A novel simplified channel tracking method for MIMO–OFDM systems with null sub-carriers

Jeon, Hyoung-Goo; Serpedin, Erchin

doi:10.1016/j.sigpro.2007.10.017

Cited by 11 publications

(6 citation statements)

References 12 publications

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“…1, in which x(k) is a linearized trajectory in the linear window model of the moving target [7] , M is a discrete linear window size, and D k is the moving interval of a target in between k-1 and k.…”

Section: ⅱ Swt Algorithmmentioning

confidence: 99%

Tracking Algorithm Based on Moving Slide Window for Manuevering Target

Bae¹,

Lee²,

Jeon³

2016

Journal of the Institute of Electronics and Information Enginee

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In this paper, we propose a novel tracking algorithm called slide window tracker (SWT) suitable for maneuvering target. To efficiently estimate trajectory of moving target, we adopt a sliding piecewise linear window which includes past trace information. By adjusting the window parameters, the proposed algorithm is to reduce measurement noise and to track fast maneuvering target with little computational increment as compared to α-β tracker. Throughout the computer simulations, we verify outstanding tracking performance of the SWT algorithm in noisy linear and nonlinear trajectories. Also, we show that the SWT algorithm is not sensitive to initial model parameter selection, which gives large degree of freedom in applying the SWT algorithm to unknown time-varying measurement environments.

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Section: ⅱ Swt Algorithmmentioning

confidence: 99%

Tracking Algorithm Based on Moving Slide Window for Manuevering Target

Bae¹,

Lee²,

Jeon³

2016

Journal of the Institute of Electronics and Information Enginee

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“…If we know the present estimated position, then we can predict the target position. If the target position varies linearly, the predicted target position can be expressed by the linear combination of the previously estimated position [7]. When initial positions are obtained greater than the window size, the process is switched to SWT from α-β tracker.…”

Section: A Switched Slide Window Trackermentioning

confidence: 99%

“…is the x coordinate of the filtered target position, m ω is the weight value [7], and μ is the coefficient for the measurement update of the slide window tracker (In this paper, we set a α μ = .). From (3), we can easily extend the equation for a 2-D problem [8].…”

Section: A Switched Slide Window Trackermentioning

confidence: 99%

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Maritime radar simulator based on DSP board using switched slide window tracker

et al. 2008

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We present a novel switched slide window tracker (SSWT) to track moving targets for Automatic Radar Plotting Aid (ARPA) information in maritime radar. The SSWT is composed of both α-β tracker to find the initial parameters of a slide window and slide window tracker (SWT) to track the moving targets. The proposed algorithm does not require a high computation complexity. In addition, the proposed method can effectively track the nonlinear model by using a piecewise linear model in a target trajectory. The maritime radar simulator with the proposed SSWT algorithm is realized using a commercial DSP board.

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“…Huang et al [14] discussed semi-blind subspace channel estimation in uplink OFDMA systems. Jeon and Serpedin [16] developed channel tracking method in timevariant scenario for MIMO OFDM systems with null subcarriers.…”

Section: Introductionmentioning

confidence: 99%

Fast Convergence on Blind and Semi-Blind Channel Estimation for MIMO–OFDM Systems

Zhang

Kuo

2014

Circuits Syst Signal Process

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In this paper, blind and semi-blind subspace channel estimations with fast convergence rate are proposed for multiple-input multiple-output orthogonal frequency division multiplexing (OFDM) systems in the presence of virtual carriers. The subspace method has a main drawback of slow convergence rate when deriving the noise subspaces from the second-order statistics of received signals. This phenomenon is especially evident when the size of received signals is large. Inspired by this fact, we present a block matrix scheme (BMS) to generate a group of sub-vectors from each OFDM symbol when virtual carriers are used. The number of equivalent signals is increased and therefore, the convergence rate of channel estimation is enhanced. The semi-blind method is also investigated by incorporating subspace technology with least square scheme. The identifiability of the BMS-based channel estimation is analyzed to derive the applicable range of the BMS size. The computational complexity of the proposed channel estimation is calculated at the end. Computer simulations show that the proposed blind and semi-blind methods converge very well in channel estimation and equalization.

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A novel simplified channel tracking method for MIMO–OFDM systems with null sub-carriers

Cited by 11 publications

References 12 publications

Tracking Algorithm Based on Moving Slide Window for Manuevering Target

Tracking Algorithm Based on Moving Slide Window for Manuevering Target

Maritime radar simulator based on DSP board using switched slide window tracker

Fast Convergence on Blind and Semi-Blind Channel Estimation for MIMO–OFDM Systems

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