The estimation problem of structured clutter covariance matrix (CCM) in space-time adaptive processing (STAP) for airborne radar systems is studied in this letter. By employing the prior knowledge and the persymmetric covariance structure, a new estimation algorithm is proposed based on the whitening ability of the covariance matrix. The proposed algorithm is robust to prior knowledge of different accuracy, and can whiten the observed interference data to obtain the optimal solution. In addition, the extended factored approach (EFA) is used in the optimization for dimensionality reduction, which reduces the computational burden. Simulation results show that the proposed algorithm can effectively improve STAP performance even under the condition of some errors in prior knowledge.

In the task of face recognition, a challenging issue is the one sample problem, namely, there is only one training sample per person. Principal component analysis (PCA) seeks a low-dimensional representation that maximizes the global scatter of the training samples, and thus is suitable for one sample problem. However, standard PCA is sensitive to the outliers and emphasizes more on the relatively distant sample pairs, which implies that the close samples belonging to different classes tend to be merged together. In this paper, we propose two-stage block-based whitened PCA (TS-BWPCA) to address this problem. For a specific probe image, in the first stage, we seek the K-Nearest Neighbors (K-NNs) in the whitened PCA space and thus exclude most of samples which are distant to the probe. In the second stage, we maximize the “local” scatter by performing whitened PCA on the K nearest samples, which could explore the most discriminative information for similar classes. Moreover, block-based scheme is incorporated to address the small sample problem. This two-stage process is actually a coarse-to-fine scheme that can maximize both global and local scatter, and thus overcomes the aforementioned shortcomings of PCA. Experimental results on FERET face database show that our proposed algorithm is better than several representative approaches.

Distance to target is fundamental and very important information in numerous engineering fields. Many distance measurement methods using sound use the time delay of a reflected wave, which is measured in reference to the transmitted wave. This method, however, cannot measure short distances because the transmitted wave, which has not attenuated sufficiently by the time the reflected waves are received, suppresses the reflected waves for short distances. Therefore, we proposed an acoustic distance measurement method based on the interference between the transmitted wave and the reflected waves, which can measure distance in a short range. The proposed method requires a cancellation processing for background components due to the spectrum of the transmitted wave and the transfer function of the measurement system in real environments. We refer to this processing as background components cancellation processing (BGCCP). We proposed BGCCP based on subtraction or whitening. However, the proposed method had a limitation with respect to the transmitted wave or additive noise in real environments. In the present paper, we propose an acoustic distance measurement method based on the new BGCCP. In the new BGCCP, we use the calibration of a real measurement system and the whitening processing of the transmitted wave and introduce the concept of the cepstrum to the proposed method in order to achieve robustness. Although the conventional BGCCP requires the recording of the transmitted wave under the condition without targets, the new BGCCP does not have this requirement. Finally, we confirmed the effectiveness of the proposed method through experiments in real environments. As a result, the proposed method was confirmed to be valid and effective, even in noisy environments.

We describe a co-channel interference rejection scheme that is suitable for QR-decomposition maximum likelihood detection (MLD) in multiple-input and multiple output (MIMO) systems. A pre-whitening matrix for interference rejection is decomposed into a triangular matrix and its Hermitian matrix by using a complex Gaxpy version of the Cholesky algorithm. The decomposed triangular matrix is used as a spatial pre-filter to whiten co-channel interference. Simulation results demonstrate that the proposed scheme can suppress co-channel interference streams at the cost of receive diversity order and achieves better transmission performance than QR-decomposition MLD itself in MIMO channels with co-channel interference.

An antenna-permutation (AP) scheme is described for channel-vector quantization (CVQ) in zero-forcing beamforming (ZFBF) multiuser multiple-input and multiple-output orthogonal frequency-division multiplexing systems with multiple receive antennas. Different sets of multiple channel sub-matrices are selected for different subcarriers and then quantized to multiple quantization vectors for finite rate feedback. Based on the quantization vectors, ZFBF provides a single stream or multiple streams to users while increasing frequency selectivity. Simulation results demonstrate that AP-CVQ with four-bit quantization that incorporates with pre-whitening maximum likelihood detection for two stream reception achieved better average packet error rates than minimum mean square error receive beamforming for single stream reception when the frequency selectivity was not severe.

The multiple space-time trellis codes (M-STTC) structure is one of the Multiple Input Multiple output (MIMO) schemes providing high transmission rate and diversity and coding gain without bandwidth expansion. In this paper, an M-STTC system is proposed wherein the transmitter employs a multiple space-time TCM for the fast fading channel and the receiver has a decoding order block, several whitening processors, STTC decoders, and interference cancellers. The proposed layered receiver adopts a whitening process instead of a minimum mean squared error (MMSE) estimator, which is widely used in the BLAST type receiver. An optimum decoding order scheme is also planned since it gives at least a 2 dB gain in the proposed system in the fast flat Rayleigh fading channel of fd Tc = 1/3 at the FER of 10-2. For an M-STTC system employing two STTCs with four transmit and receive antennas, the layered receiver with whitening process obtains a 5 dB gain over the coded layered space-time processing in the fast flat Rayleigh fading channel at the FER of 10-2.

