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By Koushil Sreenath, M.F. Mysorewalla, Dan O. Popa, Frank L. Lewis
This informative textual content for graduate scholars, researchers and practitioners engaged on cellular instant sensor networks offers theoretical established algorithms with a spotlight in the direction of sensible implementation.
Read or Download Adaptive Sampling with Mobile WSN: Simultaneous robot localisation and mapping of paramagnetic spatio-temporal fields PDF
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Extra resources for Adaptive Sampling with Mobile WSN: Simultaneous robot localisation and mapping of paramagnetic spatio-temporal fields
Example text
3:11Þ field variable with measurement noise covariance Robot input measurement: ~ i ¼ U i þ bki U 1k 1k ð3:12Þ ~ i is the measured control input with measurement noise covariance where U 1k ~i . E½bki ðbki ÞT ¼ Ri . U i is the output caused by the control input U 3k 1k 1k Field evolution factor measurement: ~ 2k ¼ U 2k þ hk U ð3:13Þ ~ 2k is the measured field parameter evolution using high spatial and low rate where U measurement with measurement noise covariance E½hk hk T ¼ R4k . U2k is the actual change in the parameter value since the last estimate.
This result is then used as a test for identifying data that is statistically different from the ambient distribution. This information-theoretic approach has been applied to ocean sampling and prediction [13]. Another non-parametric approach is cognitive or rule-based in order to adaptively select sample locations in real time based on current and past samples to CH003 15 February 2011; 19:30:48 Adaptive sampling of parametric fields 31 ensure that a field source is localized. This scheme does not involve formation of any distribution, but it is used to find distribution maxima.
Error in b 2 –2 5 0 0 5 0 0 20 Sample no. 5 0 0 0 20 Sample no. Error in c 1 0 0 20 Sample no. 5 2 20 80 x (units) 0 2-Norm of error b 4 60 –1 0 20 Sample no. 500 1000 Distance (units) 10 2-Norm of error a 100 40 40 y (units) 20 80 x (units) 5 20 40 40 10 60 5 0 0 10 20 No. of samples 30 2-Norm of error covariance 20 0 80 0 0 60 y (units) 0 10 5 0 0 500 1000 Distance (units) 0 10 20 No. 1 Comparison of GAS and AS for number of samples and distance when error covariances of different parameters are minimized Minimizing error No.