A mini-max robust estimation fusion in distributed multi-sensor target tracking systems
This paper proposed a mini-max fusion strategy in distributed multi-sensor system, which aims to minimize the worst-case squared estimation error when the cross-covariances between local sensors are unknown. The resulted estimation fusion is called as the Chebyshev fusion estimation (CFE) which is a...
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| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | |
| التنسيق: | Conference or Workshop Item |
| اللغة: | English |
| منشور في: |
2013
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://hdl.handle.net/10356/97810 http://hdl.handle.net/10220/12136 |
| الوسوم: |
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| الملخص: | This paper proposed a mini-max fusion strategy in distributed multi-sensor system, which aims to minimize the worst-case squared estimation error when the cross-covariances between local sensors are unknown. The resulted estimation fusion is called as the Chebyshev fusion estimation (CFE) which is actually a non-linear combination of local estimations. We have also proofed that the CFE is better than any local estimator in the sense of minimize the worst-case squared estimation error. Moreover, a sensitive analysis about the choice of the support bound is carried out. The simulations illustrate that the proposed CFE is a robust fusion and more accurate than the previous covariance intersection (CI) estimation fusion method. |
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