Sentence examples for sensor variances from inspiring English sources
However, as shown in Table2, DcPF has a slightly lower processing cost when the sensor variances are known.
To achieve this duality, we propose transformations which can be used to convert sensor failure probabilities into equivalent sensor variances and vice versa.
We assessed the performance of the proposed algorithms using 100 Monte Carlo runs with simulated data in three distinct scenarios assuming both unknown and known sensor variances.
As expected, the DcPF algorithm assuming known sensor variances has the same communication requirements as in the scenario with unknown variances since DcPF locally computes the likelihood functions and then broadcasts them to the entire network.
In the first scenario, we assumed unknown sensor variances and evaluated the performance of the Rao-Blackwellized ReDif-PF and two consensus-based PF trackers using respectively iterative minimum consensus (CbPFa) and flooding (CbPFb) (see also[11]).
Table1 summarizes the communication cost for each algorithm in the first scenario (unknown sensor variances) in terms of average transmission (TX) and average reception (RX) rates per node and also quantifies the processing cost for each algorithm in terms of average duty cycle per node, measured in a Intel Core i5 machine with 4GB RAM.
Therefore, an analytical evaluation of the sensor placement for the propagation of the sensor variance is provided here.
This variation was predominantly due to between-sensor variance, with the linear mixed model estimating the between-subjects variance component to be 37% of the total.
For fixed sensors, the biggest component of variance might be between sensor variability.
Performance comparison between the ReDif-PF and the ReDif-EKF algorithms assuming a non-informative prior in the second scenario with known sensor noise variances.
Performance comparison between the ReDif-PF and the selective gossip algorithms assuming a Gaussian prior distribution around the initial emitter state in the third scenario with known sensor noise variances.
