Identification of step response estimates utilizing continuous time subspace approach

Direct identification procedures using raw data seem to face difficulties especially when the data is corrupted with noise or the data acquisition leads to huge amount of data to be processed. This will lead to complexity in obtaining the accurate model of the system and the increase of computational load and time may also arise. In this paper, we present 2-stage identification, in which, the first stage involves a process to obtain step response estimates. A multi input multi output frequency sampling filter model is used to simulate the estimates. With the aid of finite impulse response model, maximum likelihood method and the predicted sum of square statistics, this procedure able to clean the noise that occurred at high frequency region, compressed the data into the reduced amount and obtained only meaningful parameter that describes the system. Next, at the second stage the continuous time subspace model identification is conducted using the step response estimates obtained from the first stage. Here, three continuous time subspace methods will be observed to develop a state space mathematical model; those are the MOESP, CCA and ORT methods. A Monte Carlo simulation is performed as to see the efficacy and robustness of those models in identifying the step response estimates of the observed system. Comparative analysis with respect to two-stage identification and direct identification procedure is also conducted. This is to show the significant contribution of having MIMO FSF in the overall identification procedure. From results, the developed MIMO FSF is able to compress raw MIMO data into fewer numbers, and produce cleaned and unbiased step response estimates. When it is implemented to MIMO continuous-time subspace identification, MOESP method has demonstrated good performance based on the accuracy and robustness of the developed model.     

Modeling of an Enhanced Rain Attenuation Prediction for Equatorial Region

Presented in this paper is an empirical model for long-term rain attenuation prediction on slant path. The slant path prediction model is derived using data collected from tropical region and the formula proposed is based on Gaussian distribution. The proposed model shows a significant improvement over the existing prediction models with considerable reduction in RMS error as compared with other testing models.