Whitening Filter Research Articles

Generalised polarimetric whitening filter for polarimetric MIMO radar detection

The polarimetric multiple-input multiple-output (MIMO) radar utilises both the polarisation and spatial diversities, thus the generalised likelihood ratio test (GLRT) of the polarimetric MIMO radar exhibits better detection performance than the GLRT of the MIMO radar when the secondary data set is large enough. However, the secondary data set is often not enough in reality. In this instance, the good detection performance of the GLRT of polarimetric MIMO radar cannot be guaranteed any more. In this study, the authors propose three kinds of generalised polarimetric whitening filters (GPWFs) to improve the detection performance of polarimetric MIMO radar in the limited secondary data environment. They first present the signal models and the test statistics for the three GPWFs. Then they analyse the detection performance of the three GPWFs by simulation, and find the second GPWF performs the best. They further compare the detection performance of the second GPWF with those of the GLRT and non-coherent integration (NCI) detector. Simulation results show that the proposed second GPWF outperforms the GLRT and NCI in the limited secondary data environment.

Underwater Image De-nosing using Discrete Wavelet Transform and Pre-Whitening Filter

Image denoising and improvement are essential processes in many underwater applications. Various scientific studies, including marine science and territorial defence, require underwater exploration. When it occurs underwater, noise power spectral density is inconsistent within a certain range of frequency, and the noise autocorrelation function is not a delta function. Therefore, underwater noise is characterised as coloured noise. In this study, a novel image denoising technique is proposed using discrete wavelet transform with different basis functions and a whitening filter, which converts coloured noise characteristics to white noise prior to the denoising process. Results of the proposed method depend on the following performance measures: peak signal-to-noise ratio (PSNR) and mean squared error. The results of different wavelet bases, such as Debauchies, biorthogonal and symlet, indicate that the denoising process that uses a pre-whitening filter produces more prominent images and better PSNR values than other methods.

Application of a zero-latency whitening filter to compact binary coalescence gravitational-wave searches

Joint electromagnetic and gravitational-wave (GW) observation is a major goal of both the GW astronomy and electromagnetic astronomy communities for the coming decade. One way to accomplish this goal is to direct follow-up of GW candidates. Prompt electromagnetic emission may fade quickly, therefore it is desirable to have GW detection happen as quickly as possible. A leading source of latency in GW detection is the whitening of the data. We examine the performance of a zero-latency whitening filter in a detection pipeline for compact binary coalescence (CBC) GW signals. We find that the filter reproduces signal-to-noise ratio (SNR) sufficiently consistent with the results of the original high-latency and phase-preserving filter for both noise and artificial GW signals (called "injections"). Additionally, we demonstrate that these two whitening filters show excellent agreement in $\chi^2$ value, a discriminator for GW signals.

Reduced-Complexity Equalization for Faster-Than-Nyquist Signaling: New Methods Based on Ungerboeck Observation Model

In this paper, we consider the detection of faster-than-Nyquist (FTN) signaling. By noticing that the whitening filter for FTN signaling cannot be directly derived when the symbol rate exceeds the signal bandwidth, we propose a new reduced-complexity M-algorithm BCJR (M-BCJR) algorithm based on the Ungerboeck observation model. By taking some “future” symbols into account, the proposed algorithm is able to select the $M$ best states in the maximum a posteriori sense. We further simplify the above algorithm by choosing the key path from each possible state, which successfully reduces the complexity while maintaining a good bit error rate performance. Simulation results show that, with the use of the proposed methods, great gains can be obtained in terms of spectral efficiency (up to 186%) or signal-to-noise ratio (up to 4.5 dB) compared with the Nyquist signaling.

Single-protein detection in crowded molecular environments in cryo-EM images.

