By Wen-An Zhang, Bo Chen, Haiyu Song, Li Yu
This publication systematically offers energy-efficient strong fusion estimation tips on how to in achieving thorough and accomplished leads to the context of network-based fusion estimation. It summarizes contemporary findings on fusion estimation with communique constraints; numerous novel energy-efficient and powerful layout equipment for facing strength constraints and network-induced uncertainties are offered, similar to delays, packet losses, and asynchronous information... the entire effects are provided as algorithms, that are handy for useful applications.
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Additional info for Distributed Fusion Estimation for Sensor Networks with Communication Constraints
However, it is required in the algorithm in  that at least one estimator should generate its local estimates uniformly with a single rate, and all the other estimation rates are integer multiple of this single rate. In  and , some very general asynchronous fusion estimation methods were presented for multisensor systems, which allow for out of sequence data and latent data. However, the results in  and  were concerned with the sampled-data fusion estimation, where the estimators estimate states at some discrete instants for a continuous-time process.
It can be seen from Figs. 4 0 10 20 30 time / s 40 50 Fig. 9 Estimation performances of sensors in N1 with different PLPs, a D 2, b D 2. (a) Local estimation performances of sensor 1. (b) Local estimation performances of sensor 2. (c) Local estimation performances of sensor 5. 5 a=2, b=1 a=4, b=1 0 0 10 20 30 time / s 40 50 Fig. 10 Estimation performances of sensors in N1 with different measurement transmission periods, Â2;1 D Â5;1 D 0:8. (a) Local estimation performances of sensor 1. (b) Local estimation performances of sensor 2.
4. Note that the links in the WSN are subject to packet losses, local estimates Ol , l 2 Nr may be lost during the transmission, and thus only the estimates that successfully arrive at the sensor r are used to generate the fused estimate ÁO or of the system state Á. ti /. ti /. 1. 4. 2. ti /g. 57) ( Ql;lp ; l; j 2 Nu;s ; l D j and Nu;s D Nu \ Ns . , sensor u and sensor s are neighbors, one has u 2 Ns and s 2 Nu . 59) u;s The proof is thus completed. ti / « 32 2 Multi-rate Kalman Fusion Estimation for WSNs where u; s 2 Nr and u ¤ s.
Distributed Fusion Estimation for Sensor Networks with Communication Constraints by Wen-An Zhang, Bo Chen, Haiyu Song, Li Yu