Showing papers on "Impulse response published in 2017"

Adaptive OFDM Integrated Radar and Communications Waveform Design Based on Information Theory

Abstract: To improve the effectiveness of limited spectral resources, an adaptive orthogonal frequency division multiplexing integrated radar and communications waveform design method is proposed. First, the conditional mutual information (MI) between the random target impulse response and the received signal, and the data information rate (DIR) of frequency selective fading channel are formulated. Then, with the constraint on the total power, the optimization problem, which simultaneously considers the conditional MI for radar and DIR for communications, is devised, and the analytic solution is derived. With low transmit power, the designed integrated waveform outperforms the fixed waveform (i.e., equal power allocation). Finally, several simulated experiments are provided to verify the effectiveness of the designed waveform.

Parameter estimation for control systems based on impulse responses

Abstract: The impulse signal is an instant change signal in very short time. It is widely used in signal processing, electronic technique, communication and system identification. This paper considers the parameter estimation problems for dynamical systems by means of the impulse response measurement data. Since the cost function is highly nonlinear, the nonlinear optimization methods are adopted to derive the parameter estimation algorithms to enhance the estimation accuracy. By using the iterative scheme, the Newton iterative algorithm and the gradient iterative algorithm are proposed for estimating the parameters of dynamical systems. Also, a damping factor is introduced to improve the algorithm stability. Finally, using simulation examples, this paper analyzes and compares the merit and weakness of the proposed algorithms.

Room Impulse Response Interpolation Using a Sparse Spatio-Temporal Representation of the Sound Field

Abstract: Room Impulse Responses (RIRs) are typically measured using a set of microphones and a loudspeaker. When RIRs spanning a large volume are needed, many microphone measurements must be used to spatially sample the sound field. In order to reduce the number of microphone measurements, RIRs can be spatially interpolated. In the present study, RIR interpolation is formulated as an inverse problem. This inverse problem relies on a particular acoustic model capable of representing the measurements. Two different acoustic models are compared: the plane wave decomposition model and a novel time-domain model, which consists of a collection of equivalent sources creating spherical waves. These acoustic models can both approximate any reverberant sound field created by a far-field sound source. In order to produce an accurate RIR interpolation, sparsity regularization is employed when solving the inverse problem. In particular, by combining different acoustic models with different sparsity promoting regularizations, spatial sparsity, spatio-spectral sparsity, and spatio-temporal sparsity are compared. The inverse problem is solved using a matrix-free large-scale optimization algorithm. Simulations show that the best RIR interpolation is obtained when combining the novel time-domain acoustic model with the spatio-temporal sparsity regularization, outperforming the results of the plane wave decomposition model even when far fewer microphone measurements are available.

An Integrated Model-Data-Based Zero-Phase Error Tracking Feedforward Control Strategy With Application to an Ultraprecision Wafer Stage

Abstract: In precision motion control, well-designed feedforward control can effectively compensate the reference-induced tracking error. To achieve excellent tracking performance such as nanometer accuracy regardless of reference variations, an integrated model-data-based zero-phase error tracking feedforward control (ZPETFC) strategy is synthesized for precision motion systems with complex and nonminimum phase (NMP) dynamics. The feedforward controller comprises a conventional ZPETFC controller and a gain compensation filter structured with symmetric finite impulse response (FIR) filter. Especially, the conventional ZPETFC is predesigned based on the plant model, and consequently, the feedforward controller is parameterized by the gain compensation filter coefficients, which results in excellent capacity for approximating the inverse behavior of the complex and NMP dynamics. In order to compensate the modeling error in the conventional ZPETFC design and improve the tracking performance, a data-based instrumental-variable method with impulse response experiment is developed to obtain the optimal parameter vector under the existence of noise and disturbances. Furthermore, the ridge estimate method using singular value decomposition is employed to guarantee a fast convergent iteration in the case of ill-conditioned Hessian matrix. The proposed ZPETFC strategy enables a convex optimization procedure with the inherent stability in the iterative tuning process, and is finally implemented on a developed ultraprecision wafer stage. Comparative experimental results demonstrate that the strategy is insensitive to reference variations in comparison with iterative learning control, and outperforms preexisting model-based ZPETFC and data-based FIR feedforward control.

