Matched Filter Theory Research Articles

An Efficient Algorithm for De-Interleaving Staggered PRI Signals

Resolution and mapping bandwidth are the two most important image performance indicators that reflect satellite synthetic aperture radar (SAR) imaging reconnaissance capability. The PRI-staggered signal can simultaneously achieve high resolution in azimuth and wide swath during SAR imaging, and is an important signal form of SAR. It is important for anti-SAR reconnaissance to de-interleave the staggered PRI signal from the mixed signals. To address the problem that the existing staggered signal de-interleaving algorithms cannot accommodate PRI jitter and are computationally inefficient, this paper proposes an efficient algorithm for de-interleaving staggered PRI signals. A clustering-based square sine wave interpolation method and a threshold criterion are proposed, improving computational efficiency while suppressing interference between sub-PRIs and the frame period of the staggered PRI signal. In addition, a sequence retrieval algorithm incorporating matched filter theory is proposed to improve the separation accuracy of radar pulse sequences. The simulation shows that the novel algorithm can adapt to PRI jitter and de-interleave staggered PRI signals from mixed signals with high efficiency. Compared with the existing staggered signal de-interleaving algorithm, the computational efficiency is improved by an order of magnitude.

Data synchronization for gas emission measurements from dairy cattle: A matched filter approach

Methane emissions from enteric fermentation in dairy cows make a substantial contribution to the greenhouse gas problem. Sniffer approaches represent a popular low-cost, mobile technology for cow gas emission measurement in industrial installations owing to their providing reliable emission estimates. Despite its advantages, the sniffer approach often yields data inconsistencies during automated acquisition due to unsynchronized clocks between sniffers and associated automatic milking machines, leading to uncertainty in the linking of each animal’s data with methane emission records. Given the constantly growing demand for large-scale methane emission measurements for genetic studies, sniffer installations are expected to increase, making the need for an efficient solution to the data synchronization issue prescient. A novel approach for handling the synchronization problem was developed in this study based on matched filter theory. The approach was verified on gas emission data from multiple commercial dairy farms in Denmark. The results were analyzed and discussed in terms of accuracy and general characteristics of computational performance irrespective of a specific software implementation. The present findings support the conclusion that the presented matched filter-based approach is robust, applicable to the problem of cattle gas emission data synchronization, and convenient for automated data processing.

Optimization of matched filters for VHR image segmentation

ABSTRACT The availability of very high resolution remotely sensed images has made the use of spectral information alone insufficient for class recognition, so the integration of spatial information into the segmentation task becomes necessary. Spatial information takes into account the surrounding of a pixel, rather than dealing with a pixel as an isolated item. In this paper, inspired by matched filtering theory, we propose a new technique for spatial feature extraction. The technique consists of using 2D kernels convolved with the spectral bands. The convolution operation bears valuable spatial information about the pixel under consideration. The concatenation of the extracted spatial features and spectral features of pixels feeds a support vector machine (SVM) to segment the image of interest. To cope with the complexity of images having objects of varying sizes (i.e. urban areas), we adopted a hierarchical strategy. That is, kernels with increasing size are applied to extract different spatial features corresponding to different objects. Thus, we have extended the use of matched filters from single object enhancement to multi-object enhancement. A challenging step in designing the matched filters is the two-dimensional kernels coefficients selection, which we formulated as an optimization problem within a particle swarm optimization framework. The optimization process was driven by two different fitness functions, the cross-validation SVM accuracy and the Bhattacharyya distance, which were both evaluated on training samples. We assessed the proposed procedure on two very high-resolution images having different spatial resolutions. The obtained results showed significant improvement in terms of the overall classification accuracy (over 10% for both images). Moreover, visual inspection of the segmented images, in addition to pepper and salt elimination, revealed significant improvement in many objects not detected by the spectral method. Our method of extracting spatial features seems to be very efficient.

