DFT Filter Research Articles

A Parallel Solution of Timing Synchronization in High-Speed Remote Sensing Data Transmission

Considering the problem that the timing synchronization calculation in high-speed remote sensing signal reception is complex and it is difficult for it to be parallel, this paper deduces and designs a parallel timing error estimation and correction scheme. This paper presents the design of polyphase DFT filter banks with non-maximum decimation. The feedforward timing error estimation and correction method is then improved to enhance synchronization performance. Finally, an implementation scheme for parallel timing error estimation and correction is proposed using the polyphase filter bank time domain decomposition method and the filter polyphase model. In the estimation module, the parallel implementation structure of the joint second-order and fourth-order cyclic statistics methods is designed, which improves the estimation accuracy. In the correction module, a fractional delay filtering method with higher accuracy is adopted in order to improve the calibration accuracy and reduce the computational complexity. The timing synchronization of a high-speed remote sensing signal with timing error is simulated and verified. The experimental results show that the parallel method proposed in this paper greatly reduces the processing speed of subband data, and has a good synchronization performance, which is close to the theoretical limit in the demodulation error rate. This paper utilizes a multi-phase DFT filter bank architecture to achieve parallel timing synchronization, which presents a novel approach for the future parallel reception of high-speed remote sensing signals.

Frequency Estimation with Co-prime DFT Filter Bank and Half Nyquist Frequency Shift

When processing wide sense stationary (WSS) signals, the sampled data rate of conventional MN bands DFT filter bank is at least MN, while the co-prime DFT filter bank (co-prime DFTFB) only needs M+N, that sharply lowers the sampling rate. Here, M and N are co-prime pair of integers. However, practical FIR filter imperfections and correlation detection mode arouse spurious peaks in signal power spectrogram, that greatly limits the application of co-prime DFTFB. In this paper, we propose a modified co-prime DFTFB by adding a real valued multiplier to the original one, where M and N are both odd numbers. Together with original co-prime DFTFB, we get a 2MN bands filter bank. In the simulation section, two BPSK signals with random carrier frequencies are employed. The results of detection probability versus SNR curves through 500 Monte Carlo experiments verify the effectiveness of our method.

A Multiple-Frequency-Taylor-Model based estimator for dynamic synchrophasor considering decaying DC component

When the grounding fault occurs in power systems, it might stimulate power oscillations and generate decaying dc component (DDC) in the fault currents. Both will cause a sharp drop in the phasor monitoring accuracy of phasor measurement units (PMUs) and even lead operational protection devices to malfunction. Thus, a multiple-frequency-Taylor-model (MFTM)-based dynamic synchrophasor estimator considering DDC (MFTDC) is proposed to overcome the adverse effect by modeling dynamic behavior and DDC. Firstly, several sub-phasors extended by the Taylor series are employed to form the dynamic fundamental component and DDC. Secondly, rough phasor estimations are obtained by DFT filters at multiple frequency bins to establish the MFTDC equation set. Then, all Taylor derivatives of MFTDC can be solved by the least square method. Finally, various IEEE standard P-class test and static/dynamic-state DDC test results of computer, PSCAD/EMTDC, and RTDS-generated signals verify that MFTDC is suitable for the protection applications of power systems. It can yield more accurate phasor estimations of PMUs than that of our previous work and shorten the convergence time to 20.3 ms or less, especially under the dynamic-state DDC scenarios.

Supraharmonic estimation by polyphase DFT filter bank

As a side effect caused by new technologies, supraharmonics attracted the attention of researchers in recent years. Mitigation and analysis of supraharmonics distortions are of great importance to guarantee the power quality (PQ) available in the power grids since such distortions can cause serious damage to the grid-connected equipment. However, there are few works in literature reporting techniques for supraharmonics analysis, and PQ equipment is rarely capable of performing measurements in the supraharmonic range. This work proposes a supraharmonic estimation system utilizing a digital filter bank through a multirate digital signal processing technique. The polyphase decomposition applied in the decimating filter bank enables the simplification of the system implementation. Both synthetic and real signals are used to evaluate the technique in analyzing supraharmonic distortions. The proposed method shows a good application prospect in accurately measuring supraharmonics and with a significant reduction in computational complexity compared with the method presented in the IEC 61000-4-30 standard.

