Mathematical Problems in Engineering

Volume 2017 (2017), Article ID 8192053, 6 pages

https://doi.org/10.1155/2017/8192053

## A Finite Memory Structure Smoother with Recursive Form Using Forgetting Factor

Department of Electronic Engineering, Korea Polytechnic University, 237 Sangidaehak-Ro, Siheung-Si, Gyeonggi-Do 15073, Republic of Korea

Correspondence should be addressed to Pyung Soo Kim

Received 17 March 2017; Accepted 25 May 2017; Published 19 June 2017

Academic Editor: Alessandro Mauro

Copyright © 2017 Pyung Soo Kim. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

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.

#### 1. Introduction

Recently, to overcome the resulting problems of existing fixed-lag Kalman smoother with the infinite memory structure (IMS) in [1–5], the fixed-lag smoother with the finite memory structure (FMS) has been developed for state estimation in discrete time-invariant systems [6–9] and discrete time-varying systems [10]. This FMS smoother has been known to have some good properties such as unbiasedness and deadbeat, which cannot be obtained by the IMS smoother. Moreover, in contrast to the IMS smoother with the recursive structure that tends to accumulate the smoothing error with the progression of time, the FMS smoother is inherently bounded input/bounded output stable and more robust against temporarily uncertain model parameters and round-off errors due to the FMS as shown in [11–15].

However, existing FMS smoothers in [6–10] have some limitations. Firstly, information about noise covariances to obtain the optimal estimate should be also assumed to be exactly known as the IMS smoother, which may be somewhat restrictive. Secondly, the recursive form of the FMS smoother has not been developed. Thus, for a large system dimension or window length, the computation load may be burdensome in real-time computation.

In this paper, to overcome the resulting difficulties in applications of existing FMS smoother, an alternative FMS smoother with a recursive form is proposed under a least squares criterion using a forgetting factor strategy. The forgetting factor strategy has been well-known in estimation areas to give exponentially less weight to older error samples as shown in [16–18]. The proposed FMS smoother does not require a priori information about noises covariance as well as the initial state. The proposed FMS smoother has good inherent properties such as time-invariance, unbiasedness, and deadbeat. The proposed FMS smoother is represented in a recursive form as well as a matrix form. It is noted that the recursive form has not been developed by the existing FMS smoother in [6–10]. 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. Finally, extensive computer simulations are performed for the F-404 engine system. As would be expected, the existing FMS smoother with correct noise covariances outperforms the proposed FMS smoother, since the existing FMS smoother is an optimal state estimate under the correct noise covariances. However, in comparison with the existing FMS smoother with incorrect noise covariances, the proposed FMS smoother can be better. In addition, the proposed FMS smoother is shown to be better than the IMS smoother for temporary uncertainties.

This paper is organized as follows. In Section 2, the FMS estimation for state-space model is reviewed briefly. In Section 3, an alternative FMS smoother is proposed and its inherent properties are shown. In Section 4, extensive computer simulations are performed to verify the proposed FMS smoother. Finally, conclusions are presented in Section 5.

#### 2. Finite Memory Structure Estimation for State-Space Model

A discrete time state-space model is considered as follows: where is the state and is the measurement. The system noise and the measurement noise are zero-mean white Gaussian and mutually uncorrelated. The covariances of and are denoted by and , respectively.

In this paper, for the state estimation in the stochastic state-space models (1), the finite memory structure (FMS) is considered. The FMS estimation utilizes only the finite number of measurements on the most recent interval [] called the window and discards past measurements outside the window. This window of finite measurements recedes forward in time at each sampling time when a new measurement is available.

The finite measurements on the most recent window are denoted by as follows:and can be represented in the following regression form from the discrete time state-space model (1): where , have the same form as (2) and matrices , are as follows: Using finite measurements on the most recent window , FMS smoothers [6–10] as well as FMS filters [12–15] have been developed.

#### 3. Alternative FMS Smoother with Forgetting Factor

In this paper, the fixed-lagged system state at the time is considered. As shown in [6–10], the fixed-lagged system state means there is a fixed delay between the measurement and the availability of its estimate. The delay length is the positive integer with . This delay length means the number of discrete time steps between the lagged time when the state is to be estimated and the current time . To estimate fixed-lagged system state , an alternative FMS smoother is developed using finite measurements on the most recent window .

