https://github.com/research-and-project/mmwave_radar_tracking
object tracking based on millimeter wave radar
https://github.com/research-and-project/mmwave_radar_tracking
kalman-filter millimeter-wave mmwave mmwave-radar radar tracker tracking
Last synced: 6 months ago
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object tracking based on millimeter wave radar
- Host: GitHub
- URL: https://github.com/research-and-project/mmwave_radar_tracking
- Owner: Research-and-Project
- License: mit
- Created: 2021-07-29T04:09:32.000Z (about 4 years ago)
- Default Branch: main
- Last Pushed: 2024-12-19T01:30:23.000Z (10 months ago)
- Last Synced: 2025-03-29T11:08:19.685Z (6 months ago)
- Topics: kalman-filter, millimeter-wave, mmwave, mmwave-radar, radar, tracker, tracking
- Language: MATLAB
- Homepage:
- Size: 14.5 MB
- Stars: 121
- Watchers: 1
- Forks: 26
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# mmWave_radar_tracking
> by [dawnlh](https://github.com/dawnlh)
## Introduction
Object tracking based on millimeter wave radar data with Kalman Filter algorithm.
### Flow chart for the whole project
### Flow chart for the object tracking module
## Result demo
## Note
- In multiple object tracking, when objects have overlapping, mistakes may occur. This problem can perhaps be solved by using a more robust `detectionToTrackAssignment.m` function. Specifically, we can take other statistic features into account when calculating the cost in `detectionToTrackAssignment.m` function. Currently, only position and speed are considerer.
- The performance of the implemented algorithm is very dependent on parameter tuning, especially the parameters of DBSCAN and the tracking module (like parameters in `detectionToTrackAssignment.m` and `updateTrackStates.m`).
- Tips to improve the performance
- A more sophisticated **denoising algorithm.** Current `point_cloud_denoise.m` only removes the static points and out-of-range points, the "real" noise is not filtered. A better denoising algorithm may consider the spatial-temporal information between adjacent frames and adjacent regions.
- A more sophisticated **cluster algorithm**, which can automatically figure out different objects in tough cases like overlapping.
- A more sophisticated **tracking algorithm and strategy**, which take more information into consideration and realize better performance.
- Optimize the code to **lower the computation cost** and speed up the running speed.
## Reference
- P. Zhao *et al.*, “mID: Tracking and Identifying People with Millimeter Wave Radar,” in *2019 15th International Conference on Distributed Computing in Sensor Systems (DCOSS)*, Santorini Island, Greece, May 2019, pp. 33–40. doi: [10.1109/DCOSS.2019.00028](https://doi.org/10.1109/DCOSS.2019.00028).
- [MATLAB: Motion-Based Multiple Object Tracking](https://ww2.mathworks.cn/help/vision/ug/motion-based-multiple-object-tracking.html)
- [MATLAB: Use Kalman Filter for Object Tracking](https://ww2.mathworks.cn/help/vision/ug/using-kalman-filter-for-object-tracking.html)