Filter-based regularisation for impulse response modelling

Abstract: In the last years, the success of kernel-based regularisation techniques in solving impulse response modelling tasks has revived the interest on linear system identification. In this work, an alternative perspective on the same problem is introduced. Instead of relying on a Bayesian framework to include assumptions about the system in the definition of the covariance matrix of the parameters, here the prior knowledge is injected at the cost function level. The key idea is to define the regularisation matrix as a filtering operation on the parameters, which allows for a more intuitive formulation of the problem from an engineering point of view. Moreover, this results in a unified framework to model low-pass, band-pass and high-pass systems, and systems with one or more resonances. The proposed filter-based approach outperforms the existing regularisation method based on the TC and DC kernels, as illustrated by means of Monte Carlo simulations on several linear modelling examples.

Depth resolution enhancement of terahertz deconvolution by autoregressive spectral extrapolation.

Abstract: This Letter presents a method for enhancing the depth resolution of terahertz deconvolution based on autoregressive (AR) spectral extrapolation. The terahertz frequency components with a high signal-to-noise ratio (SNR) are modeled with an AR process, and the missing frequency components in the regions with low SNRs are extrapolated based on the AR model. In this way, the entire terahertz frequency spectrum of the impulse response function, corresponding to the material structure, is recovered. This method, which is verified numerically and experimentally, is able to provide a “quasi-ideal” impulse response function and, therefore, greatly enhances the depth resolution for characterizing optically thin layers in the terahertz regime.

Novel Structure for Area-Efficient Implementation of FIR Filters

Abstract: It is observed that in multiplierless implementation of transposed direct form finite impulse response (FIR) filters, the adders in the product-accumulation block, which are called structural adders (SAs), contribute the major part of the overall logic complexity. A novel FIR filter structure is therefore proposed to reduce the hardware complexity of the product-accumulation block. In the proposed structure, half of the long word-length SAs are replaced by adders, which are called pre-SAs, which have a relatively shorter word length. The filter coefficients are carefully grouped to take advantage of the symmetric impulse response of linear phase FIR filters. Analysis and experimental results show that the overall area complexity and power consumption can be reduced at the expense of negligible delay overhead. The average area and power reduction over existing techniques can be as much as 23.8% and 25.4%. The overall area-delay performance and power-delay performance of the proposed implementation is superior to existing techniques.

Optimal design of 2-D FIR digital differentiator using $$L_1$$ L 1 -norm based cuckoo-search algorithm

Abstract: In this article, an optimal design of two-dimensional finite impulse response digital differentiators (2-D FIR-DD) with quadrantally odd symmetric impulse response is presented. The design problem of 2-D FIR-DD is formulated as an optimization problem based on the $$L_1$$ -error fitness function. The novel error fitness function is based on the $$L_1$$ norm which is unique and is liable to produce a flat response. This design methodology incorporates advantages of $$L_1$$ -error approximating function and cuckoo-search algorithm (CSA) which is capable of attaining a global optimal solution. The optimized system coefficients are computed using $$L_1$$ -CSA and performance is measured in terms of magnitude response, phase response, absolute magnitude error and elapsed time. Simulation results have been compared with other optimization algorithms such as real-coded genetic algorithm and particle swarm optimization and it is observed that $$L_1$$ -CSA delivers optimal results for 2-D FIR-DD design problem. Further, performance of the $$L_1$$ -CSA based 2-D FIR-DD design is evaluated in terms of absolute magnitude error and algorithm execution time to demonstrate their effect with variation in the control parameters of CSA.

Structural FECM: Cointegration in large-scale structural FAVAR models

Abstract: Summary Starting from the dynamic factor model for nonstationary data we derive the factor-augmented error correction model (FECM) and its moving-average representation. The latter is used for the identification of structural shocks and their propagation mechanisms. We show how to implement classical identification schemes based on long-run restrictions in the case of large panels. The importance of the error correction mechanism for impulse response analysis is analyzed by means of both empirical examples and simulation experiments. Our results show that the bias in estimated impulse responses in a factor-augmented vector autoregressive (FAVAR) model is positively related to the strength of the error correction mechanism and the cross-section dimension of the panel. We observe empirically in a large panel of US data that these features have a substantial effect on the responses of several variables to the identified permanent real (productivity) and monetary policy shocks.