Design of a Narrow Transition Band Dynamic Digital Channelized Receiver withoutMerging Adjacent Sub-channels

In electronic reconnaissance, effective channelization of several input signals with unknown parameters is the key requirement of the broadband receiver. Generally, since the prototype filter must be perfect, the dynamic channelized receiver based on merging adjacent sub-channels to reconstruct non-uniform filter Banks occupies too many resources and is difficult to realize. This paper proposed a sharp wideband channelized receiver based on DFT filter banks and interpolated filter banks. The proposed receiver consists of an analysis section, channel detection and arbitration section, and a synthesis section. The analysis section divides the wideband signal into multiple sub-channels. Channel detection and arbitration section output the center frequencies and the bandwidths of the input signals. In the synthesis section, after being converted to baseband, the input signals are filtered and decimated to achieve the low rate signals. The proposed method released the design of the prototype filter from the requirement of perfect reconstruction. So it is computationally efficient. Besides, time-multiplexed and interpolated filter techniques can be used to further reduce the hardware complexity. Simulation results verify the validity of the proposed method.

Development of an expert system for iron ore classification

The accurate classification of iron ores is one of the challenging tasks in the mining industry, and at the same time, it is essential for the future destination of the ores. The present study attempts to develop a machine-vision-based system, combining feature extraction, feature selection, and image classification, in order to predict the class of the iron ores. A total of 280 image features were extracted from each of the captured images of iron ores. The feature includes 10 statistical features for each component of the six colour spaces (RGB, HSI, XYZ, CMYK, Lab, and Grey) and four frequency-transformed components (DCT, DFT, DWT, and Gabor filter). In order to select the optimum feature subset for the model development, a sequential forward floating selection (SFFS) algorithm was used. The optimum feature contains only 3% of the total number of features. The support vector machine (SVM) algorithm was used for the development classification model. The four confusion matrix parameters (sensitivity, specificity, accuracy, and misclassification) along with the Q-statistics and the correlation were used as performance criteria for the model classification and were found to be 0.9792, 0.9949, 0.9918, 0.0082, 0.9999, and 0.9695, respectively. The high value of sensitivity, specificity, and accuracy and the low value of the misclassification indicate a good performance of the model. The performance of the model was also compared with other classification algorithms (k-nearest neighbours, classification tree, discriminant classifier, naive Bayes). It was observed that the proposed algorithm performs reasonably well in terms of categorising the classes of iron ore.

Resolution Doubled Co-Prime Spectral Analyzers for Removing Spurious Peaks

Co-prime DFT filter banks provide an effective tool of spectral analysis in case that the sampling rate is far lower than the Nyquist sampling rate. However, since the practical prototype filters are not ideal, spurious peaks will occur in multiple locations when the frequency offsets of the stimulus components are close to half of the frequency resolution of the co-prime spectrum. As a result, the quality of the original co-prime panorama spectrum will degrade greatly. This paper proposes a modified analyzer by synthesizing the original co-prime analyzer and a 0.5 frequency resolution shifted analyzer, which is derived from the original one by applying two simple operations on each low-rate data channel. Following some decision criterion between the original co-prime analyzer and the shifted analyzer, the synthesized analyzer not only removes all the annoying spurious peaks but also doubles the spectral resolution. Numerical comparisons are presented throughout to illustrate the above improvements.

송전선로용 디지털 고장점 표정장치에 관한 연구

Transmission line is exposed to a large area, and then faults are likely to occur than the other component of power system. When a fault occurs on a transmission line, fault locator helps fast recovery of power supply on power system. This paper deals with the design of a digital fault locator for improvement accuracy of the fault distance estimation and a fault occurrence position for transmission line. The algorithm of a fault locator uses a DC offset removal filter and DFT filter. The algorithm utilizes a fault data of GPS time synchronized. The computed fault information is transmitted to the other side substation through communication. The digital fault locator includes MPU module, ADPU module, SIU module, and a power module. The MMI firmware and software of the fault locator was implemented.

