Mitigating the effects of residual biases with Schmidt-Kalman filtering

Novoselov, Roman; Herman, Shawn M.; Gadaleta, Sabino; Poore, Aubrey B.

doi:10.1109/icif.2005.1591877

Cited by 33 publications

(26 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, v o [k] is assumed to be zero. This is in correspondence with the case described in [28]. The main difference between the structure in this case and [29] is the fact that the covariance increases at each time update until a correction is available in [29].…”

Section: Tracking System Based On a Schmidt-kalman Filtersupporting

confidence: 60%

“…The equations for the SchmidtKalman filter is described thoroughly in [28] and will not be explained further here. It is important, however, to point out that you don't want to estimate or predict the state δx o , but rather account for the uncertainty.…”

Section: Tracking System Based On a Schmidt-kalman Filtermentioning

confidence: 99%

“…This mindset leads to the Schmidt-Kalman filter. Target tracking with a Schmidt-Kalman filter is described in [27][28][29].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Tracking of Ocean Surface Objects from Unmanned Aerial Vehicles with a Pan/Tilt Unit using a Thermal Camera

Helgesen

Leira

Fossen

et al. 2017

J Intell Robot Syst

View full text Add to dashboard Cite

This paper presents four vision-based tracking system architectures for marine surface objects using a fixed-wing unmanned aerial vehicle (UAV) with a thermal camera mounted in a pan/tilt gimbal. The tracking systems estimate the position and velocity of an object in the North-East (NE) plane, and differ in how the measurement models are defined. The first tracking system measures the position and velocity of the target with georeferencing and optical flow. The states are estimated in a Kalman filter. A Kalman filter is also utilized in the second architecture, but only the georeferenced position is used as a measurement. A bearing-only measurement model is the foundation for the third tracking system, and because the measurement model is nonlinear, an extended Kalman filter is used for state estimation. The fourth tracking system extends the bearing-only tracking system to let navigation uncertainty in the UAV position affect the target estimates in a Schmidt-Kalman filter. All tracking architectures are evaluated on data gathered at a flight experiment near the Azores islands outside of Portugal. The results show that various marine vessels can be tracked quite accurately.

show abstract

Section: Tracking System Based On a Schmidt-kalman Filtersupporting

confidence: 60%

Section: Tracking System Based On a Schmidt-kalman Filtermentioning

confidence: 99%

See 1 more Smart Citation

Tracking of Ocean Surface Objects from Unmanned Aerial Vehicles with a Pan/Tilt Unit using a Thermal Camera

Helgesen

Leira

Fossen

et al. 2017

J Intell Robot Syst

View full text Add to dashboard Cite

show abstract

“…The DUKF structure in nonetheless differentiated from the standard UKF steps as follows: DUKF updates the observable components of the estimates of the mean vector and covariance matrix during the Kalman updating step:x k|k−1 →x k|k and P k|k−1 → P k|k , using an appropriate Kalman gain matrix defined based on the observable components. The unobservable parts are retained invariant, while the cross terms of the covariance are updated using the Schmidt-Kalman Filter (Schmidt, 1966;Novoselov et al, 2005). The steps of the DUKF algorithm are summarized in detail in Table 2.…”

Section: Discontinuous Unscented Kalman Filter Dukfmentioning

confidence: 99%

A Discontinuous Unscented Kalman Filter for Non-Smooth Dynamic Problems

Chatzis

Chatzi

2017

Front. Built Environ.

View full text Add to dashboard Cite

For a number of applications, including real/time damage diagnostics as well as control, online methods, i.e., methods which may be implemented on-the-fly, are necessary. Within a system identification context, this implies adoption of filtering algorithms, typically of the Kalman or Bayesian class. For engineered structures, damage or deterioration may often manifest in relation to phenomena such as fracture, plasticity, impact, or friction. Despite the different nature of the previous phenomena, they are described by a common denominator: switching behavior upon occurrence of discrete events. Such events include for example, crack initiation, transitions between elastic and plastic response, or between stick and slide modes. Typically, the state-space equations of such models are non-differentiable at such events, rendering the corresponding systems non-smooth. Identification of non-smooth systems poses greater difficulties than smooth problems of similar computational complexity. Up to a certain extent, this may be attributed to the varying identifiability of such systems, which violates a basic requirement of online Bayesian Identification algorithms, thus affecting their convergence for nonsmooth problems. Herein, a treatment to this problem is proposed by the authors, termed the Discontinuous D-modification, where unidentifiable parameters are acknowledged and temporarily excluded from the problem formulation. In this work, the D-modification is illustrated for the case of the Unscented Kalman Filter UKF, resulting in a method termed DUKF, proving superior performance to the conventional, and widely adopted, alternative.

show abstract

“…Instead of estimating the bias, the authors of [9] proposed to treat the bias as a zero-mean nuisance parameter with known a priori statistics. The impact of the bias could then be mitigated by means of an SKF.…”

Section: Previous Workmentioning

confidence: 99%

Suboptimal Kalman filters for target tracking with navigation uncertainty in one dimension

Brekke¹,

Wilthil²

2017

2017 IEEE Aerospace Conference

View full text Add to dashboard Cite

Abstract-The vast majority of literature on target tracking assumes that the position and orientation of the tracking sensor is stationary and/or known. However, for many applications the sensor is mounted on a moving vehicle, whose motion only can be estimated with a non-negligible uncertainty. In this paper, we suggest seven possible architectures for Kalman filtering in the simplest such scenario that we can construct: Assuming that both the ownship and a single target moves along a straight line according to linear kinematics. Some of the tracking filters are parameterized in the stationary world frame, while others are parameterized the body frame of the ownship. Also, some of the tracking filters take correlations between target state and ownship state into account, while others neglect such correlations. Simulations demonstrate that the suboptimal architectures may or may not reach similar performance as the optimal filter, depending on the process noise of the target and the performance measure chosen. Simulations of multitarget scenarios demonstrate that compensation of navigation uncertainty generally can reduce track-loss rates and OSPA distance.

show abstract

Mitigating the effects of residual biases with Schmidt-Kalman filtering

Cited by 33 publications

References 7 publications

Tracking of Ocean Surface Objects from Unmanned Aerial Vehicles with a Pan/Tilt Unit using a Thermal Camera

Tracking of Ocean Surface Objects from Unmanned Aerial Vehicles with a Pan/Tilt Unit using a Thermal Camera

A Discontinuous Unscented Kalman Filter for Non-Smooth Dynamic Problems

Suboptimal Kalman filters for target tracking with navigation uncertainty in one dimension

Contact Info

Product

Resources

About