Exploiting MIT Shocks in Heterogeneous-Agent Economies: The Impulse Response as a Numerical Derivative

Abstract: We propose a new method for computing equilibria in heterogeneous-agent models with aggregate uncertainty. The idea relies on an assumption that linearization offers a good approximation; we share this assumption with existing linearization methods. However, unlike those methods, the approach here does not rely on direct derivation of first-order Taylor terms. It also does not use recursive methods, whereby aggregates and prices would be expressed as linear functions of the state, usually a very high-dimensional object (such as the wealth distribution). Rather, we rely merely on solving nonlinearly for a deterministic transition path: we study the equilibrium response to a single, small "MIT shock" carefully. We then regard this impulse response path as a numerical derivative in sequence space and hence provide our linearized solution directly using this path. The method can easily be extended to the case of many shocks and computation time rises linearly in the number of shocks. We also propose a set of checks on whether linearization is a good approximation. We assert that our method is the simplest and most transparent linearization technique among currently known methods. The key numerical tool required to implement it is value-function iteration, using a very limited set of state variables.

Statistical Analysis of Time-Variant Channels in Diffusive Mobile Molecular Communications

Abstract: In this paper, we consider a diffusive mobile molecular communication (MC) system consisting of a pair of mobile transmitter and receiver nano- machines suspended in a fluid medium, where we model the mobility of the nano-machines by Brownian motion. The transmitter and receiver nano-machines exchange information via diffusive signaling molecules. Due to the random movements of the transmitter and receiver nano-machines, the statistics of the channel impulse response (CIR) change over time. We introduce a statistical framework for characterization of the impulse response of time-variant MC channels. In particular, we derive closed-form analytical expressions for the mean and the autocorrelation function of the impulse response of the channel. Given the autocorrelation function, we define the coherence time of the time-variant MC channel as a metric that characterizes the variations of the impulse response. Furthermore, we derive an analytical expression for evaluation of the expected error probability of a simple detector for the considered system. In order to investigate the impact of CIR decorrelation over time, we compare the performances of a detector with perfect channel state information (CSI) knowledge and a detector with outdated CSI knowledge. The accuracy of the proposed analytical expression is verified via particle-based simulation of the Brownian motion.

Estimation of charge, energy and polarity of noisy partial discharge pulses

Abstract: The algorithms for the computation of charge, energy and polarity of partial discharge (PD) pulses are affected by noise, which can lead to over and underestimation of the PD quantities. These quantities can be computed in time domain, frequency domain and according to the impulse response method (standard IEC270). In this paper, a theoretical study is performed in which each computation method is applied to simulated PD pulses having different waveforms and noise level to evaluate the extent of affectation on the results due to the noise. The results suggest that the error in the estimation of the PD charge is higher for oscillatory pulses regardless of the method. In contrast, the estimation of energy is more resilient to the PD waveform and the estimation in frequency domain gives rise to low error. In time domain, the charge an energy estimation method can be improved by filtering the PD pulse and integrating the output pulse to certain limits. A new method for the estimation of PD polarity is proposed based on the derivative of the filtered pulse, showing accurate estimation of the polarity even for the pulses with low signal to noise ratio.

The Harmonic Analysis of Kernel Functions

Abstract: Kernel-based methods have been recently introduced for linear system identification as an alternative to parametric prediction error methods. Adopting the Bayesian perspective, the impulse response is modeled as a non-stationary Gaussian process with zero mean and with a certain kernel (i.e. covariance) function. Choosing the kernel is one of the most challenging and important issues. In the present paper we introduce the harmonic analysis of this non-stationary process, and argue that this is an important tool which helps in designing such kernel. Furthermore, this analysis suggests also an effective way to approximate the kernel, which allows to reduce the computational burden of the identification procedure.

Gimme a break! identification and estimation of the macroeconomic effects of monetary policy shocks in the united states

Abstract: We employ a novel identi…cation scheme to quantify the macroeconomic eects of monetary policy shocks in the United States. The identi…cation of the shocks is achieved by exploiting the instabilities in the contemporaneous coe¢ cients of the structural VAR (SVAR) and in the covariance matrix of the reduced-form residuals. Dierent volatility regimes can be associated with dierent transmis- sion mechanisms of the identi…ed structural shocks. We formally test and reject the stability of our impulse responses estimated with post-WWII U.S. data by working with a break in macroeconomic volatilities occurred in the mid-1980s. We show that the impulse responses obtained with our non-recursive identi…ca- tion scheme are quite similar to those conditional on a standard Cholesky-SVARs estimated with pre-1984 data. In contrast, recursive vs. non-recursive identi…ca- tion schemes return substantially dierent macroeconomic reactions conditional on Great Moderation data, in particular as for in‡ation and a long-term interest rate. Using our non-recursive SVARs as auxiliary models to estimate a small-scale new-Keynesian model of the business cycle with an impulse response function matching approach, we show that the instabilities in the estimated VAR impulse responses are informative as for the estimation of some key-structural parameters.

