This paper presents a novel strategy of particle filtering for state estimation based on Generalized Gaussian distributions (GGDs). The proposed strategy is implemented with the Gaussian particle pilter (GPF), which has been proved to be a powerful approach for state estimation of nonlinear systems with high accuracy and low computational cost. In our investigations, the distribution which gives the complete statistical characterization of the given data is obtained by exponent parameter estimation for GGDs, which has been solved by many methods. Based on GGDs, an extension of GPF is proposed and the simulation results show that the extension of GPF has higher estimation accuracy and nearly equal computational cost compared with the GPF which is based on Gaussian distribution assumption.
This paper presents a Kalman filter based method for diagnosing both parametric and catastrophic faults in analog circuits. Two major innovations are presented, i.e., the Kalman filter based technique, which can significantly improve the efficiency of diagnosing a fault through an iterative structure, and the Shannon entropy to mitigate the influence of component tolerance. Both these concepts help to achieve higher performance and lower testing cost while maintaining the circuit.s functionality. Our simulations demonstrate that using the Kalman filter based technique leads to good results of fault detection and fault location of analog circuits. Meanwhile, the parasitics, as a result of enhancing accessibility by adding test points, are reduced to minimum, that is, the data used for diagnosis is directly obtained from the system primary output pins in our method. The simulations also show that decision boundaries among faulty circuits have small variations over a wide range of noise-immunity requirements. In addition, experimental results show that the proposed method is superior to the test method based on the subband decomposition combined with coherence function, arisen recently.