Design of optimum filters for signal processing with silicon drift detectors

In order to improve noise filtering for high‐resolution X‐ray spectroscopy using silicon drift detectors, optimum finite impulse response filters are calculated and tested experimentally. Common matched filter theory cannot be applied for this problem since the filters need to fulfill several requirements regarding their time domain transfer function, like, for example, the presence of a flat‐top, zero‐filter area. Therefore, the utilization of digital penalized least mean square method with silicon drift detectors is presented. Adaptations to the method are applied in order to suit the considered application targeting low dead‐time at high count rates using modern silicon drift detectors with fast charge amplifiers. The workflow for filter calculation is presented: Signal data are acquired using a spectroscopy setup in order to obtain pulse shape and noise information. Desired constraints regarding the time‐domain transfer function of the filters are written out, and limits for the precision of their fulfillment are derived. Optimization is carried out, and resulting filters are presented. In order to experimentally test the calculated filters, implementation in hardware is done. 55Fe spectra are acquired and evaluated. Noise reduction is rated by calculation of residual electronic noise in the recorded 55Fe spectra. Comparison to the noise reduction of trapezoid filters is done. Improvement up to (5.2 ± 0.3)% was found using the calculated optimum filter, proving the successful utilization of digital penalized least mean square method for signal processing with modern silicon drift detectors.

A matched filter based approach for high-energy estimation in calorimetry

In high-energy calorimeters, a crucial task is the reconstruction of the energy deposited by particle interactions. Standard techniques used in modern calorimeters rely on the energy estimation to select the signal with relevant information. This work presents a new approach, which performs the signal detection against noise as a first step, followed by the energy estimation task. The method is fully based on the Matched Filter (MF) theory, which is known to produce the optimum detection efficiency with respect to the signal-to-noise ratio. Furthermore, the MF output can be calibrated to estimate the signal amplitude and, thus, the energy. The proposed method is compared to different optimum filtering algorithms, which are currently being used for energy reconstruction in modern calorimeter systems. The results from simulated data show that the proposed method achieves better performance in terms of both signal detection efficiency and estimation error.

A Combined Filter for Synchronous Detection in Underwater Acoustic Mobile Communication System

A scale-stretching combined filter is proposed for underwater acoustic mobile communication receivers. The combined filter consists of the sum of signals with different time stretching and shifting. Based on the matched filtering theory, Doppler stretching scales, time shift, and the order of each component of the combined filter are derived. The method proposed in this paper improves the matching degree of the filter with wideband Doppler echo signals with different moving speeds. Simulation results show that the combined filter proposed in this paper can improve the detection probability and synchronization time estimation accuracy of wideband Doppler echo. In addition, the combined filter proposed in this paper has the same calculation amount as the matched filter. This feature makes this filter suitable for the synchronous detection of underwater acoustic mobile communication with real-time operation and low power consumption requirements.

A Communication Theoretic Interpretation of Modern Portfolio Theory Including Short Sales, Leverage and Transaction Costs

Modern Portfolio Theory is the ground upon which most works in portfolio optimization context find their foundations. Many studies attempt to extend the Modern Portfolio Theory to include short sale, leverage and transaction costs, features not considered in Markowitz’s seminal work from 1952. The drawback of such theories is that they complicate considerably the simplicity of the original technique. Here, we propose a simple and unified method, which takes inspiration from, and shows connections with the matched filter theory in communications, to evaluate the best portfolio allocation with the possibility of including a leverage factor and short sales. Finally, we extend the presented method to also consider the transaction costs.

An Improved Azimuth Angle Estimation Method with a Single Acoustic Vector Sensor Based on an Active Sonar Detection System.

In this paper, an improved azimuth angle estimation method with a single acoustic vector sensor (AVS) is proposed based on matched filtering theory. The proposed method is mainly applied in an active sonar detection system. According to the conventional passive method based on complex acoustic intensity measurement, the mathematical and physical model of this proposed method is described in detail. The computer simulation and lake experiments results indicate that this method can realize the azimuth angle estimation with high precision by using only a single AVS. Compared with the conventional method, the proposed method achieves better estimation performance. Moreover, the proposed method does not require complex operations in frequency-domain and achieves computational complexity reduction.

