Adaptive probability hypothesis density filter for multi-target tracking with unknown measurement noise statistics
Under the Gaussian noise assumption, the probability hypothesis density (PHD) filter represents a promising tool for tracking a group of moving targets with a time-varying number. However, inaccurate prior statistics of the random noise will degrade the performance of the PHD filter in many practica...
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2021-03-01
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| Series: | Measurement + Control |
| Online Access: | https://doi.org/10.1177/0020294021992800 |