Sliding discrete Fourier transform‐based mono‐component amplitude modulation–frequency modulation signal decomposition

The sliding discrete Fourier transform-based phase locking scheme (SDFT-PLL) has been adapted for the extraction of message signals from mono-component amplitude modulation-frequency modulation (AM-FM) signals. In the proposed scheme, the SDFT-PLL is designed to track a carrier signal, which has been modulated by sinusoidal message signals both in amplitude and frequency. The sliding DFT filter is prone to windowing effect if input signal frequency drifts from centre carrier frequency. The amplitude and phase errors caused by this effect have been utilised to achieve locking condition by adaptive sampling frequency control. The SDFT-PLL is modified in such a way that the numerically controlled oscillator produces sampling pulses suitable for SDFTbin-1 and SDFT bin-0 for the retrieval of frequency and amplitude modulation signals, respectively. Hence, the frequency and amplitude variation of monocomponent AM-FM could be retrieved simultaneously. Furthermore, Bessel function-based analysis provides an insight to the removal of unwanted spectral components present in error signal. Simulation results prove the capability of PLL in message signal retrieval for large variation in frequency and amplitude. The proposed scheme is implemented in field programmable gate array to validate the performance of SDFT-PLL in decomposition of the mono-component AM-FM signal.

발전기 계자보호를 위한 계자상실 알고리즘에 관한 연구

The most widely used scheme for detecting loss of excitation on generator is used to sense apparent impedance from generator terminals. This paper presents loss of excitation algorithm using DFT filter based negative offset mho elements for generator field protection. It’s algorithm includes two negative mho characteristics looking in the generator. The generator control system was modeled by PSCAD/EMTDC software, and then the proposed algorithm was tested by the collecting relaying signals from selected the generator model. From simulation results, the loss of excitation algorithm can be used to field protection for generator.

A tree-structured DFT filter bank based spectrum detector for estimation of radio channel edge frequencies in cognitive radios

Cognitive Radio (CR) aims to provide efficient spectrum utilization in spectrum scarce wireless environments. One of the key CR functionalities is the spectrum sensing, which allows CRs to monitor the electromagnetic spectrum and detect unused bands of spectrum. Wideband spectrum sensing needs to be employed for better spectrum opportunity detection and interference avoidance both in the case of commercial and military applications. Accurate sensing needs to be employed for blocker detection in commercial systems such as LTE for the design of transmit/receive path. In military radios, the challenge lies in the robust detection of the location of the center frequencies and bandwidths of individual radio channels in the wideband input signal. In this paper, an energy detector based on tree-structured discrete Fourier transform based filter bank (TDFTFB) is proposed for detecting the edges of the channels in the spectrum. The proposed method is compared with the conventional wavelets based method for complexity and performance. The design example and simulations show that the gate count resource utilization of the proposed detection scheme is 22.9% lesser than the wavelets method at the cost of a slight degradation (0.5%) in detection accuracy. Over-the-air tests performed using Universal Software Radio Peripheral 2 (USRP2) and MATLAB/SIMULINK showed that the present method is not input specific whereas the conventional wavelet based approach depends on the spectral location of the input.

Optimization and Implementation for the Modified DFT Filter Bank Multicarrier Modulation System

The modified Discrete Fourier Transform (MDFT) filter bank is a Nearly Perfect Reconstruction (NPR) system based on pseudo-QMF filter bank. In this paper, we introduce an optimized algorithm to implement the MDFT filter bank based multicarrier modulation system. The system based on this optimization algorithm has the low error rate and system delay. To verify the designed system we have compared the MDFT multicarrier modulation system with the OFDM modulation system. The results show that at high speed communication the data error rate in the MDFT filter bank multicarrier modulation system is clear improved in comparison with OFDM systems.  Index Terms—MDFT filter bank, NPR, Error rate, Optimization algorithm

