Kernel Density Estimation-Based Data Correlation

Calibration is the process of identifying and correcting the most likely error in sensor measurements. The basis for the authors´ calibration procedure is to construct a statistical error model that captures the characteristics of the measurement errors. Such an error model can be constructed either offline or online and is derived using the nonparametric kernel-density-estimation techniques. Models constructed using various forms of the kernel smoothing functions are compared using statistical evaluation methods. Based on the selected error model, they propose four alternatives to make the transition from the error model to the calibration model, which is represented by piecewise polynomials. In addition, statistical validation and evaluation methods such as resubstitution, is used in order to establish the interval of confidence for both the error model and the calibration model. Traces of the acoustic signal-based distance measurements recorded by infield deployed sensors are used as their demonstrative example. Finally, they discuss the broad range of applications of the error models and provide an example on how adopting statistical error model as the optimization objective impacts the accuracy of the location discovery problem in wireless sensor networks