We present an approach to study macromolecular assemblies by detecting component proteins' characteristic high-resolution projection patterns, calculated from their known 3D structures, in single electron cryo-micrographs. Our method detects single apoferritin molecules in vitreous ice with high specificity and determines their orientation and location precisely. Simulations show that high spatial-frequency information and-in the presence of protein background-a whitening filter are essential for optimal detection, in particular for images taken far from focus. Experimentally, we could detect small viral RNA polymerase molecules, distributed randomly among binding locations, inside rotavirus particles. Based on the currently attainable image quality, we estimate a threshold for detection that is 150 kDa in ice and 300 kDa in 100 nm thick samples of dense biological material.

등척성 일정 자의 수축 시에 기록한 표면근전도 신호에 대한 근전도 진폭 추정기의 정량적 분석

The EMG amplitude estimator, which has been investigated as an indicator of muscle force, is utilized as the control input to artificial prosthetic limbs. This paper describes an application of the optimal EMG amplitude estimator to the surface EMG signals recorded during constant isometric %MVC (maximum voluntary contraction) for 30 seconds and reports on assessing performance of the amplitude estimator from the application. Surface EMG signals, a total of 198 signals, were recorded from biceps brachii muscle over the range of 20-80%MVC isometric contraction. To examine the estimator performance, a SNR(signal-to-noise ratio) was computed from each amplitude estimate. The results of the study indicate that ARV(average rectified value) and RMS(root mean square) amplitude estimation with forth order whitening filter and 250[㎳] moving average window length are optimal and showed the mean SNR improvement of about 50%, 40% and 20% for each 20%MVC, 50%MVC and 80%MVC surface EMG signals, respectively.

Adaptive filtering‐based multi‐innovation gradient algorithm for input nonlinear systems with autoregressive noise

SummaryIn this paper, by means of the adaptive filtering technique and the multi‐innovation identification theory, an adaptive filtering‐based multi‐innovation stochastic gradient identification algorithm is derived for Hammerstein nonlinear systems with colored noise. The new adaptive filtering configuration consists of a noise whitening filter and a parameter estimator. The simulation results show that the proposed algorithm has higher parameter estimation accuracies and faster convergence rates than the multi‐innovation stochastic gradient algorithm for the same innovation length. As the innovation length increases, the filtering‐based multi‐innovation stochastic gradient algorithm gives smaller parameter estimation errors than the recursive least squares algorithm.

Improved underwater signal detection using efficient time–frequency de-noising technique and Pre-whitening filter

Optimal signal detection is important in sonar and underwater digital communication. However, a detailed knowledge of the statistics of the noise present is required to achieve optimal signal detection. Additive white Gaussian noise (AWGN) is assumed in many applications; thus, a linear correlator (LC), which is known to be optimal in the presence of AWGN, is normally used. However, underwater acoustic noise (UWAN) influences the reliability of signal detection in applications, in which the noise is non-white and non-Gaussian. As a result, an LC detector performs poorly in underwater applications. Accordingly, the Gaussian noise injection detector (GNID) is proposed in this study to improve detection probability (PD) based on a noise-enhanced signal detection using a pre-whitening filter, a time–frequency de-noising method based on S-transform, and an inverse whitening filter. Sea-truth data are collected at Desaru Beach on the eastern shore of Johor in Malaysia using broadband hydrophones. These data are used as UWAN to validate the proposed method. The performances of four different detectors, namely, the proposed GNID, a locally optimal (LO) detector, a sign correlation (SC) detector, and a conventional LC detector, are evaluated according to their PD values. Given a false alarm probability of 0.01 and PD value of 90%, the energy-to-noise ratios of the GNID are better than the LO, SC, and LC detectors by 1.77, 3.81, and 3.61dB, respectively, for a time-varying signal.