As shown in [6–10], the state at the lagged time is represented from the discrete time state-space model (1) as follows: where Therefore, finite measurements (3) can be modified bywhere

An alternative FMS smoother is developed under a least squares criterion using the forgetting factor strategy. Given measurements on the most recent window , the FMS smoother is obtained from the following forgetting factor least squares criterion:where is a diagonal matrix as where is called the forgetting factor. The forgetting factor strategy has been well-known in estimation areas to give exponentially less weight to older error samples as shown in [16–18]. A main role of the forgetting factor is to account for the fact that the state-space models (1) are not perfect to globally model the observed phenomenon and thus is to make the model that is locally well modeling the observations by concentrating on finite measurements on the most recent window . Then, when is observable and , the solution of (9) is given by the following matrix form:wherewhere is upper rows of and is lower rows of .

In the following theorem, the proposed FMS smoother (11) with a matrix form is represented in a recursive form.

Theorem 1. *When is observable and , the FMS smoother (11) can be represented in the following recursive form:where smoother gain matrices are defined by*

*Proof. *Using (12), at the lagged time , the FMS smoother (11) can be written byUsing (15), at the lagged time , the FMS smoother can be written by This completes the proof.

Note that this recursive form has not been developed by the existing FMS smoother in [6–10]. Smoother gain matrices , , and in (14) require offline computations only on the interval once and then they are time-invariant for all windows. Therefore, the proposed FMS smoother can be time-invariant.

In the following theorem, the proposed FMS smoothers in (11) and (13) are shown to have an unbiasedness property when there are noises and a deadbeat property when there are no noises. The unbiasedness property means that the mean value of tracks the mean value of the state at every time. The deadbeat property means that tracks exactly the state at every time.

Theorem 2. *When is observable and , the FMS smoother is unbiased for noisy systems and exact for noise-free systems.*

*Proof. *When there are noises on the window , since is zero-mean in (7), . Therefore, the following is obtained from (11): This completes the proof of the unbiasedness property.

When there are no noises on the window as and , the observation is determined by the current state as . Therefore, the following is obtained directly from the proof of the unbiasedness property: This completes the proof of the deadbeat property.

The deadbeat property in Theorem 2 means that the FMS smoother designed for the system (1) with noises provides exact state when, in actual, there are no noises. Note that the proposed FMS smoother can have the finite convergence time and the fast estimation ability due to the deadbeat property. Hence, the proposed FMS smoother can be useful in many engineering problems such as fault detection and diagnosis, maneuver detection, and target tracking, because these problems require fast estimation and detection of signals with unknown times of occurrence. These good inherent properties, time-invariance, unbiasedness, and deadbeat of the proposed FMS smoother cannot be obtained by the IMS smoother such as the Kalman smoother.

The important issue here is how to choose an appropriate forgetting factor that makes the residual performance as good as possible. When the window length is fixed, the forgetting factor should be chosen. Intuitively, a reasonable criterion for the choice of the forgetting factor should be how much information about the current state of the system the older data and the new data contain. If the newly coming data bring enough information about the current state, or the older data contain less information on the present data, the forgetting factor should be smaller. When the exact information about the noise covariances cannot be obtained but some rough information about the noises can be obtained, there are some choices of the forgetting factor. If the covariance of the system noise is smaller, the older data should contain more information on the current state. Therefore, the smaller is, the bigger forgetting factor should be. In comparison with the covariance of the system noise, if the covariance of the measurement noise is relatively bigger, should be bigger too. Intuitively, the above facts can be roughly explained as follows. When the covariance of the measurement noise is larger, more data should be used to suppress the influence of the noise by means of averaging the measurement data, which means that the forgetting factor should be larger. Thus, the forgetting factor is a continuous parameter to adjust finely the smoothing performance.

#### 4. Computer Simulations

Computer simulations are performed for the following discrete time F-404 engine system [19] with a fixed-lag as well as a uncertain model parameter to illustrate the validity of the proposed FMS smoother and to compare with both existing FMS smoother and IMS smootherwhere actual system and measurement noise covariances are taken as and . The delay length is set by .

First, to show how to choose the forgetting factor , the proposed FMS smoother is compared with the existing FMS smoother. To make a clearer comparison of estimation performances, Monte Carlo simulations of 100 runs are performed and in each simulation 600 sample points are generated. The estimation performance of smoothers can be evaluated by the mean of root-squared estimation error. It was mentioned in previous section that when the measurement noise covariance is relatively bigger than the system noise covariance , should be bigger too. It is easy to see that the above states seem right from simulation results in Table 1 when the window length is taken . These results can provide practical guidance on the choice of a forgetting factor . The simulation results in Table 2 show estimation performance of the proposed FMS smoother with and the existing FMS smoother. In comparison with the existing FMS smoother, the FMS smoother performs worse than when information about noise covariances is assumed to be exactly known. This may be the expected result since the existing FMS smoother in this case is optimal. However, when this assumption is not satisfied, the performance of the FMS smoother can be better than that of the existing FMS smoother.