A modified weighted overlap and add-based spectral subtraction method

The Weighted Overlap and Add (WOLA) method is an optimization of frequency modulated DFT filter banks. However, the decrement in frequency resolution of speech signal caused by WOLA leads to noise estimation error. In this study, a speech power compression and gain compensation method was proposed to solve the low-resolution problem from the WOLA process. Finally, a multi-band spectral subtraction method was developed by combining the WOLA and the spectral subtraction algorithm in frequency domain. The noise reduction degrees were determined by the individual Signal-to-Noise Ratios (SNRs) in each frequency bands. Objective evaluation of the proposed noise reduction method was performed based on Itakura-Saito Distortion Measure under different types of noise. Statistical results showed that this proposed method could improve noisy speech by 5 dB SNR with little distortion. This noise reduction method has an advantage on low computation load, real-time processing and good performance on noise reduction. So this method can be implemented in embedded devices, e.g. hearing aid and cochlear implants. # This work is supported by National Natural Science Foundation of China (11104316). and Shanghai Natural Science Foundation (11ZR1446000).

Filter-bank based efficient transmission of Reduced-Guard-Interval OFDM

We propose a new way to structure the digital signal processing for reduced guard-interval (RGI) OFDM optical receivers. The idea is to digitally parallelize the processing over multiple parallel virtual sub-channels, occupying disjoint spectral sub-bands. This concept is well known in the optical or analog sub-carrier domains, but it turns out that it can also be performed efficiently in the digital domain. Here we apply critically sampled uniform analysis and synthesis DFT filter bank signal processing techniques in order to realize a novel hardware efficient variant of RGI OFDM, referred to as Multi-Sub-Band OFDM (MSB-OFDM), reducing by 10% receiver computational complexity, relative to a single-polarization version of the CD pre-equalizer. In addition to being more computationally efficient than a conventional RGI OFDM system, the signal flow architecture of our scheme is amenable to being more readily realized over multiple FPGAs, for experimental demonstrations or flexible prototyping.

Theory of Sparse Coprime Sensing in Multiple Dimensions

Coprime sampling and coprime sensor arrays have been introduced recently for the one-dimensional (1-D) case, and applications in beamforming and direction finding discussed. A pair of coprime arrays can be used to sample a wide-sense stationary signal sparsely, and then reconstruct the autocorrelation at a significantly denser set of points. All applications based on autocorrelation (e.g., spectrum and DOA estimation) benefit from this property. It was also shown in the past that coprimality can be exploited in the frequency domain by using a pair of coprime DFT filter banks, to produce the effect of a much denser tiling in the frequency domain, compared to what the two filter banks can individually achieve. This paper extends these ideas to multiple dimensions. In the 1-D case the samples or sensors lie on a pair of uniform grids, whereas in the multidimensional case, they lie on a pair of multidimensional lattices, not necessarily rectangular. This makes the developments mathematically more intricate. First several properties of coarrays of lattices are derived. It is shown how one can get dense coarrays from sparse arrays on non rectangular lattices. This requires that the lattice generating matrices M and N be commuting and coprime (to be defined). Multidimensional DFT filter banks for applications such as beamforming, with commuting coprime lattice arrays, are then described, and it is shown that a very dense tiling of the frequency plane can be obtained from the two sparse lattice arrays. A particular family of commuting coprime matrices called adjugate pairs are considered in some detail, and shown to have attractive properties. A brief review of the 1-D case is included at the beginning for convenience.

Study on Advanced Frequency Estimation Technique using Gain Compensation

Frequency is an important operating parameter for the protection, control, and stability of a power system. Thus, it must be maintained very close to its nominal frequency. Due to the sudden change in generation and loads or faults in a power system, however, frequency deviates from its nominal value. An accurate monitoring of the power frequency is essential for optimum operation and prevention of wide area blackout. Most conventional frequency estimation schemes are based on the DFT filter. In these schemes, the gain error could cause defects when the frequency deviates from the nominal value. We present an advanced frequency estimation technique using gain compensation to enhance the DFT filter-based technique. The proposed technique can reduce the gain error caused when the frequency deviates from the nominal value. Simulation studies are performed using both the data from EMTP-RV software and the user-defined arbitrary signals to demonstrate the effectiveness of the proposed algorithm. Results show that the proposed algorithm achieves good performance under both steady state tests and dynamic conditions.

