimated set of coordinates according to the set of coordinates and the weighting values.