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Preview of https://doi.org/10.4 6620/21-0068
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Summary

This article presents a processing chain for multitarget tracking in an indoor scenario using a Texas Instruments IWR6843 radar development kit. The chain works on top of a radar point cloud and detects and tracks people using a clustering algorithm and Kalman filtering. The detected and postprocessed data are then fused onto a live image acquisition from a surveillance camera as a complementary sensor. The processing chain consists of four main steps: clustering, filtering, tracking, and sensor fusion. The clustering method used is density-based spatial clustering of applications with noise (DBSCAN), which identifies groups of points in the spatial domain. The filtering step removes clutter originating from multipath reflections, and the tracking step uses a Kalman filter to predict the state vector of the targets. The state transition matrix and observation matrix are used to describe the dynamics of the system, and the extended Kalman filter approximates these functions into a linear approximation. The algorithm predicts every existent track, compares the results to the measured cluster centroid, and associates the closest distance between the centroid and the prediction.

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Description

This article presents a processing chain for multitarget tracking in an indoor scenario using a Texas Instruments IWR6843 radar development kit.

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  • File Format: PDF
  • File Size: 307 KB
  • Pages: 4
  • Language: EN
  • Total Downloads: 165
  • Last Updated: 2 hours ago

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