Multiple Radar Sensor Position Tracking With Predictive Matching
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Solution Overview
Problem
Existing tracking technologies face challenges in maintaining accuracy when measurement signals are weak, dropped, or spurious, particularly in challenging conditions, leading to potential loss of tracking and reduced field of view.
Innovation Solution
The use of multiple radar sensors arranged along perpendicular lines intersecting at the origin of a global coordinate space, combined with a predictive method involving unscented Kalman filtering and Hungarian bipartite matching, to determine and update the object's state based on sensor measurements, ensuring redundancy and improved field of view coverage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple radar sensors are used to improve tracking reliability, then tracking accuracy is improved, but device complexity increases
Solution Approach 1:
The system divides the tracking function across multiple independent radar sensors (first radar sensor and second radar sensor) positioned at different locations. Each sensor independently measures aspects of the object's motion, and the processor segments the processing by handling measurements from each sensor separately before integrating them to improve overall tracking reliability
Solution Approach 2:
The system merges measurements from multiple radar sensors by transforming them into a common coordinate system and combining them through coordinate transformations. The processor integrates data from both sensors to determine the object's state, achieving improved reliability through data fusion while managing complexity through systematic coordinate transformation methods
2Area of stationary object
If multiple sensors are used to improve field of view coverage, then field of view coverage is improved, but computational cost increases
Solution Approach 1:
The system addresses field of view coverage by adding spatial dimensionality through multiple sensors positioned at different locations and orientations. The first sensor covers one angular range while the second sensor covers another, together providing omnidirectional or enhanced multi-directional coverage without requiring a single complex sensor
3Duration of action of stationary object
If predictive filtering is used to maintain tracking during signal loss, then tracking continuity is improved, but measurement precision requirements increase
Solution Approach 1:
The system performs preliminary action by using the unscented Kalman filter to predict the object's state before actual measurements are available or when measurements are lost. The filter maintains continuous tracking by predicting position and velocity based on previous states, allowing the system to bridge gaps when sensor signals are weak or dropped
Solution Approach 2:
The unscented Kalman filter implements feedback by continuously comparing predicted states with actual measurements and adjusting the state estimation accordingly. When measurements are available, the filter corrects predictions; when measurements are lost, the predictions continue based on the object's motion model, maintaining tracking continuity through this feedback loop
Data Source
AI summary
In an embodiment, a method of tracking includes 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; and updating the state of the object based on the matching result. The first and second sensors are arranged along perpendicular lines which intersect at the origin of the global coordinate space.