Name
Title | ||
|---|---|---|
The multi-sensor PHD filter: Analytic implementation via Gaussian mixture and effective binary partition. |
Abstract | ||
|---|---|---|
An analytic suboptimum solution is given for the theoretically rigorous multi-sensor probability hypothesis density (MS-PHD) filter due to R. Mahler. Under linear Gaussian assumptions, the propagating formulas for the means, covariances and weights of the constituent Gaussian components of the posterior intensity are given. Furthermore, a method, named 'Effective Binary Partition (EBP)', is proposed for limiting the number of considered partitions to reduce the computational complexity. The EBP method makes it possible to implement the exact MS-PHD filter formulas. The computational complexity of EBP of the proposed two-sensor PHD filter is O(τ(l0)·Jkk-1 + 1), where π(l0) is a constant corresponding to the effective measurement number l0, Jkk-1 is the number of predicted targets. Finally, the validity of the proposed algorithm is demonstrated by numerical simulations. © 2013 ISIF ( Intl Society of Information Fusi. |
Year | DOI | Venue |
|---|---|---|
2013 | null | Information Fusion |
Keywords | Field | DocType |
computational modeling,clutter,time measurement,gaussian processes,computational complexity,ebp | Probability hypothesis density filter,Clutter,Gaussian,Artificial intelligence,Gaussian process,Partition (number theory),Limiting,Machine learning,Mathematics,Computational complexity theory,Binary number | Conference |
Volume | Issue | ISBN |
null | null | 978-605-86311-1-3 |
Citations | PageRank | References |
4 | 0.49 | 4 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jian Xu | 1 | 6 | 0.87 |
| Fang-ming Huang | 2 | 4 | 0.49 |
| Zhi-Liang Huang | 3 | 4 | 0.49 |