CiteULike: dcastro's kalman

Analysis of a Kalman Approach for a Pedestrian Positioning System in Indoor Environments

Edith Herrera — 2008-06-13T06:44:20-00:00

Euro-Par 2007 Parallel Processing (2007), pp. 931-940.

In this work we present the design principles of a wearable positioning system for users in unprepared indoor environments. We describe the most suitable technology for our application and we model the dynamics of a walking user. The system uses inertial sensors and a location system based on ultrawideband (UWB). Data fusion is carried out with a Kalman filter. The user position is estimated from data provided by the UWB location system. To update the position and direction of the user we use a dead reckoning algorithm. The use of redundant sensors and the data fusion technique minimises the presence of shadow zones in the environment. We show the advantages of combining different sensors systems.

Adaptive synchronization and channel parameter estimation using an extended Kalman filter

A Aghamohammadi — 2008-05-22T09:06:42-00:00

Communications, IEEE Transactions on, Vol. 37, No. 11. (1989), pp. 1212-1219.

Unified modeling and estimation of the MD (multiplicative distortion) in finite-alphabet digital communication systems is presented. A simple form of MD is the carrier phase exp(<e1>j</e1>&thetas;), which has to be estimated and compensated for in a coherent receiver. A more general case with fading must, however, allow for amplitude as well as phase variations of the MD. The authors assume a state-variable model for the MD and generally obtain a nonlinear estimation problem with additional randomly varying system parameters such as received signal power, frequency offset, and Doppler spread. An extended Kalman filter is then applied as a near-optimal solution to the adaptive MD and channel parameter estimation problem. Examples are given to show the use and some advantages of this scheme

On phase-locked loops and Kalman filters

A Patapoutian — 2008-05-22T08:58:52-00:00

Communications, IEEE Transactions on, Vol. 47, No. 5. (1999), pp. 670-672.

Driessen (1994) and Christiansen (1994) independently showed that for a specific dynamic model, the proportional-integral phase-locked loop (PLL) has the same structure as the Kalman filter. In this paper, closed-form expressions of the corresponding Kalman gain values are derived both in acquisition and tracking modes of the PLL

Estimation of OFDM Time-Varying Fading Channels Based on Two-Cross-Coupled Kalman Filters

Ali Jamoos — 2008-05-19T19:10:45-00:00

International Joint Conferences on Computer, Information, and Systems Sciences, and Engineering (CISSE 07) (2007)

Anti multipath cellular radio location for DS/CDMA systems using a novel EKF subchip RAKE tracking loop

E Fishler — 2008-05-09T15:42:49-00:00

Military Communications Conference Proceedings, 1999. MILCOM 1999. IEEE, Vol. 2 (1999), pp. 1328-1332 vol.2.

This paper discusses an improved method for localization in a DS-CDMA based cellular-phone communication link. This method suggests an optimization for code synchronization, which allows for TDOA (time difference of arrival) estimations to be solved for the position of a mobile phone. It is known that the time delay of a received DS signal, derived from the classical DLL, may express severe timing errors due to multipath effects. A new anti multipath multi-tracking extended Kalman filter loop is shown to achieve far better results especially in the environment of specular multipath. Specifically, this new loop minimizes the errors due to multipath components by letting the EKF decide upon the best weights of its sub-chip processing branches, resulting in a RAKE-like tracking loop. This loop tracks not only the line of sight path, but also the other multipath components as well as their power and phase. It is shown that the implementation of this technique converges to the classical non-coherent code tracking DLL structure when no multipath is assumed, but results in a new and efficient tracking loop structure in the more realistic fading channel case. The application of this technique to the EIA IS-95 system is considered, where accurate location estimations as well as power management utilities are treated

Multi-input multi-output fading channel tracking and equalization using Kalman estimation

C Komninakis — 2007-11-28T10:36:58-00:00

Signal Processing, IEEE Transactions on [see also Acoustics, Speech, and Signal Processing, IEEE Transactions on], Vol. 50, No. 5. (2002), pp. 1065-1076.