Analog realization of fractional filters: Laguerre approximation approach

Abstract: Fractional filters are attractive tools for signal processing allowing greater flexibility in shaping the frequency response. Unfortunately they are very difficult to realize directly, as for digital approach they require infinite memory and for analog approach they require fully fractional capacitors or inductors. That is why approximation approaches are so attractive. In this paper we present a method of implementing an analog approximation of fractional filter. Our approach is based on Laguerre Impulse Response Approximation method. We present its modification allowing reduction of the required connections and without problems with signal-to-noise ratios. Our results are illustrated both with simulations and with experiments.

Retrieving Impulse Response Function Amplitudes From the Ambient Seismic Field

Abstract: S U M M A R Y Seismic interferometry is now widely used to retrieve the impulse response function of the Earth between two distant seismometers. The phase information has been the focus of most passive imaging studies, as conventional seismic tomography uses traveltime measurements. The amplitude information, however, is harder to interpret because it strongly depends on the distribution of ambient seismic field sources and on the multitude of processing methods. Our study focuses on the latter by comparing the amplitudes of the impulse response functions calculated between seismic stations in the Kanto sedimentary basin, Japan, using several processing techniques. This region provides a unique natural laboratory to test the reliability of the amplitudes with complex wave propagation through the basin, and dense observations from the Metropolitan Seismic Observation network. We compute the impulse response functions using the cross correlation, coherency and deconvolution techniques of the raw ambient seismic field and the cross correlation of 1-bit normalized data. To validate the amplitudes of the impulse response functions, we use a shallow Mw 5.8 earthquake that occurred on the eastern edge of Kanto Basin and close to a station that is used as the virtual source. Both S and surface waves are retrieved in the causal part of the impulse response functions computed with all the different techniques. However, the amplitudes obtained from the deconvolution method agree better with those of the earthquake. Despite the expected wave attenuation due to the soft sediments of the Kanto Basin, seismic amplification caused by the basin geometry dominates the amplitudes of S and surface waves and is captured by the ambient seismic field. To test whether or not the anticausal part of the impulse response functions from deconvolution also contains reliable amplitude information, we use another virtual source located on the western edge of the basin. We show that the surface wave amplitudes of the anticausal part agree well with those of a shallow Mw 4.7 event that occurred close to the virtual source. This study demonstrates that the deconvolution technique seems to be the best strategy to retrieve reliable relative amplitudes from the ambient seismic field in the Kanto Basin.

Hammerstein System Identification With the Nearest Neighbor Algorithm

Abstract: The nonlinear characteristic in a Hammerstein system, i.e., a system in which a nonlinear memoryless subsystem and a linear dynamic are connected in a cascade, is recovered with the nonparametric nearest neighbor regression estimate. The a priori information is nonparametric, both the nonlinear characteristic and the impulse response are completely unknown and can be of any form. Local and global properties of the estimate are examined. Whatever the probability density of the input signal, the estimate converges at every continuity point of the characteristic as well as in the global sense. We derive the asymptotic bias and variance of the proposed estimate. As a result, the optimal rate of convergence is established that additionally is independent of the shape of the input density. Results of numerical simulations are also presented.

Impulse response and spatio-temporal wave-packets: The common feature of rogue waves, tsunami, and transition to turbulence

Abstract: Here, we present the impulse response of the canonical zero pressure gradient boundary layer from the dynamical system approach. The fundamental physical mechanism of the impulse response is in creation of a spatio-temporal wave-front (STWF) by a localized, time-impulsive wall excitation of the boundary layer. The present research is undertaken to explain the unit process of diverse phenomena in geophysical fluid flows and basic hydrodynamics. Creation of a tsunami has been attributed to localized events in the ocean-bed caused by earthquakes, landslides, or volcanic eruptions, whose manifestation is in the run up to the coast by surface waves of massive amplitude but of very finite fetch. Similarly rogue waves have often been noted; a coherent account of the same is yet to appear, although some explanations have been proposed. Our studies in both two- and three-dimensional frameworks in Sengupta and Bhaumik [“Onset of turbulence from the receptivity stage of fluid flows,” Phys. Rev. Lett. 107(15), 154501...

