Robust Kalman filter and smoother for errors-in-variables state space models with observation outliers based on the minimum-covariance determinant estimator

Liang

2014

An Interacting Multiple Model (IMM) filter suppresses outliers by integrating model‐conditioned estimation and model mode recognition through adaptive mode switches based on a Bayesian probability update. However, it may face significant outlier‐caused peak errors, which are undesirable or even impermissible in many applications, for example, target tracking. This paper considers that, from the view of the Dempster‐Shafer Theory, the IMM filter actually fuses the Bayesian beliefs of the model mode with Dempster's Rule of Combination, which can deal with uncertainties instead of the evidence conflicts that may exist as outliers appear. Therefore, we propose the adaptive robust multiple model (RMM) filter through introducing practical expert knowledge in the belief function framework, which runs online to reduce outlier‐caused peak errors. In RMM, the Likelihood Temporal Ratio (LTR) is incorporated to provide extra information on the tendency of mode switching. Moreover, expert rules are introduced to construct adaptive belief functions using discount techniques and to select the combination rule that better deals with evidence conflicts when outliers appear. The simulations in target tracking scenarios show that the proposed RMM filter significantly reduces outlier‐caused peak errors and hence obtains a lower rate of track loss in the cluttered environment.

Section: Dempster-shafer Theorymentioning

confidence: 99%

A Novel Robust MM Filter against Outliers

Liang

2014

“…, M) are the unknown error parameters to be identified. The standard form of the KF is given here [1,22,23]. The time update is formulated aŝ|…”

Section: Estimation Error Analysismentioning

confidence: 99%

“…The standard form of the KF is given here . The time update is formulated as

{\overset{x}{true}}_{k | k - 1} = {bold-italicF}_{k} {\overset{x}{true}}_{k - 1}

{bold-italicP}_{k | k - 1} = {bold-italicF}_{k} {bold-italicP}_{k - 1} {bold-italicF}_{k}^{T} + {bold-italicQ}_{k}

where

{\overset{x}{true}}_{k}

and

{\overset{x}{true}}_{k | k - 1}

are the state estimate and prediction at time k , respectively.…”

Section: Estimation Error Analysismentioning

confidence: 99%

Compensation for Periodic Star Sensor Measurement Error on Satellite

Xiong¹,

Liu²

2013

A novel method based on the least squares estimation and the augmented Kalman filter (KF) is proposed to identify and compensate for the star sensor measurement error due to the periodic star sensor alignment change. For the satellite attitude determination system, the periodic star sensor measurement error causes periodic variations in the gyroscope bias estimate. Thus, the gyroscope bias estimate is adopted as an indicator for the identification of the periodic star sensor measurement error. The high performance of the proposed method is illustrated through numerical simulations with the use of the real gyroscope data. It is shown that the proposed method is effective.

“…The Kalman filter is typically employed to predict system states for control purposes. Some examples can be found in . Tenn et al .…”

Section: Introductionmentioning

confidence: 99%

“…The Kalman filter is typically employed to predict system states for control purposes. Some examples can be found in [9][10][11]. Tenn et al also presented a method that estimates the attitudes for the rolling and pitching attitude control of an unmanned helicopter in hovering flight mode [12].The Kalman filter is also applied to vision-based tracking.…”

Section: Introductionmentioning

confidence: 99%

Vision‐Based Tracking and Position Estimation of Moving Targets for Unmanned Helicopter Systems

Lin

Hsiao

2013

The primary goal of this study is to track a ground-moving target using a machine-vision system installed on an unmanned helicopter, and to estimate its position if the target becomes unobservable. The machine-vision system is accomplished using real-time color images obtained from a charge-coupled device (CCD) camera mounted on a computer-controlled gimbaled system that can pitch and yaw. To avoid real-time image-tracking failure resulting from a moving target becoming concealed, the Kalman filtering technique is applied to predict the target's follow-on position, so that the camera can continuously track the target. The entire system is initially tested on the ground and then mounted on a helicopter for in-flight testing. The following three cases are shown in the flight tests: (1) an uncovered static target; (2) a moving visible target; and (3) a target that moves in a straight line at a constant speed and becomes temporarily concealed. The vision-based tracking system with the developed algorithm is successfully applied in all three cases.