Target Tracking Device Adaptive Motion Model Selection
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Solution Overview
Problem
Existing target tracking devices for vehicles require multiple filtering processes in parallel for one target, leading to an increased processing load and reduced estimation accuracy due to discrepancies between the motion model and actual target motion.
Innovation Solution
A target tracking device configured with a state quantity estimation unit, a model selection unit, and an estimation selection unit, which estimates state quantities for each target using a single selected motion model, thereby reducing processing load and improving estimation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple filtering units with different motion models are used in parallel to estimate state quantities, then estimation accuracy is improved, but processing load increases
Solution Approach 1:
The system dynamically switches between different motion models (constant velocity, constant acceleration, maneuvering models) based on the current target state and observation characteristics. Instead of running all filters in parallel, the appropriate model is selected adaptively, reducing computational complexity while maintaining estimation accuracy for different target behaviors
Solution Approach 2:
The system changes the motion model parameters (acceleration, velocity, position) based on the target's observed behavior patterns. By adjusting which model parameters are active based on target classification and observation quality, the system achieves accurate tracking without the computational burden of parallel processing multiple models
2Reliability
If multiple filtering processes are performed in parallel for one target, then robustness to motion model discrepancies is improved, but processing time increases
Solution Approach 1:
The system performs preliminary classification of target behavior patterns before selecting the filtering approach. By pre-categorizing targets into different motion types based on initial observations, the system prepares the appropriate motion model in advance, avoiding the need for parallel processing while ensuring the correct model is applied from the start
Solution Approach 2:
The system dynamically adapts the filtering process by switching between different motion models based on real-time target behavior analysis. This dynamic adaptation provides robustness against model discrepancies without requiring all models to run simultaneously, thus reducing processing time while maintaining reliability
Data Source
AI summary
In a target tracking device, a state quantity estimation unit is configured to, every time a preset repetition period of a processing cycle elapses, estimate, for each of the one or more targets, a current state quantity based on at least either observation information of the one or more targets observed by a sensor or past state quantities of the one or more targets. A model selection unit is configured to, for each of the one or more targets, select one motion model from a plurality of predefined motion models, based on at least either states of the one or more targets or a state of the vehicle. An estimation selection unit is configured to, for each of the one or more targets, cause the state quantity estimation unit to estimate the state quantity of the target with the one motion model selected by the model selection unit.