Published Papers - IJIMA Hiroshi

Iterative approach to estimate the parameters of a TVAR process corrupted by a MA noise

Hiroshi Ijima, Roberto Diversi, Eric Grivel

European Signal Processing Conference ( European Signal Processing Conference, EUSIPCO )    456 - 460   2014.11

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A great deal of interest has been paid to the time-varying autoregressive (TVAR) parameter tracking, but few papers deal with this issue when noisy observations are available. Recently, this problem was addressed for a TVAR process disturbed by an additive zero-mean white noise, by using deterministic regression methods. In this paper, we focus our attention on the case of an additive colored measurement noise modeled by a moving average process. More particularly, we propose to estimate the TVAR parameters by using a variant of the improved least-squares (ILS) methods, initially introduced by Zheng to estimate the AR parameters from a signal embedded in a white noise. Simulation studies illustrate the advantages and the limits of the approach.

ITERATIVE APPROACH TO ESTIMATE THE PARAMETERS OF A TVAR PROCESS CORRUPTED BY A MA NOISE

Hiroshi Ijima, Roberto Diversi, Eric Grivel

2014 PROCEEDINGS OF THE 22ND EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO) ( IEEE )    456 - 460   2014  [Refereed]

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A great deal of interest has been paid to the time-varying autoregressive (TVAR) parameter tracking, but few papers deal with this issue when noisy observations are available. Recently, this problem was addressed for a TVAR process disturbed by an additive zero-mean white noise, by using deterministic regression methods. In this paper, we focus our attention on the case of an additive colored measurement noise modeled by a moving average process. More particularly, we propose to estimate the TVAR parameters by using a variant of the improved least-squares (ILS) methods, initially introduced by Zheng to estimate the AR parameters from a signal embedded in a white noise. Simulation studies illustrate the advantages and the limits of the approach.

PREDICTION ERROR METHOD TO ESTIMATE THE AR PARAMETERS WHEN THE AR PROCESS IS DISTURBED BY A COLORED NOISE

Roberto Diversi, Hiroshi Ijima, Eric Grivel

2013 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) ( IEEE )  2013 Vol.9   6143 - 6147   2013  [Refereed]

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Estimating the autoregressive parameters from noisy observations has been addressed by various authors for the last decades. Although several on-line or off-line approaches have been proposed when the additive noise is white, few papers deal with the additive moving average noise. In this paper, we suggest estimating the model parameters by using the prediction error method. Despite its high computational cost, the method has the advantage of being efficient in the Gaussian case. A comparative study with existing methods is then carried out and points out the efficiency of our approach especially when the number of samples is small.

Deterministic regression methods for unbiased estimation of time-varying autoregressive parameters from noisy observations

Hiroshi Ijima, Eric Grivel

SIGNAL PROCESSING ( ELSEVIER SCIENCE BV )  92 ( 4 ) 857 - 871   2012.04  [Refereed]

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A great deal of interest has been paid to autoregressive parameter estimation in the noise-free case or when the observation data are disturbed by random noise. Tracking time-varying autoregressive (TVAR) parameters has been also discussed, but few papers deal with this issue when there is an additive zero-mean white Gaussian measurement noise. In this paper, one considers deterministic regression methods (or evolutive methods) where the WAR parameters are assumed to be weighted combinations of basis functions. However, the additive white measurement noise leads to a weight-estimation bias when standard least squares methods are used. Therefore, we propose two alternative blind off-line methods that allow both the variance of the additive noise and the weights to be estimated. The first one is based on the errors-in-variable issue whereas the second consists in viewing the estimation issue as a generalized eigenvalue problem. A comparative study with other existing methods confirms the effectiveness of the proposed methods. (C) 2011 Elsevier B.V. All rights reserved.

DOI

Estimation of multichannel TVAR parameters from noisy observations based on an evolutive method

Hiroshi Ijima, Eric Grivel

European Signal Processing Conference     990 - 994   2011

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The multichannel time-varying autoregressive (M-TVAR) model is used in various applications such as radar processing, mobile communications and econometric time series. In that case, the M-TVAR parameter matrices are usually estimated by extending the evolutive method, initially proposed by Grenier in the 80ies for the scalar case. In that case, the time-varying coefficients of each matrix are expressed as weighted combinations of pre-defined basis functions. However, the estimation of the M-TVAR parameter matrices from noisy observations is a still key issue to be addressed. In this paper, we propose an effective evolutive method to estimate M-TVAR matrices from noisy observations. We suggest viewing the weight estimation from noisy observations as an errors-invariables issue. Despite its high computational cost, the approach has the advantage of also providing the covariance matrix of both the driving process and the additive noise. Simulation results point out the relevance of the approach. © 2011 EURASIP.