Treating an averaged multiple-trials data or non-averaged single-trial data is a main approach in recent topics on applying independent component analysis (ICA) to neurobiological signal processing. By taking an average, the signal-to-noise ratio (SNR) is increased but some important information such as the strength of an evoked response and its dynamics will be lost. The single-trial data analysis, on the other hand, can avoid this problem but the SNR is very poor. In this study, we apply ICA to both non-averaged single-trial data and averaged multiple-trials data to determine the properties and advantages of both. Our results show that the analysis of averaged data is effective for seeking the response and dipole location of evoked fields. The non-averaged single-trial data analysis efficiently identifies the strength and dynamic component such as α-wave. For determining both the range of evoked strength and dipole location, an analysis of averaged limited-trials data is better option.

We newly propose a stable algorithm for blind source separation (BSS) combining multistage ICA (MSICA) and linear prediction. The MSICA is the method previously proposed by the authors, in which frequency-domain ICA (FDICA) for a rough separation is followed by time-domain ICA (TDICA) to remove residual crosstalk. For temporally correlated signals, we must use TDICA with a nonholonomic constraint to avoid the decorrelation effect from the holonomic constraint. However, the stability cannot be guaranteed in the nonholonomic case. To solve the problem, the linear predictors estimated from the roughly separated signals by FDICA are inserted before the holonomic TDICA as a prewhitening processing, and the dewhitening is performed after TDICA. The stability of the proposed algorithm can be guaranteed by the holonomic constraint, and the pre/dewhitening processing prevents the decorrelation. The experiments in a reverberant room reveal that the algorithm results in higher stability and separation performance.

This paper proposes an enhanced method for multiple description coding (MDC) using whitening transform. The MDC using correlating transform is an error resilient coding technique that explicitly adds correlation between two descriptions to enable the estimation of one set from the other when one set is dropped in channel. This paper proposes a method to overcome practical problems that decoder must know statistics of original image in the conventional correlating transform method. The MDC using whitening transform does not need additional statistical information to reconstruct a image because the coefficients whitening transformed have uni-variance statistics. Our experimental results show that the proposed method achieves a good trade-off between the coding efficiency and the reconstruction quality. We obtain that PSNR of image reconstructed from two descriptions is about 0.93 dB higher at the 1.0BPP and PSNR from only one description is about 1.88 dB higher than conventional method at the same rate of 'Lena' image.

A technique that uses a linear prediction error filter (LPEF) and an adaptive digital filter (ADF) to achieve noise reduction in a speech degraded by additive background noise is proposed. It is known that the coefficients of the LPEF converge such that the prediction error signal becomes white. Since a voiced speech can be represented as the stationary periodic signal over a short interval of time, most of voiced speech cannot be included in the prediction error signal of the LPEF. On the other hand, when the input signal of the LPEF is a background noise, the prediction error signal becomes white. Assuming that the background noise is represented as generate by exciting a linear system with a white noise, then we can reconstruct the background noise from the prediction error signal by estimating the transfer function of noise generation system. This estimation is performed by the ADF which is used as system identification. Noise reduction is achieved by subtracting the noise reconstructed by the ADF from the speech degraded by additive background noise.

Achieving optimal performance with minimal complexity are conflicting problems encountered in constructing receivers. In this paper, to solve the problem, we propose sector beamed space hopping which utilizes a Viterbi equalizing receiver. Reduction of the number of RF circuit sets, system complexity and decreasing the computational burden of the Viterbi equalizer through the use of sector beamed space hopping is presented. This is achieved using a sector beamed antenna which limits the number of paths in the multipath channel environment. This paper describes each key component which comprises the system and discusses the application of FH-SS communication. The channel is assumed to be an industrial indoor propagation channel, such as those found in a factory, where high reliability is required and many complex multipaths exist. We confirm through simulation that Viterbi equalization using less computational complexity can be obtained. It is found that there exists a trade off between system complexity and performance. Through the discussion of power consumption, cost and BER performance, we show that the proposed system achieves acceptable performance while having a low system complexity.

The blind separation of sources is a recent and important problem in signal processing. Since 1984, it has been studied by many authors whilst many algorithms have been proposed. In this paper, the description of the problem, its assumptions, its currently applications and some algorithms and ideas are discussed.

To improve the demodulated signal-to-noise ratio, SNR, for colored noise environments, we present a new direct-sequence spread-spectrum receiver system, whose construction is based on the concept of Shaped M-sequence Demodulation (SMD). This receiver has the function for shaping the local dispreading-code waveform. This method can modify the frequency transfer function from a received input to the damp-integrated output according to the power spectrum of colored noise added in the transmission process. SMD performs the combined function of a whitening filter and a matched filter, which can be used to implement an optimum receiver. For the case when the additive colored-noise power spectrum is known and the transmission channel is non-band-limited, a design theory is derived that provides the maximum SNR by choosing the best dispreading-code sequence corresponding to a given signature spreading-code sequence. The noise power component produced in the receiver damp-integrated-output is anayzed by introducing the auto-correlation matrix of the additive noise. The SNR performance of systems, one using non-optimized codes and the other using optimized codes, is examined and compared for various noise models. It is verified by analysis and computer simulation that, compared to a conventional system using non-optimized codes, remarkable SNR improvements can be achieved due to the whitening effect acquired without producing inter-symbol interference. In contrast, if a transversal whitening filter is front-ended, it produces inter-frame interference, degrading the SNR performance. The band-limiting effect of the transmission channel is also analyzed, and we confirmed that the codes optimized for the non-band-limited channel can be applied to the band-limited channel with little degradation of SNR. SMD is inherently tolerant of fast-changing noise such as fading, due to its frame-by-frame operation. Considering this function as a general demodulation scheme, it may be called "Local Code Filtering."