IMM Tracking With Rest-State Models for Accurate 3D Positioning
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Solution Overview
Problem
Existing tracking technologies struggle to provide a robust and accurate estimate of the 3-D position of movable targets, particularly when they are in a rest state, leading to inaccuracies and difficulties in controlling user interfaces effectively.
Innovation Solution
Employing an Interacting Multi-Model (IMM) tracking algorithm that combines a movement state model and a rest state model to process depth data from sensors like radar, TOF, stereo cameras, or LIDAR, using unscented Kalman filters to predict and combine state vectors, and determine the likelihood of the target being in each state, thereby enhancing accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single tracking model is used, then the device complexity is reduced, but the measurement precision deteriorates when the target transitions between movement and rest states
Solution Approach 1:
The system dynamically switches between different tracking models (movement model and rest state model) based on the detected state of the target. The IMM algorithm adapts the tracking approach in real-time, selecting the appropriate model to match the current motion state, thereby maintaining high accuracy without requiring a permanently complex multi-model system.
Solution Approach 2:
The system changes the parameters of the tracking algorithm by switching between different state models. When the target is detected to be in motion, the movement model parameters are activated; when the target is stationary, the rest state model parameters are used. This parameter switching enables accurate tracking across different states without permanently increasing system complexity.
2Reliability
If multiple tracking models are combined, then the reliability of tracking improves, but the device complexity increases
Solution Approach 1:
The IMM algorithm provides a dynamic framework that manages multiple models through probabilistic switching. Rather than simultaneously processing all models with equal weight, the system dynamically adjusts the contribution of each model based on the likelihood of the target being in a particular state, achieving robust tracking while controlling computational complexity through adaptive model selection.
3Measurement precision
If depth data processing is enhanced, then the measurement precision improves, but the loss of time increases due to additional processing steps
Solution Approach 1:
The system dynamically adjusts the processing intensity based on the target state. During movement, the algorithm processes depth data with movement model assumptions; during rest states, it switches to rest state model processing which can be computationally more efficient. This dynamic adaptation maintains high precision while minimizing processing time by avoiding unnecessary computational steps.
Data Source
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AI summary
A method includes determining an estimate of a 3-D position (91) of a moveable target (80) using an interacting multi-model, IMM, tracking algorithm based on measurements of depth data, at least one model of the IMM tracking algorithm modeling a rest state (41) of the moveable target.