High-performance beamformer and low-complexity detector for DF-based full-duplex mimo relaying networks

In this paper, we consider a full-duplex multiple-input multiple-output (MIMO) relaying network with the decode-and-forward (DF) protocol. Due to the full-duplex transmissions, the self-interference from the relay transmitter to the relay receiver degrades the system performance. We thus propose an iterative beamforming structure (IBS) to mitigate the self-interference. In this method, the receive beamforming at the relay is optimized to maximize the signal-to-interference- plus-noise-ratio (Max-SINR), while the transmit beamforming at the relay is optimized to maximize the signal-to-leakage-plusnoise- ratio (Max-SLNR). To further improve the performance, the receive and transmit beamforming matrices are optimized between Max-SINR and Max-SLNR in an iterative manner. Furthermore, in the presence of the residual self-interference, a low-complexity whitening-filter (WF) maximum likelihood (ML) detector is proposed. In this detector, a WF is designed to transform a colored interference- plus-noise to a white noise, while the singular value decomposition is used to convert coupled spatial subchannels to parallel independent ones. From simulations, we find that the proposed IBS performs much better than the existing schemes. Also, the proposed low-complexity detector significantly reduces the complexity of the conventional ML (CML) detector from exponential time (an exponential function of the number of the source transmit antennas) to polynomial one while achieving a slightly better BER performance than the CML due to interference whitening.

Artificial bandwidth extension using deep neural network‐based spectral envelope estimation and enhanced excitation estimation

The authors propose a robust artificial bandwidth extension (ABE) technique to improve narrowband (NB) speech signal quality using an enhanced spectrum envelope and excitation estimation. For envelope estimation, they propose an enhanced envelope estimation method using a deep neural network with multiple layers. For excitation estimation, they use a whitened NB excitation signal that is generated by passing the excitation signal through a whitening filter. An adaptive spectral double shifting method is introduced to obtain an enhanced wideband (WB) excitation signal. The proposed ABE system is applied to the decoded output of an adaptive multi-rate (AMR) codec at 12.2 kbps. They evaluate its performance using log spectral distortion, a WB perceptual evaluation of speech quality, and a formal listening test. The objective and subjective evaluations confirm that the proposed ABE system provides better speech quality than AMR at the same bit rate.

Eigen Domain Interference Rejection Combining Algorithm for MIMO Systems

In this letter, we investigate an eigen domain interference rejection combining (E-IRC) algorithm for multiple-input–multiple-output (MIMO) systems suffering from narrowband interference (NBI). The received signals are transformed into the eigen domain, which is obtained by eigenvalue decomposition of the time domain autocorrelation matrix of the NBI, and the interference is rejected in the eigen domain via multiple antennas. V-BLAST and Alamouti schemes are considered in this letter. For the V-BLAST scheme, the E-IRC algorithm is an extension of the one for SIMO system. For the Alamouti scheme, we redesign the transmission codeword, which can preserve the orthogonality in the eigen domain, and propose an NBI rejection algorithm based on the spatial whitening filter.

On log‐cumulants of multilook polarimetric whitening filter for polarimetric SAR data

Many multivariate statistical distributions have been derived using the well-known product model to stochastically model PolSAR data. One important factor in their utilisation is the estimation of their texture parameters. The method based on the second-order matrix log-cumulants for multilook PolSAR statistical distributions results in estimators with low bias and variance properties. Recently, it has been shown that the method of multivariate fractional moment (MOMFM) has an even lower mean-square error (MSE) than the method of multivariate log-cumulants-based estimators, which is based on fractional moments of the multilook polarimetric whitening filter (MPWF). However, it performs worse in G0-Wishart distribution especially the shape parameter is small. In this study, the authors propose new estimation methods based on log-cumulants of the MPWF. One method is based on the first-order log-cumulants of the MPWF, which is suitable for K-distribution case; the other method is based on the second-order log-cumulants of the MPWF, which is suitable for G0-distribution case. The bias, variance, MSE and computational time of the new estimators are all lower than the MOMFM estimator. Comparisons are made among proposed methods and some popular methods using simulated and real PolSAR data. The results show that new estimators have outstanding performance among the popular estimators. It is worth to mentioning that the proposed estimators can be easily derived for all commonly occurring multilook PolSAR distributions.