EVOLUTIVE METHOD BASED ON A GENERALIZED EIGENVALUE DECOMPOSITION TO ESTIMATE TIME VARYING AUTOREGRESSIVE PARAMETERS FROM NOISY OBSERVATIONS

Hiroshi Ijima, Julien Petitjean, Eric Grivel

2011 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING ( IEEE )  2011 Vol.5   3808 - 3811   2011  [Refereed]

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A great deal of interest has been paid to the estimation of time-varying autoregressive (TVAR) parameters. However, when the observations are disturbed by an additive white measurement noise, using standard least squares methods leads to a weight-estimation bias. In this paper, we propose to jointly estimate the TVAR parameters and the measurement-noise variance from noisy observations by means of a generalized eigenvalue decomposition. It extends to the TVAR case an off-line method that was initially proposed for AR parameter estimation from noisy observations. A comparative study is then carried out with existing methods such as the recursive errors-in-variable approach and Kalman based algorithms.

ESTIMATION OF MULTICHANNEL TVAR PARAMETERS FROM NOISY OBSERVATIONS BASED ON AN EVOLUTIVE METHOD

Hiroshi Ijima, Eric Grivel

19TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO-2011) ( EUROPEAN ASSOC SIGNAL SPEECH & IMAGE PROCESSING-EURASIP )    990 - 994   2011  [Refereed]

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The multichannel time-varying autoregressive (M-TVAR) model is used in various applications such as radar processing, mobile communications and econometric time series. In that case, the M-TVAR parameter matrices are usually estimated by extending the evolutive method, initially proposed by Grenier in the 80ies for the scalar case. In that case, the time-varying coefficients of each matrix are expressed as weighted combinations of pre-defined basis functions. However, the estimation of the M-TVAR parameter matrices from noisy observations is a still key issue to be addressed. In this paper, we propose an effective evolutive method to estimate M-TVAR matrices from noisy observations. We suggest viewing the weight estimation from noisy observations as an errors-in variables issue. Despite its high computational cost, the approach has the advantage of also providing the covariance matrix of both the driving process and the additive noise. Simulation results point out the relevance of the approach.

Kalman Filter‐based Detector of Signals in Nonstationary Random Noise

IJIMA Hiroshi, OHSUMI Akira

Proceedings of the ISCIE International Symposium on Stochastic Systems Theory and its Applications (Institute of Systems, Control and Information Engineers)   40th   210 - 214   2009  [Refereed]

Detection of Signals Corrupted by Nonstationary Random Noise via Kalman Filter-based Stationarization Approach

Ijima Hiroshi, Ohsumi Akira

Proceedings of the Annual Conference of the Institute of Systems, Control and Information Engineers ( The Institute of Systems, Control and Information Engineers )  8 ( 0 ) 324 - 324   2008  [Refereed]

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本論文では，非定常不規則雑音に埋もれた信号を検出する新しい手法を提案する．従来，信号検出問題においては，雑音は数学的に取扱いが容易であることからその統計的性質が時間的に変化しない定常確率過程として取り扱われてきた．しかし，実際に観測されるデータには，例えば海上や海底における潮力などの影響によって非定常なものが多く存在する．このような非定常雑音中の信号検出問題に対して，本論文では信号は時間的に局所的に存在し，雑音の非定常性は緩やかに変化するものと仮定して，観測雑音を時変係数を持つARMAモデルで記述できるとする．このモデルパラメータをカルマンフィルタにより推定し非定常観測データを定常とみなせる観測データに変換する手法を提案し，その有効性を数値シミュレーションにより確認する．この手法により，非定常雑音が観測データに介在する問題は，従来の定常雑音に埋もれた信号の検出問題に帰着できる．

DOI

Detection of signals in nonstationary random noise via stationarization of data incorporated with Kalman filter

Hiroshi Ijima, Yukinori Yamashita, Akira Ohsumi

2007 IEEE INTERNATIONAL SYMPOSIUM ON SIGNAL PROCESSING AND INFORMATION TECHNOLOGY, VOLS 1-3 ( IEEE )    562 - +   2007  [Refereed]

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Recently, the authors have proposed a method for the detection of signals corrupted by nonstationary random noise based on stationarization of the observation data which can be modeled by the first-order Ito stochastic differential equation. In this paper, in order to apply this method to more general situation, we propose a stationarization method incorporated with Kalman filter. To test the proposed method simulation experiments are presented.

