A modified adaptive Kalman filter for real-time applications

Chen, G.; Chui, Charles K.

doi:10.1109/7.68157

Cited by 38 publications

(4 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In generally, , therefore,we approximately consider that is complex valued Gussian noise,whose mean and variance are unknown.Thus the filter performance is compensated by a fiction noise .Moreover, we assume the variance of is unknown too.Denote by the identified channel at time n based on the state at time m. Extend the works in [6], [7]and [8],we obtain following time-varying noise recursive estimator:…”

Section: Mimo Channel Identification Based On Kfmentioning

confidence: 99%

“…Nevertheless, as we know,if the a priori system model is low precision or timevarying,performance of Kalman filtering will degenerate,even lead to divergency.In order to improve the performance of KF,Sage and Husa propose a kind of adaptive KF in [7].But it isn't able to estimate time-varying noise.For implement in systems with time-varying process noise and measurement noise, a modified adaptive KF is derived in [8],Dynamic matrix and forgetting factor to emphasize the effect of new data and forget old data gradually are adopted in [9].In [10],Z.L. Deng compensated unknown time-varying fiction noise for autoregressive move average(ARMA) model error of the system: hence,the word "robust",and obtain good performance.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Semi-blind Method for MIMO Channel Identification

Huang

Liu

et al. 2007

2007 IEEE International Conference on Automation and Logistics

View full text Add to dashboard Cite

A semi-blind algorithm to perform channel identification for multiple input multiple output(MIMO) systems in frequency and time-selective fading environments is proposed. It contains two steps:training and tracking.In training phase, improved Kalman filtering,namely robust Kalman filtering (RKF), is exploited to identify channel impulse response(CIR). After that,in tracking stage,the RKF and minimum mean-square error feedback decision equalizer (MMSE-DFE) cooperate to track the time-varying channel.The RKF recursions is presented and a closed-form solution for baud rate MIMO MMSE-DFE under perfect knowledge of CIR and correct past decisions conditions is derived. In addition, it regards unknown dc-offset due to zero intermediate frequency(IF) at the receiver as the mean of measurement noise, which is estimated as a byproduct through robust Kalman filters. Finally, it is compared with wellknown ones, such as least mean-square(LMS),recursive least square (RLS),Kalman filtering (KF).All these show that the proposal exhibits better performance.

show abstract

Section: Mimo Channel Identification Based On Kfmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Semi-blind Method for MIMO Channel Identification

Huang

Liu

et al. 2007

2007 IEEE International Conference on Automation and Logistics

View full text Add to dashboard Cite

show abstract

“…Some of the later works reported [11][12][13] use the innovation to tune the noise covariances by modifying Mehra's propositions. A modified adaptive Kalman filtering algorithm is derived [14] for the standard linear problem under an irregular environment where all variances of the zero mean Gaussian white (system and observation) noises are unknown a priori. On the other hand, Loh and Hauser [15] used the innovations to tune the Kalman gain matrix straightaway.…”

Section: Innovation Based Approachesmentioning

confidence: 99%

Adaptive Design for Estimation of Mixing Heights From Sodar Based Measurements

Mukherjee

Pal

2006

Fluct. Noise Lett.

View full text Add to dashboard Cite

Accurate estimation of mixing height is important, since it is an important parameter for lower atmospheric studies involving aerosol monitoring and pollutant dispersal models. Sodar happens to be one of the best instruments for monitoring the mixing height. But it suffers from the drawback of acoustic noise, which makes the measurement inaccurate. Conventional Kalman filter has been used to estimate atmospheric boundary layer by filtering the measurement noise involved in sodar data. But there are certain limitations of the accuracy available from conventional Kalman filter which may be overcome by proper adaptive design. The present work develops an adaptive scheme for estimation of mixing heights. It considers the selection of a proper meteorological system model from a bank of system models. Diurnal and seasonal changes in measurement noise statistics of the acoustic radar is taken into account by designing a fuzzy logic based adaptive scheme.

show abstract

“…Nevertheless, as we know,if the a priori system model is low precision or time-varying,performance of Kalman filtering will degenerate, even lead to divergence. In order to improve the performance of KF,Sage and Husa proposed a kind of adaptive KF in [12],but it isn't able to estimate time-varying noise.A modified adaptive KF was derived in [13]. A forgetting factor to emphasize the effect of new data and forget old data gradually was adopted in [14],which can be applied into time-varying systems.…”

Section: Introductionmentioning

confidence: 99%

Time-selective MIMO channel estimation based on implicit training

Zuo

Liu

2007

2007 International Symposium on Intelligent Signal Processing and Communication Systems

View full text Add to dashboard Cite

A novel scheme to perform multiple input multiple output(MIMO) channel estimation in time-selective fading environments is suggested.In order to improve transmission efficiency,training sequences are arithmetically added to the information symbols. We regard the output of information symbols ,additive white noises and dc-offsets at the receiver as fiction measurement noise, whose mean and variance are both unknown.A time-varying measurement noise recursive estimator and Kalman filters cooperate to track the timevarying MIMO channel impulse response(CIR). Finally,it is compared with some existing methods,and all these indicate that the proposal exhibits better performance.

show abstract

A modified adaptive Kalman filter for real-time applications

Cited by 38 publications

References 3 publications

A Semi-blind Method for MIMO Channel Identification

A Semi-blind Method for MIMO Channel Identification

Adaptive Design for Estimation of Mixing Heights From Sodar Based Measurements

Time-selective MIMO channel estimation based on implicit training

Contact Info

Product

Resources

About