Multi-Radar Position Tracking Under Weak and Dropped Signals
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing tracking technologies face challenges in maintaining accuracy when measurement signals are weak, dropped, or spurious, particularly in conditions of timing jitter and unsynchronized sensors, leading to loss of tracking.
Innovation Solution
The use of two radar sensors arranged along perpendicular lines intersecting at the origin of a global coordinate space, with a processor implementing methods for predicting, transforming, and updating the object's state using unscented Kalman filters and Hungarian bipartite matching algorithms to enhance tracking accuracy and redundancy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single radar sensor is used for tracking, then the device complexity is low, but the tracking reliability deteriorates when the object is out of field of view or signals are weak/dropped
Solution Approach 1:
The tracking system is segmented into multiple independent radar sensors (at least two sensors) that operate in parallel to track the same object. Each sensor provides independent measurements, and the system processes measurements from multiple sensors to maintain tracking reliability when one sensor loses signal or the object moves out of its field of view.
Solution Approach 2:
The patent merges data from multiple radar sensors through a unified tracking algorithm that combines measurements from different sensors. The system integrates position, velocity, and other state information from multiple sources to produce a more reliable and accurate track, resolving the contradiction by combining multiple simple sensors to achieve high reliability.
2Reliability
If multiple radar sensors are deployed to improve field of view coverage, then the tracking reliability improves, but the computational cost increases
Solution Approach 1:
The system performs preliminary actions by maintaining a predicted state of the object using a prediction algorithm (such as a Kalman filter) before actual measurements are available. This allows the system to anticipate object position and velocity, reducing the computational burden of real-time processing and enabling the system to handle multiple sensors efficiently.
Solution Approach 2:
The tracking algorithm continuously updates the object state by combining predicted states with actual measurements from multiple sensors in a continuous manner. This continuous update process efficiently fuses data streams from multiple sensors without requiring computationally intensive reprocessing, maintaining reliability while managing computational costs.
3Measurement precision
If traditional tracking methods are used, then the system is simple to implement, but tracking accuracy deteriorates in challenging conditions such as weak signals, dropped signals, or spurious measurements
Solution Approach 1:
The tracking system implements feedback by continuously comparing predicted object states with actual measurements from radar sensors. The algorithm uses measurement residuals (differences between predicted and actual measurements) to update and refine the object state estimate, correcting for weak signals, dropped signals, and spurious measurements through this closed-loop feedback mechanism.
Solution Approach 2:
The system dynamically adjusts tracking parameters such as measurement weights, covariance matrices, and detection thresholds based on signal quality and measurement reliability. This allows the algorithm to adapt to challenging conditions by emphasizing reliable measurements and downweighting or discarding spurious ones, improving tracking accuracy without requiring overly complex hardware.
Data Source
Figure 1~2
Figure 3~4B
Figure 5~6
AI summary
A method of tracking an object is disclosed, including predicting a predicted state, in a global coordinate space, of an object based on a state of the object; determining in local coordinates the predicted state; determining a plurality of measurements of the object, in the local coordinates, with first and/or second radar sensors; determining a matching of the predicted state and the plurality of measurements, in the local coordinates, for a matching result; updating the state X of the object based on the matching result. The first and second sensors can be along perpendicular lines which intersect at the origin of the global coordinate space. Herein is disclosed a device including a plurality of sensors and a processor that is configured to execute the method.