ESTIMATION OF MOTION PARAMETERS OF MOVING TARGET USING WIGNER DISTRIBUTION

Hiroshi Ijima, Azusa Matsuoka, Tetsuya Nakajima, Akira Ohsumi

ICSPC: 2007 IEEE INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING AND COMMUNICATIONS, VOLS 1-3, PROCEEDINGS ( IEEE )    688 - +   2007  [Refereed]

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The purpose of this paper is to estimate unknown motion parameters, acceleration and initial velocity, from the radar signal corrupted by random noise. The principal attack of the approach is to use the (pseudo)-Wigner distribution which is computed from the noisy observation data. Parameters are estimated by least-squares method for the noisy instanteanous frequency of returned signal. Numerical simulations are presented to verify the efficacy of the proposed method.

A method of detection of signals corrupted by nonstationary random noise via stationarization of the data

Hiroshi Ijima, Akira Ohsumi, Ryo Okui

ELECTRONICS AND COMMUNICATIONS IN JAPAN PART III-FUNDAMENTAL ELECTRONIC SCIENCE ( SCRIPTA TECHNICA-JOHN WILEY & SONS )  90 ( 8 ) 29 - 38   2007  [Refereed]

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This paper proposes a method for detecting a signal embedded in nonstationary noise. In most past studies of the signal detection problem, random noise is considered as a stationary stochastic process, since it is mathematically easy to handle. However, the noise observed in practice contains many nonstationary elements with time-varying (evolutionary) statistical properties. In this study, observational noise is modeled as a probability density function with slowly evolving parameters. Then, based on the evolving spectral representation, the nonstationary observation data are transformed to a stationary process. A new method is proposed as follows. It is assumed that nonstationarity remains in the stationarized observation data in the interval containing the signal, due to the effect of the signal. Then the signal is detected by testing for stationarity. In the proposed method, Priestley's evolutionary spectrum is used in the spectral representation of the nonstationary stochastic process, and the method of Okabe and colleagues based on the KM2O-Langevin equation is used for the stationarity test. The effectiveness of the proposed method is verified by a simulation experiment. (C) 2007 Wiley Periodicals, Inc. Electron Comm Jpn Pt 3, 90(8): 29-38, 2007; Published online in Wiley InterScience (www.interscience.wiley.com).

DOI

A Method of Detection of Signals Corrupted by Nonstationary Random Noise via Stationarization of the Data

IJIMA Hiroshi, OHSUMI Akira, OKUI Ryo

The IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences (Japanese edition) A ( The Institute of Electronics, Information and Communication Engineers )  89 ( 6 ) 535 - 543   2006.06  [Refereed]

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本研究は非定常雑音に埋もれた信号の検出に対する一つの手法を提案したものである.従来の多くの信号検出問題においては,不規則雑音は数学的取扱いが容易であることから定常確率過程として取り扱われてきた.しかし,実際観測される雑音には時間とともに統計的性質が変化する非定常雑音が多く存在する.本研究では,観測雑音を緩やかに変化するパラメータをもつ確率密度関数としてモデル化し,その時間変化するスペクトル表現から非定常な観測データを定常過程に変換する.このとき,信号が存在する区間においてはその信号の影響により定常化された観測データに非定常性が残存すると考え,その定常性の検定により信号を検出するという新しい手法を提案する.この手法において,非定常確率過程のスペクトル表現にはPriestleyの時変スペクトル,定常性の検定には岡部らによるKM_2O-ランジュヴァン方程式に基づいた手法を用いる.提案した手法の有効性をシミュレーション実験により確認している.

Time-delay estimation of signals in nonstationary random noise via stationarization and Wigner distribution-based approach.