This paper addresses the problem of channel tracking and equalization for multi-input multi-output (MIMO) time-varying frequency-selective channels. These channels model the effects of inter-symbol interference (ISI), co-channel interference (CCI), and noise. A low-order autoregressive model approximates the MIMO channel variation and facilitates tracking via a Kalman filter. Hard decisions to aid Kalman tracking come from a MIMO finite-length minimum-mean-squared-error decision-feedback equalizer (MMSE-DFE), which performs the equalization task. Since the optimum DFE for a wide range of channels produces decisions with a delay Δ > 0, the Kalman filter tracks the channel with a delay. A channel prediction module bridges the time gap between the channel estimates produced by the Kalman filter and those needed for the DFE adaptation. The proposed algorithm offers good tracking behavior for multiuser fading ISI channels at the expense of higher complexity than conventional adaptive algorithms. Applications include synchronous multiuser detection of independent transmitters, as well as coordinated transmission through many transmitter/receiver antennas, for increased data rate

Comparison of Kalman Filter and Wavelet Filter for Denoising

S Postalcloglu — 2007-11-14T17:50:16-00:00

Neural Networks and Brain, 2005. ICNN&B '05. International Conference on, Vol. 2 (2005), pp. 951-954.

This paper presents denoising the signal using Wavelet filter and Kalman filter. The noise is zero mean and the variance value is 0,001. Kalman filter removes disturbances or faults from the signal by using initialization and propagation of error covariance statistics. Implementation of Kalman filter is impractical in large scale models as shown for the oscillator system. As an alternative Wavelet filter has been used for the same system. Coiflet 2 which is orthogonal wavelet has been used. Soft thresholding has been applied. Decomposition is performed at level 9. The results of Wavelet filter and Kalman filter are shown. Response of Wavelet filter is better when compared with Kalman filter result.

A DS/SS predictive PN code tracking loop using a Kalman filter

Takashi Shono — 2007-11-14T17:47:46-00:00

Electronics and Communications in Japan (Part I: Communications), Vol. 83, No. 12. (2000), pp. 71-83.

We present a new code tracking loop, which is stable and has high capability of tracking, for DS/SS communication systems between aircraft and ground base stations. The proposed loop uses delay estimators combining the modified code tracking loop (MCTL) with the Kalman filter, which is usually used to track the position of an aircraft, but is used to estimate the propagation delay of the spreading sequence in our loop. We call the proposed loop the predictive PN code tracking loop (PCTL). The estimated delay-controlled device transforms the propagation delay obtained by the Kalman filter to the adaptive signal and inputs it to the MCTL discretely through the operational circuit. The PCTL can revise the degradation in the tracking performance of the MCTL under the constraint of Doppler effects and the channel noise. The tracking performance of the PCTL is investigated and compared with that of the MCTL by computer simulations. It is shown that our proposed system has a higher code tracking performance than the MCTL from the viewpoint of tracking jitter. © 2000 Scripta Technica, Electron Comm Jpn Pt 1, 83(12): 71-83, 2000

GPS/INS uses low-cost MEMS IMU

AK Brown — 2007-08-29T17:45:12-00:00

Aerospace and Electronic Systems Magazine, IEEE, Vol. 20, No. 9. (2005), pp. 3-10.

A description of the design, operation, and test results of a miniature, low-cost, integrated GPS/inertial navigation system that uses commercial off-the-shelf micro-electro-mechanical system (MEMS) accelerometers and gyroscopes. The MEMS inertial measurement unit (EMU) is packaged in a small size and provides the raw EMU data through a serial interface to a processor board where the inertial navigation solution and integrated GPS/inertial Kalman filter is generated. The GPS/inertial software integration is performed using NAVSYS' modular InterNav software product. This allows integration with different low-cost GPS chip sets or receivers and also allows the integrated GPS/inertial navigation solution to be embedded as an application on a customer's host computer. This modular object-oriented architecture facilitates integration of the miniature MEMS GPS/INS navigation system for embedded navigation applications and is designed to handle the large errors characteristic of a low-grade MEMS IMU. Test results are presented showing the performance of the integrated MEMS GPS/inertial navigation system. Data is provided showing the position, velocity, and attitude accuracy when operating with GPS aiding and also for periods where GPS dropouts occur and alternative navigation update sources are used to bound the MEMS inertial navigation error growth.