The design and research of anti-color-noise chaos M-ary communication system

Previously a novel chaos M-ary digital communication method based on spatiotemporal chaos Hamilton oscillator has been proposed. Without chaos synchronization circumstance, it has performance improvement in bandwidth efficiency, transmission efficiency and anti-white-noise performance compared with traditional communication method. In this paper, the channel noise influence on chaotic modulation signals and the construction problem of anti-color-noise chaotic M-ary communication system are studied. The formula of zone partition demodulator’s boundary in additive white Gaussian noise is derived, besides, the problem about how to determine the boundary of zone partition demodulator in additive color noise is deeply studied; Then an approach on constructing anti-color-noise chaos M-ary communication system is proposed, in which a pre-distortion filter is added after the chaos baseband modulator in the transmitter and whitening filter is added before zone partition demodulator in the receiver. Finally, the chaos M-ary communication system based on Hamilton oscillator is constructed and simulated in different channel noise. The result shows that the proposed method in this paper can improve the anti-color-noise performance of the whole communication system compared with the former system, and it has better anti-fading and resisting disturbance performance than Quadrature Phase Shift Keying system.

Spectral Efficient Cognitive Radio Transmission using Full Duplex Communication

Background/Objectives: The rapid growth of demand for ever-increasing data rate and wireless services results in spectrum scarcity. Cognitive radio technology is used as solution for this problem. Methods/Statistical Analysis: Spectrum sensing is the key mechanism used to share the spectrum in cognitive radio which will solve the spectrum scarcity problem. Conventional cognitive radio is operated in half-duplex mode which fist sense the spectrum hole and transmit if spectrum hole is found. This listen and talk approach has the constraint of losing transmission opportunities during sensing or has the Risk of undetectable collisions during transmission. This problem can be rectified by operating the cognitive radio in full duplex mode. Findings: Modern full-duplex wireless radio transceivers will be capable of simultaneous spectrum sensing and transmission. But one of the main issues in the full duplex simultaneous sensing and transmission is the residual self-interference.In this paper a full duplex cognitive radio with two antennas (2x2 MIMO) which will provide for higher physical isolation is proposed with the self-interference cancelling technique. The technique consists of a power control and interference cancellation filter or self-interference whitening filter. Since the full-duplex system, radios transmit and sense simultaneously in the same frequency band at the same time, providing improvement in spectral efficiency over a half-duplex system. Applications/Improvements: The result shows that the proposed full duplex cognitive radio provides spectral efficiency of 5.2 bits/Hz/sec at 11dB SNR but the conventional half duplex cognitive radio provides only 2 bits/Hz/sec.

The Extended Polarimetric Whitening Filter and Its Application to Target Detection in Polarimetric Synthetic Aperture Radar Images

In this letter, we propose the extended polarimetric whitening filter (EPWF) for target detection in polarimetric synthetic aperture radar (PolSAR) images. We adopt finite mixture models fitted by the expectation-maximization algorithm for modeling PolSAR clutter. Specifically for single-look and multilook PolSAR data, the Gaussian mixture model (GMM) and Wishart mixture model (WMM) are applied, respectively. At the core of the EPWF, we present the closed-form solution to the optimal weighting matrix that minimizes the speckle of filtered clutter. We also derive the null hypothesis distribution of the EPWF detector to determine the detection threshold given a false alarm rate. The effectiveness of the EPWF is evaluated on both simulated and real PolSAR data. Experimental results demonstrate that the GMM and WMM are suitable for PolSAR clutter with channel-dependent texture. Compared with alternative detection statistics, the speckle level of the EPWF is lower in terms of the standard-deviation-to-mean ratio, which leads to a preferable target detection performance of the EPWF detector.

Joint Transceiver Design for Non-Concurrent MIMO Two-Way AF Relaying

We consider an amplify-and-forward infrastructure non-concurrent two-way relaying (ncTWR) scenario where a base station serves a transmit-only user TUE and a receive-only user RUE. Unlike conventional two-way relaying, the RUE in ncTWR experiences back-propagating interference (BI). Uniform channel decomposition and Tomlinson–Harashima precoding improve the bit error rate (BER) of multiple-antenna–multiple-output (MIMO) systems. Inspired by these techniques, we design a transceiver to reduce BER of both users in MIMO ncTWR. This transceiver cancels BI of the RUE and uses spatial noise whitening filters, designed herein, to uniformly decompose MIMO channels of both TUE and RUE. This letter quantifies the improved BER of the proposed transceiver.