Hiroshi Ijima, Akira Ohsumi, Satoshi Yamaguchi

14th European Signal Processing Conference, EUSIPCO 2006, Florence, Italy, September 4-8, 2006 ( IEEE )    1 - 5   2006  [Refereed]

Detection of signals in nonstationary random noise via stationarization and stationarity test

Hiroshi Ijima, Ryo Okui, Akira Ohsumi

2005 IEEE/SP 13th Workshop on Statistical Signal Processing (SSP), Vols 1 and 2 ( IEEE )    543 - 546   2005  [Refereed]

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In this paper, an effective method is proposed for detecting a signal which is corrupted by nonstationary random noise. First, the noisy observation data whose statistical property is nonstationary owing to the noise is modified such that they become stationary ones. Then, based on this stationarized observation process, the stationarity test is executed to detect the signal. The method is tested by simulations to show the efficacy.

Motion parameters estimation by new propagation approach and time-frequency representations

Djurovic, I, S Stankovic, A Ohsumi, H Ijima

SIGNAL PROCESSING-IMAGE COMMUNICATION ( ELSEVIER SCIENCE BV )  19 ( 8 ) 755 - 770   2004.09  [Refereed]

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Estimation of time-varying motion parameters of moving objects is considered. The motion parameters are mapped into the instantaneous frequency (IF) of a frequency modulated signal. The IF estimation is obtained by using the Wigner distribution. Generalization in the case of multiple objects and reduced interference distributions is given. A new propagation approach is proposed for joint estimation of velocity and position of objects in a video-sequence. This approach produces better accuracy than the variable mu-propagation-based one. (C) 2004 Elsevier B.V. All rights reserved.

DOI

Parameter Estimation of Chirp Signals in Random Noise Using Wigner Distribution

Proc. 47th IEEE International Midwest Symposium on Circuits and Systems (MWSCAS 2004)   vol.Ⅱ   177 - 180   2004.07  [Refereed]

Parameter Estimation for FM-CW Radar Signals in Random Noise Using Wigner Distribution

Ijima Hiroshi, Ohsumi Akira

Proceedings of the Annual Conference of the Institute of Systems, Control and Information Engineers ( The Institute of Systems, Control and Information Engineers )  4 ( 0 ) 32 - 32   2004  [Refereed]

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The authors have recently proposed a method for estimating unknown parameters attributed to the received signal which is contaminated by random noise and, its efficacy has been tested to the real ultra sonic radar system. In this paper the method is applied to the parameter estimation for FM-CW radar signals. The effectiveness of the method is confirmed by simulation experiments.

DOI

Estimation of the parameters and angle-of-arrival of wideband signals on sensor arrays based on the phase differentiation algorithm

Djurovic, I, A Ohsumi, H Ijima

OCEANS '04 MTS/IEEE TECHNO-OCEAN '04, VOLS 1- 2, CONFERENCE PROCEEDINGS, VOLS. 1-4 ( IEEE )  Vol.2, pp.631- 634   631 - 634   2004  [Refereed]

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Phase differentiation algorithms represent a powerful tool for estimation of polynomial phase wideband signals parameters. Here, they are used for development of a suboptimal algorithm for estimation of parameters of wideband signals received on a linear sensor array. The proposed suboptimal method is considered in the case of linear FM signal and compared with the chirp-beamformer approach. The proposed approach behaves slightly worse than the later. However, the chirp beamformer requires a 3D search procedure (or genetic algorithms) that is very consuming comparing to the ID search for chirp-rate parameter and 2D search for carrier frequency and angle-of-arrival estimation. This approach can be generalized to the polynomial phase signals of higher order.

Parameter Estimation of Signals in Random Noise : An Approach Using Pseudo-Wigner Distribution

IJIMA Hiroshi, OHSUMI Akira, DJUROVIC Igor, SATO Hideaki, OKURA Hiroyuki

The Transactions of the Institute of Electronics, Information and Communication Engineers A ( The Institute of Electronics, Information and Communication Engineers )  86 ( 11 ) 1158 - 1169   2003.11  [Refereed]

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不規則雑音に埋もれた信号の検定・推定問題は,音声,通信,計測など工学の分野だけでなく医療,自然信号など様々な分野で応用されている.本論文では不規則雑音に埋もれて受信される信号の遅れ時間と伸縮パラメータを同時に推定する新しい手法を提案する.信号の伸縮が受信された信号の周波数に反映されるということに着目して,時間-周波数解析の有力な手法の一つである擬似ウィグナー分布を用い,その時間-周波数領域における確率場に対して尤(ゆう)度関数を求め,それを最大にするアルゴリズムを導出することによってこれらの未知パラメータを推定する.更に,その有効性を数値シミュレーションによって示す.