Distortion Outage Analysis for Joint Space-Time Coding and Kalman Filtering

Parseh, Reza; Kansanen, Kimmo; Slock, Dirk

doi:10.1109/tsp.2017.2655493

Cited by 6 publications

(10 citation statements)

References 19 publications

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“…Applying then the well-known standard EKF synthesis procedure [4] for Eqs. (6) and (7), one can also easily obtain the algorithm Eq. (8).…”

Section: Stochastic Differential Equation Of the First Order (Sde-1)mentioning

confidence: 99%

“…One can assume, as an alternative to the quasi-linear EKF algorithms, where the linearization is instantaneously updated, that the robust solution for the EKF applications might be found if a "fixed linearization" (with predefined linearization matrix) is used instead of an "instantaneous" one. It actually means that instead of the EKF, the standard Kalman filtering (SKF) approach is applied [3][4][5][6][7], and obviously one has to admit some "losses" in the filtering accuracy for this case. At the same time, it has to be taken into account that the local Gaussian approximation of the a posteriori PDF assumes that actually all the model components are almost linear and therefore the accuracy losses might be rather moderate.…”

Section: Computational Complexitymentioning

confidence: 99%

“…In this sense, the filtering approach based on the theory of dynamic systems [3][4][5] pops up immediately as one of the possible ways to address this issue. The dynamic filtering approach, such as classic linear Kalman filtering, has been applied for many problems long ago [6] and recently as well [7]. However, in the following the dynamic filtering is proposed adopting a different (nonlinear) angle [8,9], namely, using signals from nonlinear chaotic attractors as a model for the desired signals arriving at the filtering structure.…”

Section: Introductionmentioning

confidence: 99%

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Ubiquitous Filtering for Nonlinear Problems

Kontorovich¹,

Ramos-Alarcon²

2020

Research Advances in Chaos Theory

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This chapter develops and extends the general theoretical results, previously published in the chapter "Nonlinear filtering of weak chaotic signals", and presents detailed implementations of a computationally simple, robust (filtering fidelity almost insensitive to changes of the desired input signal properties) and rather precise approach for the filtering of weak signals of different physical nature (biological, seismic, voice, etc.) in presence of white Gaussian noise. The implementations rely on non-linear filtering techniques that in general can be considered as either one-moment or multi-moment, in the sense that they operate with a single sample (instantaneous fashion) or with various adjacent samples (non-instantaneous fashion). Chaotic modeling of the real input signals allows achieving an almost ubiquitous filtering approach with a computationally simple implementation. Application of the linearization strategies (for both one and twomoment filtering) provide, additionally, "invariance" of the processing algorithms to variations on the nature and statistics of the input signals.

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“…Applying then the well-known standard EKF synthesis procedure [4] for Eqs. (6) and (7), one can also easily obtain the algorithm Eq. (8).…”

Section: Stochastic Differential Equation Of the First Order (Sde-1)mentioning

confidence: 99%

Section: Computational Complexitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Ubiquitous Filtering for Nonlinear Problems

Kontorovich¹,

Ramos-Alarcon²

2020

Research Advances in Chaos Theory

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“…Como alternativa a los algoritmos cuasilineales del EKF, donde la linealización se actualiza instantáneamente, se puede buscar una solución robusta y de baja complejidad computacional usando una «linealización fija» (con una matriz de linealización predefinida para el problema específico bajo estudio) en vez de la linealización instantánea. De hecho, eso significa que se estaría usando el filtro estándar de Kalman (SKF) [7][8][9][10][11] en vez del EKF y por consecuencia, aunque se tendría una menor complejidad, se tendrían también pérdidas en la precisión del filtraje. Sin embargo, hay que tener en cuenta que la LGA de la PDF a posteriori supone que de hecho todas las componentes del modelo son casi lineales y por lo tanto las pérdidas de precisión podrían ser moderadas.…”

Section: Complejidad Computacionalunclassified

“…Con una historia de más de 50 años, la teoría de sistemas dinámicos [7][8][9] es uno de los pilares de muchas áreas científicas, como física, control automático, comunicaciones, etc. ; particularmente es muy relevante para el filtraje mediante el célebre filtro de Kalman (propuesto por Rudolph E. Kalman en 1960), el cual permite un filtraje muy preciso considerando que la señal deseada es un sistema dinámico lineal bajo la influencia de ruido blanco aditivo gaussiano (AWGN) y desde su invención hasta la fecha cuenta con un sinnúmero de aplicaciones prácticas recientes y añejas (por ejemplo, [10,11]).…”

Section: Introductionunclassified

Filtraje robusto de señales débiles de fenómenos reales

Kontorovich

Ramos-Alarcon

2019

ings

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En un gran número de escenarios de la vida real se requiere procesar señales de interés que se encuentran muy inmersas en medio de ruido de fondo: señales tectónicas de las entrañas de la Tierra, otras provenientes del lejano cosmos, de telemetría biomédica, acústicas lejanas, interfaces neuronales no invasivas, etc. El propósito de este trabajo es presentar la descripción de una plataforma robusta y eficiente para hacer filtraje en tiempo real de señales muy inmersas en ruido (bastante débiles) y de naturaleza muy diferente. La estrategia propuesta se basa en dos principios: el modelado de las señales de los fenómenos físicos mediante procesos caóticos y la aplicación de estrategias de filtraje basadas en la teoría de sistemas dinámicos no lineales. Tomando como caso de estudio señales sísmicas, señales de electrocardiogramas fetales, señales de tipo voz y señales de interferencias de radiofrecuencia, este trabajo experimental muestra que la metodología es eficiente (error cuadrático medio menor al 1 %) y robusta (la estructura de filtraje, basada en filtro de Kalman, es invariante ante diferentes señales fenomenológicas). La metodología presentada resulta ser muy atractiva para aplicaciones prácticas para la detección de señales débiles en tiempo real por su alta precisión de filtraje con una mínima complejidad computacional y tiempos de procesamiento muy cortos.

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The research on channel estimation and signal‐noise ratio estimation based on minimum error entropy kalman filter for single carrier frequency domain equalization system

Pengyu

Xiang

Liang

et al. 2022

Int J Communication

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In this paper, the channel estimation and signal-to-noise ratio (SNR) estimation technique of single-carrier frequency domain equalization (SC-FDE) system under low SNR in aeronautical multipath channel are studied, a SNR estimation algorithm which is easy to implement in engineering and an improved LS channel estimation algorithm based on Kalman filter using minimum error entropy (MEE-KF) are proposed. This paper first introduces the SC-FDE system and introduces the principle of MEE-KF, and then, the channel estimation flow based on MEE-KF is obtained by combining it with the traditional LS channel estimation algorithm, which makes the estimation results perform better. Simulation results show that after getting more accurate noise variance, the channel estimation results can better follow the changes of the channel after MEE-KF processing, so as to resist the doppler frequency offset effect and make the channel estimation results more accurate, that is the channel response results of the data part can be closer to the real situation, so that the communication performance of SC-FDE system has also been greatly improved.

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Distortion Outage Analysis for Joint Space-Time Coding and Kalman Filtering

Cited by 6 publications

References 19 publications

Ubiquitous Filtering for Nonlinear Problems

Ubiquitous Filtering for Nonlinear Problems

Filtraje robusto de señales débiles de fenómenos reales

The research on channel estimation and signal‐noise ratio estimation based on minimum error entropy kalman filter for single carrier frequency domain equalization system

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