Radar Imaging of Sidelobe Suppression Based on Sparse Regularization

Synthetic aperture radar based on the matched filter theory has the ability of obtaining two-di- mensional image of the scattering areas. Nevertheless, the resolution and sidelobe level of SAR imaging is limited by the antenna length and bandwidth of transmitted signal. However, for sparse signals (direct or indirect), sparse imaging methods can break through limitations of the conventional SAR methods. In this paper, we introduce the basic theory of sparse representation and reconstruction, and then analyze several common sparse imaging algorithms: the greed algorithm, the convex optimization algorithm. We apply some of these algorithms into SAR imaging using RadBasedata. The results show the presented method based on sparse construction theory outperforms the conventional SAR method based on MF theory.

New digital demodulator with matched filters and curve segmentation techniques for BFSK demodulation: Analytical description

<p class="Abstractandkeywordscontent">The present article relates in general to digital demodulation of Binary Frequency Shift Keying (BFSK). The objective of the present research is to obtain a new processing method for demodulating BFSK-signals in order to reduce hardware complexity in comparison with other methods reported. The solution proposed here makes use of the matched filter theory and curve segmentation algorithms. This paper describes the integration and configuration of a Sampler Correlator and curve segmentation blocks in order to obtain a digital receiver for a proper demodulation of the received signal. The proposed solution is shown to strongly reduce hardware complexity. In this part a presentation of the proposed solution regarding the analytical expressions is addressed. The paper covers in detail the elements needed for properly configuring the system. In a second part it is presented the implementation of the system for FPGA technology and the simulation results in order to validate the overall performance.</p><br /><div id="__if72ru4sdfsdfruh7fewui_once" style="display: none;"> </div><div id="__hggasdgjhsagd_once" style="display: none;"> </div>

Linearized Aeroelastic Gust Response Analysis of a Launch Vehicle

Amethodology for aeroelastic gust response of launch vehicles inflight is developedbyusing apreviously validated aeroelastic stability analysis. The effects on the aeroelastic vibrations of a launch vehicle encountering a wind gust while operating at a particular supersonic flight condition are determined. A linearized dynamic aeroelastic analysis of the launch vehicles is performed using the previously developed procedure, but this time in supersonic flow. Then, the pressure on the moving launcher surface resulting from the combined motion induced by each elastic mode is obtained from the solution of the Euler equation. These effects are superimposed to determine the combined aerodynamic effects of all themodes. Thus, a generalized aerodynamic forcematrix of the aerodynamic loads caused by themotion of the launch vehicles (rigid body, aswell as elastic deformations) and the effect of a gust encountered in the flight is constructed. Finally, a generalized (iterative) eigenvalue analysis is performed to evaluate the aeroelastic stability of the linearizedmodel of the launch vehicles, operating at the prescribedflowconditions,when it encounters a gust. This approach, along with the use the matched-filter theory on the resulting frequency response function, allows one to evaluate the effects of the worst-case excitation on the launch vehicle’s aeroelastic response. The methodology has been applied to predict successfully the aeroelastic stability of LYRA launch vehicles when it flies through transverse gusts.

On the use of geometry design variables in the MDO analysis of wing structures with aeroelastic constraints on stability and response

