A view of three decades of linear filtering theory

Kailath, T.

doi:10.1109/tit.1974.1055174

Cited by 608 publications

(176 citation statements)

References 255 publications

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“…This gives the first impression that there is a lack of symmetry between the forward and backward predictors. However, there is a simple relation between F f m and F b m credited to Burg (Kailath, 1974); namely,…”

Section: Properties Of Matrices F F M and F B Mmentioning

confidence: 99%

The Theory of Linear Prediction

Vaidyanathan

2007

Synthesis Lectures on Signal Processing

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Section: Properties Of Matrices F F M and F B Mmentioning

confidence: 99%

The Theory of Linear Prediction

Vaidyanathan

2007

Synthesis Lectures on Signal Processing

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“…The mature state of the art in digital signal processing was summarised in the texts of Rabiner and Gold (1975) and of Oppenheim and Schafer (1975) and in the survey paper of Kailath (1974).…”

Section: Introductionmentioning

confidence: 99%

Filters, Waves and Spectra

Pollock

2018

Econometrics

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Econometric analysis requires filtering techniques that are adapted to cater to data sequences that are short and that have strong trends. Whereas the economists have tended to conduct their analyses in the time domain, the engineers have emphasised the frequency domain. This paper places its emphasis in the frequency domain; and it shows how the frequency-domain methods can be adapted to cater to short trended sequences. Working in the frequency domain allows an unrestricted choice to be made of the frequency response of a filter. It also requires that the data should be free of trends. Methods for extracting the trends prior to filtering and for restoring them thereafter are described.

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“…As a matter of fact, the more the noise and the relevant signal share the same (or a very similar) frequency spectrum, the less the designer can remove the unwanted information by using classical filters [24,[44][45][46][47][48][49]. Furthermore, if the signal of interest and/or the noise are not stationary processes, which is also the case under study, the use of a Wiener filter [50,51] is inadequate [24].…”

Section: Introductionmentioning

confidence: 99%

Improving the response of wheel speed sensors by using robust and optimal signal processing techniques

Hernández

2005

Proceedings of the IEEE International Symposium on Industrial Electronics, 2005. ISIE 2005.

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Among the complete family of sensors for automotive safety, consumer and industrial application, speed sensors stand out as one of the most important. Actually, speed sensors have the diversity to be used in a broad range of applications. In today's automotive industry, such sensors are used in the antilock braking system, the traction control system and the electronic stability program. Also, typical applications are cam and crank shaft position/speed and wheel and turbo shaft speed measurement. In addition, they are used to control a variety of functions, including fuel injection, ignition timing in engines, and so on. However, some types of speed sensors cannot respond to very low speeds for different reasons. What is more, the main reason why such sensors are not good at detecting very low speeds is that they are more susceptible to noise when the speed of the target is low. In short, they suffer from noise and generally only work at medium to high speeds. This is one of the drawbacks of the inductive (magnetic reluctance) speed sensors and is the case under study. Furthermore, there are other speed sensors like the differential Hall Effect sensors that are relatively immune to interference and noise, but they cannot detect static fields. This limits their operations to speeds which give a switching frequency greater than a minimum operating frequency. In short, this research is focused on improving the performance of a variable reluctance speed sensor placed in a car under performance tests by using a recursive least-squares (RLS) lattice algorithm. Such an algorithm is situated in an adaptive noise canceller and carries out an optimal estimation of the relevant signal coming from the sensor, which is buried in a broad-band noise background where we have little knowledge of the noise characteristics. The experimental results are satisfactory and show a significant improvement in the signal-to-noise ratio at the system output.

show abstract

A view of three decades of linear filtering theory

Cited by 608 publications

References 255 publications

The Theory of Linear Prediction

The Theory of Linear Prediction

Filters, Waves and Spectra

Improving the response of wheel speed sensors by using robust and optimal signal processing techniques

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