Nari Tanabe

Restoration method for degraded images using two-dimensional block Kalman filter with colored driving source

This paper proposes a robust image restoration method using two-dimensional block Kalman filter with colored driving source. This method aims to achieve high quality image restoration for blur and noise disturbance from the canonical state space models with (i) a state equation composed of the original image, and (ii) an observation equation composed of the original image, blur, and noise. The remarkable feature of the proposed method is realization of high performance image restoration without sacrificing original image despite simple image restoration using only Kalman filter algorithm, while many conventional methods based on the Kalman filter theory usually perform the image restoration, using the parameter estimation algorithm of AR(auto regressive) system and the Kalman filter algorithm. We show the effectiveness of the proposed method using numerical results and subjective evaluation results.

Practical OFDM channel estimation method with Kalman filter theory

This paper proposes the practical OFDM channel estimation method with only Kalman filter theory. The method aims to achieve robust OFDM channel estimation from the canonical state space models with (i) a state equation composed of the channel gain and (ii) an observation equation composed of the pilot signal, the channel gain, and the additive noise. The remarkable features of this method are that (1) the channel gain estimation accuracy does not depend on the parameter of the AR system, (2) the maximum Doppler frequency is unknown, and (3) the proposed method can estimate the channel gain by low computational complexity, while many conventional methods based on the Kalman filter theory usually perform the channel gain estimation using the parameter estimation algorithm of the AR (auto-regressive) system and the Kalman filter algorithm. Numerical examples show the effectiveness of the proposed method.

Noise Suppression with High Speech Quality Based on Kalman Filter

We propose a noise suppression algorithm based on Kaiman filter. The algorithm achieves a noise suppression with high speech quality under the condition of the AWGN (additive white Gaussian noise), from the canonical state space models with a state equation composed of the clean speech signal and an observation equation composed of the clean speech signal and AWGN. The special feature of the proposed algorithm is realization of high speech quality noise suppression utilizing only the Kalman filter, while the conventional algorithm utilizes the linear prediction algorithm and the Kalman filter. The simulation results show that the proposed method improved the noise suppression capability by about 5 dB than that of the conventional method

Extraction method of accompaniment signal using stereo type noise suppression method

This paper presents the stereo type noise suppression method for extracting the accompaniment signal from the stereo music signal. The conventional method extracts the accompaniment signal from the stereo music signal using the spectrum ratio. However, this method loses the realism of the stereo music since the conventional method utilizes the difference signal of the left and right channels with reduced the musical noise. The proposed method has the remarkable features that (i) the stereo accompaniment signal with the realism can be extracted and that (ii) the proposed method is reduced computational complexity. The effectiveness of the proposed method is shown by the computer simulation.

Show-through cancellation using Kalman filter with colored driving source in duplex scanning

We present a show-through cancellation method using the Kalman filter with colored driving source in duplex scanning. The remarkable feature of the proposed method is reduced show-through effect without sacrificing quality of the original image using the canonical state space model with (i) a state equation composed of the front-side original image, and (ii) an observation equation composed of the front and back side original images, transmittance of paper, and show-through point spread function. Simulation result shows that this proposed method can remove show-through effectively.

Noise suppression algorithm using Kalman filter with reduced computational complexity

We propose a noise suppression algorithm using Kalman filter with colored driving source. The algorithm aims to achieve robust noise suppression with reduced computational complexity by modifying the canonical state space models in [4]. The remarkable features of the proposed algorithm are that it can be realized by 3 multiplications and that it has the better performances compared with [2–4] despite the reduction of computational complexity, using only the Kalman filter algorithm for the proposed canonical state space models with the colored driving source: (i) a vector state equation is composed of the only speech signal, and (ii) a scalar observation equation is composed of speech signal and additive noise. We have confirmation of validity of the proposed canonical state space models with the colored driving source, and also show the effectiveness through numerical results and subjective evaluation results.

Affine Projection Type Blind Multiuser Detection

We propose an affine projection type multiuser detection for the DS-CDMA (direct sequence code division multiple access) system. This proposed algorithm estimates the desired information symbols using the conventional method based on affine projection algorithm [5], and then modifies the constraints of the conventional method in order to achieve the good MAI (multiple access interference) suppression capability of the blind multiuser detection. The remarkable feature of the proposed algorithm is realization of the same performance as those of the optimum linear MMSE (minimum mean square error) multiuser detector based on MOE (minimum output energy) criterion [2]. Numerical examples show the effectiveness of the proposed algorithm.

A Kalman Filter based Fast Noise Suppression Algorithm

We have proposed a robust noise suppression algorithm with Kalman filter theory [7]. In this paper, we propose a fast noise suppression algorithm by modifying the canonical state space model in [7]. The algorithm aims to achieve robust noise suppression with reduced computational complexity without sacrificing high quality of speech signal. The remarkable features of the proposed algorithm are that it can be realized by 3 multiplications and that it has the same performances or better ones compared with [7] despite the reduction of computational complexity under the same environments, using only the Kalman filter algorithm for the proposed canonical state space model with the colored driving source: (i) a vector state equation is composed of the only speech signal, and (ii) a scalar observation equation is composed of speech signal and additive noise. We have confirmation of validity of the proposed canonical state space model with the colored driving source, and also show the effectiveness through numerical results and subjective evaluation results.

Practical Wireless LAN Systems with Simple Tracking Algorithm for Fast Fading Environment

This paper proposes practical wireless LAN (local area network) systems which can be applied in a high speed mobile communication environment. These tracking algorithms have good performance using pilot signals arrangement and linear/parabolic interpolation approximation, under the conditions of both 2-ray and 18-ray Rayleigh fading model with a maximum Doppler frequency of 500 Hz or 1000 Hz. The special features of the proposed algorithms are (1) practical method in IEEE 802.11a and IEEE 802.11g, and (2) simple tracking algorithms for fast fading environment without sacrificing the transmission efficiency. Performance evaluation shown, with the proposed methods, it is possible to improve the average BER (bit error rate) by about 10 times for 2-ray Rayleigh fading model and about 4~5 times for 18-ray Rayleigh fading model without sacrificing the transmission efficiency