A Multidisciplinary-Design-Optimization (MDO) approach for the initial design of wing structures based on the integrated modeling of structures, aerodynamics, flight dynamics, and aeroelasticity, has been developed. The procedure is based on the use of a standard numerical optimizer which employs structural, aeroelastic, and aerodynamic analyzers (a finite-element analyzer for structures, panel method analyzer for aerodynamics, and a longitudinal trim analyzer for flight mechanics) in the MDO process. The space discretizations of the numerical models – namely, the structural and aerodynamic meshes – are dynamically updated as function of wing-structure geometry variables during the optimization process. The geometric-updating procedure has been validated using available analytical solutions for a structural optimization problem. The obtained MDO solutions are essentially based on a first-principle formulation but, in the meanwhile, they do not result to be computationally expansive. Moreover, in order to make the implemented MDO algorithm more computationally efficient and effective, an analytical method based on Matched-Filter Theory (MFT) has been used to evaluate the worst aeroelastic-response case due to a gust input having an assigned energy level with the purpose of obtaining a final design also optimizing the gust-response performance. The optimization capabilities of using geometry design variables vs the standard structural design variables in the MDO process have been highlighted. Moreover, the use of a composite objective function, combining structure and aerodynamic issues, has been shown further capabilities of the presented MDO procedure. Finally, some results are also presented for a benchmark regional aircraft wing to show the potentiality of the approach.

SPARSE RECONSTRUCTION FOR SAR IMAGING BASED ON COMPRESSED SENSING

Synthetic Aperture Radar (SAR) can obtain a two- dimensional image of the observed scene. However, the resolution of conventional SAR imaging algorithm based on Matched Filter (MF) theory is limited by the transmitted signal bandwidth and the antenna length. Compressed sensing (CS) is a new approach of sparse signals recovered beyond the Nyquist sampling constraints. In this paper, a high resolution imaging method is presented for SAR sparse targets reconstruction based on CS theory. It shows that the image of sparse targets can be reconstructed by solving a convex optimization problem based on L1 norm minimization with only a small number of SAR echo samples. This indicates the sample size of SAR echo can be considerably reduced by CS method. Super-resolution property and point-localization ability are demonstrated using simulated data. Numerical results show the presented CS method outperforms the conventional SAR algorithm based on MF even though small sample size of SAR echo is used in this method.

Short-Term Memory Trace in Rapidly Adapting Synapses of Inferior Temporal Cortex

Visual short-term memory tasks depend upon both the inferior temporal cortex (ITC) and the prefrontal cortex (PFC). Activity in some neurons persists after the first (sample) stimulus is shown. This delay-period activity has been proposed as an important mechanism for working memory. In ITC neurons, intervening (nonmatching) stimuli wipe out the delay-period activity; hence, the role of ITC in memory must depend upon a different mechanism. Here, we look for a possible mechanism by contrasting memory effects in two architectonically different parts of ITC: area TE and the perirhinal cortex. We found that a large proportion (80%) of stimulus-selective neurons in area TE of macaque ITCs exhibit a memory effect during the stimulus interval. During a sequential delayed matching-to-sample task (DMS), the noise in the neuronal response to the test image was correlated with the noise in the neuronal response to the sample image. Neurons in perirhinal cortex did not show this correlation. These results led us to hypothesize that area TE contributes to short-term memory by acting as a matched filter. When the sample image appears, each TE neuron captures a static copy of its inputs by rapidly adjusting its synaptic weights to match the strength of their individual inputs. Input signals from subsequent images are multiplied by those synaptic weights, thereby computing a measure of the correlation between the past and present inputs. The total activity in area TE is sufficient to quantify the similarity between the two images. This matched filter theory provides an explanation of what is remembered, where the trace is stored, and how comparison is done across time, all without requiring delay period activity. Simulations of a matched filter model match the experimental results, suggesting that area TE neurons store a synaptic memory trace during short-term visual memory.

Worst-Case Gust-Response Analysis for Typical Airfoil Section with Control Surface

We determine the worst-case gust response of a typical airfoil section with a control surface. Matched filter theory is employed in order to compute the gust that produces a maximum response. These results are compared with a tuned one-minus-cosine gust, one of the standard representations of discrete gusts

Time-reversal processing for an acoustic communications experiment in a highly reverberant environment.

