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This paper proposes an alternative finite memory structure (FMS) smoother with a recursive form under a least squares criterion using a forgetting factor strategy. The proposed FMS smoother does not require information of the noise covariances as well as the initial state. The proposed FMS smoother is shown to have good inherent properties such as time-invariance, unbiasedness, and deadbeat. The forgetting factor is shown to be considered as useful parameter to make the estimation performance of the proposed FMS smoother as good as possible. Through computer simulations for the F-404 engine system, it is shown that the proposed FMS smoother can be better than the existing FMS smoother for incorrect noise covariances and the IMS smoother for temporary uncertainties.

In this paper two stage biometric data protection scheme is being proposed using permutation and substitution mechanism of the chaotic theory which is lossless in nature. Arnold transformation and Henon map is used to design an efficient encryption system. The encryption method is aimed at generating an encrypted image that will have statistical properties completely dissimilar from the original image analysis which will make it difficult for any intruder to decrypt the image. The performance of the method has been experimentally analyzed using statistical analysis and correlation based methods. Correlation coefficient analysis is done to evaluate the behavior of pixels in horizontal and vertical directions and the results are found to be encouraging. This protection scheme provides the ability to encrypt the data and secure it from unauthorized users. Upon decryption the data is completely recovered making this scheme a lossless and efficient method of biometric data security.

In this paper, a finite memory structure (FMS) filtering with two kinds of measurement windows is proposed using the chi-square test statistic to cover nominal systems as well as temporarily uncertain systems. First, the simple matrix form for the FMS filter is developed from the conditional density of the current state given finite past measurements. Then, one of the two FMS filters, the primary FMS filter or the secondary FMS filter, with different measurement windows is operated selectively according to the presence or absence of uncertainty, to obtain a valid estimate. The primary FMS filter is selected for the nominal system and the secondary FMS filter is selected for the temporarily uncertain system, respectively. A declaration rule is defined to indicate the presence or absence of uncertainty, operate the suitable one from two filters, and then obtain the valid filtered estimate. A test variable for the declaration rule is developed using a chi-square test statistic from the estimation error and compared to a precomputed threshold. In order to verify the proposed selective FMS filtering and compare with the existing FMS filter and the infinite memory structure (IMS) filter, computer simulations are performed for a selection of dynamic systems including a F404 gas turbine aircraft engine and an electric motor. Simulation results confirm that the proposed selective FMS filtering works well for nominal systems as well as temporarily uncertain systems. In addition, the proposed selective FMS filtering is shown to be remarkably better than the IMS filtering for the temporarily uncertain system.

In this paper, a state estimation problem is considered for a target tracking scheme in wireless network environments. Firstly, a unified algorithm of finite memory structure (FMS) filtering and smoothing is proposed for a discrete-time state-space model. As shown in the terminology unified, both FMS filter and smoother are derived by solving one optimization problem directly with incorporation of the unbiasedness constraint. Hence, the unified algorithm provides simultaneously the current state estimate as well as the lagged state estimate using only finite measurements and inputs on the most recent window. The proposed unified algorithm of FMS filtering and smoothing shows that there are some unique properties such as unbiasedness, deadbeat, time-invariance and intrinsic robustness, which cannot be obtained by the recursive infinite memory structure (IMS) filtering such as Kalman filter. The on-line computational complexity of the proposed unified algorithm is discussed. Secondly, as an application of the proposed unified algorithm, a target tracking scheme in wireless network environments is considered via computer simulations for moving target’s accelerations of various shapes. The proposed unified algorithm-based target tracking scheme provides estimates for position as well as acceleration of moving target in real time, while eliminating unwanted noise effects and maintaining desired moving positions. Due to intrinsic robustness and deadbeat properties, the proposed unified algorithm-based scheme can outperform the existing IMS filtering-based scheme when acceleration suddenly changes.