Sparse Sensing With Co-Prime Samplers and Arrays

This paper considers the sampling of temporal or spatial wide sense stationary (WSS) signals using a co-prime pair of sparse samplers. Several properties and applications of co-prime samplers are developed. First, for uniform spatial sampling with M and N sensors where M and N are co-prime with appropriate interelement spacings, the difference co-array has O(MN) freedoms which can be exploited in beamforming and in direction of arrival estimation. An M -point DFT filter bank and an N-point DFT filter bank can be used at the outputs of the two sensor arrays and their outputs combined in such a way that there are effectively MN bands (i.e., MN narrow beams with beamwidths proportional to 1/MN), a result following from co-primality. The ideas are applicable to both active and passive sensing, though the details and tradeoffs are different. Time domain sparse co-prime samplers also generate a time domain co-array with O(MN) freedoms, which can be used to estimate the autocorrelation at much finer lags than the sample spacings. This allows estimation of power spectrum of an arbitrary signal with a frequency resolution proportional to 2π/(MNT) even though the pairs of sampled sequences xc(NTn) and xc(MTn) in the time domain can be arbitrarily sparse - in fact from the sparse set of samples xc(NTn) and xc(MTn) one can estimate O(MN) frequencies in the range |ω| <; π/T. It will be shown that the co-array based method for estimating sinusoids in noise offers many advantages over methods based on the use of Chinese remainder theorem and its extensions. Examples are presented throughout to illustrate the various concepts.

Joint use of DFT filter banks and modulated transmultiplexers for multicarrier communications

In this paper, new procedures for performing multicarrier modulation (MCM) are presented, based on using IFFT/FFT embedded in a filter bank-based transmultiplexer system. The IFFT/FFT blocks, along with the use of cyclic prefix or zero-padding strategies, enable easy equalization of the transmission channel. Additionally, the transmultiplexer system provides improved spectral discrimination, which can increases the transceiver robustness against mainly narrow-band interferences. Moreover, one of the proposed procedures presents an IDFT as the final stage of the MCM transmitter, and a DFT as the first stage of the receiver. This front-end is similar to standardized systems, enabling its compatibility. As it is illustrated by means of several examples, given a signal to noise ratio and comparing the new procedures to DFT-based MCM adopted in several digital communications standards, the following attractive features can be found: (a) greater spectral separation in the information transmitted over each subcarrier and (b) the robustness of the systems in noisy environments, mainly when narrow-band interferences appear, can be increased, allowing a reduction in the bit error probability.

Efficient iterative design of modified DFT filter banks

An iterative algorithm based on the Lagrange multiplier method is presented for designing modified discrete Fourier transform (MDFT) filter banks with perfect reconstruction (PR). The dominant work at each iteration is to solve a system of linear equations involving a symmetric block matrix. By exploiting its special structure, the block matrix is factored into the product of lower and upper triangular matrices, thus leading to improved efficiency. The main benefit of the proposed scheme is that it allows the efficient design of PR MDFT filter banks with a large number of channels and/or long prototype filters.

Analysis and Properties of Time-Varying Modified DFT Filter Banks

We provide a comprehensive analysis of the time-varying modified DFT (MDFT) filter bank based on the general time-varying filter bank theories (Wang, 2005, 2006, 2008, 2009) in both the time domain and frequency domain. We give firstly the description of the time-invariant MDFT filter bank including its perfect reconstruction (PR) condition, its mechanism of aliasing error cancellation and the relationship with the cosine-modulated filter bank in detail. Then, the time-varying MDFT filter bank is analyzed according to the time-domain description. Finally, the window switching method is introduced to design the prototype filter in the time-varying MDFT filter bank with examples. The error analysis shows that the introduced design approach is useful in practice.