Time-reversal (T/R) communications is a new application area motivated by the recent advances in T/R theory. Although perceived by many in signal processing as simply an application of matched-filter theory, a T/R receiver offers an interesting solution to the communications problem for a reverberant channel. In this paper, the performance of various realizations of the T/R receiver for an acoustic communications experiment in air is described along with its associated processing. The experiment is developed to evaluate the performance of point-to-point T/R receivers designed to extract a transmitted information sequence propagating in a highly reverberant environment. It is demonstrated that T/R receivers are capable of extracting the transmitted coded sequence from noisy microphone sensor measurements with zero-symbol error. The processing required to validate these experimental results is discussed. These results are also compared with those produced by an equivalent linear equalizer or inverse filter, which provides the optimal solution when it incorporates all of the reverberations.

Analysis of Random Gust Response with Nonlinear Unsteady Aerodynamics

Experimental nonlinear unsteady aerodynamics is employed to determine the maximal aircraft response to random gust through matched filter theory. Two airplane configurations, the F-18 High Alpha Research Vehicle (HARV) and F-16XL, have been tested in forced oscillation to provide the necessary data. The atmospheric turbulence is assumed to be characterized by the von Karman gust power spectral density function. The nonlinear aerodynamic models are set up through Fourier functional analysis of the test data. For comparative purposes, linear aerodynamic models are also calculated by using the unsteady quasi-vortex-lattice method. It is shown that nonlinear unsteady aerodynamics produce the instantaneous maximum lift response to gust significantly higher than that by the linear unsteady aerodynamics

Aeroelastic Sensitivity Analyses for Flutter Speed and Gust Response

Two methods for the aeroelastic eigensensitivity analysis and the sensitivity analysis of an aeroelastic discrete-gust response have been developed. Finite state modeling of the unsteady aerodynamics allows one to determine explicitly the aeroelastic sensitivity with respect to a structural design variable and the aeroelastic behavior with respect to other design variables such as fuel weight, wing stiffness, and engine location. An analytical method based on the matched filter theory has been developed that allows one to estimate the sensitivity, with respect to the same design variable, of the maximum peak reached by the gust response due to a discrete gust. This approach allows one to evaluate the maximum value of the response corresponding to a discrete-gust input once the energy level of the input has been established. The sensitivity of this maximum value with respect to an aeroelastic-design variable can be evaluated too. The structural and aerodynamic contributions to the sensitivity have been separately identified following several levels of approximation. Numerical results, in the case of an ultrahigh capacity aircraft wing, are presented. Because of the large flexibility of the wing, the aeroelastic behavior has been included in the stability margin estimate and in the gust response. The application limits of the sensitivity approximations are discussed. The proposed approach, which uses structural and aerodynamic data by standard codes, could be useful in the preliminary design to evaluate and preestimate the aeroelastic performances.

Matched-filter and stochastic-simulation-based methods of gust loads prediction

Two analysis methods, one deterministic and the other stochastic, for computing maximized and timecorrelated gust loads for aircraft with nonlinear control systems are described. The first method is based on matched-filter theory, and the second is based on stochastic simulation. This article summarizes the methods, discusses the selection of gust intensity for each method, and presents numerical results. The critical gust profiles and the maximized and time-correlated load time histories obtained from the two methods are compared. Their striking similarity demonstrates that the key quantities from the matched-filter-based method are realized in the stochastic-simulation-based method. The similarity between the results of the two methods is seen to exist for both linear and nonlinear configurations, indicating that these methods are capable of predicting the critical gust profiles and loads for those classes of nonlinear systems examined.

A near-optimum discriminator demodulator for binary FSK with wide tone spacing

Optical FSK communication systems often require large tone spacings to reduce bit-error-rate (BER) degradation from laser-linewidth-induced crosstalk. Until now, discriminator detection of FSK for such wide tone spacings has fallen short of matched filter performance because of the suboptimal choice of a prefilter. A near-optimum demodulator for 240 Mb/s, three-times minimum orthogonal CPFSK (continuous-phase frequency-shift keying) has been constructed, with a measured performance that is 0.5 dB from matched filter theory at 10/sup -9/ BER. The design can be scaled to other data rates and tone spacings. The demodulator incorporates a frequency tracking loop that has good performance at low signal levels and no data-pattern dependence. >