A rapid and robust gradient measurement technique using dynamic single‐point imaging

Abstract: Purpose We propose a new gradient measurement technique based on dynamic single-point imaging (SPI), which allows simple, rapid, and robust measurement of k-space trajectory. Methods To enable gradient measurement, we utilize the variable field-of-view (FOV) property of dynamic SPI, which is dependent on gradient shape. First, one-dimensional (1D) dynamic SPI data are acquired from a targeted gradient axis, and then relative FOV scaling factors between 1D images or k-spaces at varying encoding times are found. These relative scaling factors are the relative k-space position that can be used for image reconstruction. The gradient measurement technique also can be used to estimate the gradient impulse response function for reproducible gradient estimation as a linear time invariant system. Results The proposed measurement technique was used to improve reconstructed image quality in 3D ultrashort echo, 2D spiral, and multi-echo bipolar gradient-echo imaging. In multi-echo bipolar gradient-echo imaging, measurement of the k-space trajectory allowed the use of a ramp-sampled trajectory for improved acquisition speed (approximately 30%) and more accurate quantitative fat and water separation in a phantom. Conclusion The proposed dynamic SPI-based method allows fast k-space trajectory measurement with a simple implementation and no additional hardware for improved image quality.Magn Reson Med, 2016. © 2016 International Society for Magnetic Resonance in Medicine

A Novel Model Order Reduction Technique for Linear Continuous-Time Systems Using PSO-DV Algorithm

Abstract: In this paper a new frequency-domain model order reduction method is proposed for the reduction of higher-order linear continuous-time single input single output systems using a recent hybrid evolutionary algorithm. The hybrid evolutionary algorithm is developed from the mutual synergism of particle swarm optimization and differential evolution algorithm. The objective of the proposed method is to determine an optimal reduced-order model for the given original higher-order linear continuous-time system by minimizing the integral square error (ISE) between their step responses. The method has significant features like easy implementation, good performance, numerically stable and fast convergence. Applicability and efficacy of the method are shown by illustrating an IEEE type-1 DC excitation system, and by a typical ninth-order system taken from the literature. The results obtained from the proposed algorithm are compared with many familiar and recent reduction techniques that are available in the literature, in terms of step ISE values and impulse response energies of the models. Furthermore step and frequency responses are also plotted.

Room acoustic modal analysis using Bayesian inference.

Abstract: Strong modal behavior can produce undesirable acoustical effects, particularly in recording studios and other small rooms. Although closed-form solutions exist to predict modes in rectangular rooms with parallel walls, such solutions are typically not available for rooms with even modest geometrical complexity. This work explores a method to identify multiple decaying modes in experimentally measured impulse responses from existing spaces. The method adopts a Bayesian approach working in the time domain to identify numerous decaying modes in an impulse response. Bayesian analysis provides a unified framework for two levels of inference: model selection and parameter estimation. In this context model selection determines the number of modes present in an impulse response, while parameter estimation determines the relevant parameters (e.g., decay time and frequency) of each mode. The Bayesian analysis in this work is implemented using an approximate numerical technique called nested sampling. Experimental measurements are performed in a test chamber in two different configurations. Experimentally measured results are compared with simulated values from the Bayesian analyses along with other, more classical calculations. Discussion of the results and the applicability of the method is provided.

Full proportionate functional link adaptive filters for nonlinear acoustic echo cancellation

Abstract: Nonlinear acoustic echo cancellation (NAEC) can be mainly addressed by solving two different sub-problems: the estimation of the acoustic impulse response and the modeling of the nonlinearities rebounding in it, mostly caused by the electroacoustic chain. Both the modeling processes share an important characteristic: the majority of the parameters to be estimated are very close to zero, with only a small fraction of them having non-negligible magnitude. In this paper, a novel NAEC model is proposed taking into account both the above sub-problems under a joint optimization problem. In particular, the proposed model involves two separate and parallel filters, one mainly focusing on the estimation of the acoustic impulse response (AIR) and the other one aiming at the nonlinear modeling. In order to optimize the modeling processes, both the filters are adapted by using a joint proportionate algorithm. Experimental results prove the effectiveness of the proposed model in NAEC problems.