Optimal receive filter design under similarity constraint in coloured noise

This study considers the design of optimal receive filter under similarity constraint in coloured noise and compares it with signal design methods and conventional schemes. Similarity constraint restricts the sought-after filter to be close to the transmit waveform to control the output sequence envelope, avoiding potential risks of poor range solution and high sidelobes of whitening filter (WF) in severe coloured noise. The detailed solution to this constrained optimisation problem is provided. Then, the authors compare the filter design (FD) with the schemes of conventional or designing signal based on the matched/whitening filter. Five schemes are analysed on their performances of output signal-to-noise ratio, sequence control and requirements for practical applications. The merits of five schemes are concluded. In simulations, comparison between the designed filter and whitening filter illustrates the necessity of similarity constraint. FD under similarity constraint is regarded as a robust scheme for output sequence envelope control, easy to implement for coloured noise suppression.

Parameter estimation for polarimetric synthetic aperture radar imagery based on thezr log zmoments

The K and G0 distributions are widely used to establish models for polarimetric synthetic aperture radar (PolSAR) data. The estimation of their texture parameters is an important factor in their utilization. Traditionally, the method of matrix log cumulants (MoMLC) is adopted for its low bias and variance properties. Recently, the MFM, which is an estimator based on fractional moments of the multilook polarimetric whitening filter (MPWF), is exploited due to its lower mean square error. However, these estimators are implemented by solving the implicit equations, which are computationally complicated. We propose two new estimators based on the z r log z moments for each of the K and G0 distributions. Both estimators have analytical expressions, which allow rapid calculations. Using the simulated data, comparisons about the accuracy and speed are presented to demonstrate the performance of our estimators. The results show that the proposed estimators yield a faster calculating speed while retaining the accuracy. Finally, a goodness-of-fit test based on MLCs has been used to assess the fitting accuracy of the estimators for real PolSAR data, and the results are according to those from the simulated data.

Analysis of signal detection SNR limits in snapshot-deficient scenarios with colored noise

Most source enumeration algorithms assume a white noise background; however, in many underwater applications, the background noise is colored. Various researchers have investigated the problem of source enumeration when the background noise is colored. The standard approach is to apply a whitening filter before the signal enumeration algorithm. Most of these algorithms perform poorly when the number of noise snapshots used to estimate the whitening filter is small. Nadakuditi and Silverstein [IEEE J. Sel. Topics Signal Process., 2010] developed an algorithm for source enumeration using random matrix theory that provides the fundamental SNR limits for snapshot-deficient scenarios with arbitrary noise. Nadakuditi and Silverstein's analysis focuses on the performance when the number of signal plus noise snapshots varies while the number of snapshots used for the whitening filter stays constant. This talk analyzes the performance of this algorithm when number of snapshots available to estimate the whitening filter varies from N to 10 N. Simulations using two different colored noise models for a large vertical linear array located in the deep ocean will be presented.

Environmentally corrected matched filter

Matched filtering is commonly used to process broadband active acoustic echoes providing higher levels of range resolution and reverberation noise suppression than can be realized through narrowband processing. Since theoretical processing gains are proportional to the signal bandwidth, it is typically desirable to utilize the widest band signals possible. However, as signal bandwidth increases, so do environmental effects that increase decorrelation between the received echo and the transmitted waveform. This loss of coherence often results in processing gains and range resolution much lower than theoretically predicted. Weiner filtering, commonly used in image processing to improve distorted and noisy photos, was investigated as an approach to correct for these environmental effects. This improved signal processing, environmentally corrected matched filter (ECMF), first uses a Wiener filter to estimate the environmental transfer function and then again to correct the received signal. This process can be viewed as a “smarter” inverse or whitening filter that optimally adjusts according to the signal to noise ratio across the spectrum. Sonar simulation toolset (SST) synthetic data and measured in-air data will be presented to illustrate the improved processing.