Adaptive Minimum Variance Research Articles

A novel strengthened road roughness estimation framework based on the adaptive unbiased minimum variance with unknown input

The accurate acquisition of the strengthened road roughness is significant to vehicle reliability and durability. However, the existing road roughness estimation methods are inapplicable due to the non-Gaussian characteristic of the strengthened road roughness. To fulfil this gap, this work proposes a novel strengthened road roughness estimation framework based on the adaptive unbiased minimum variance with unknown input (AUMV-UI) to accurately estimate the roughness of strengthened roads. The proposed AUMV-UI framework consists of four sub-models, i.e. the quarter suspension model, the vehicle parameter identification model, the road roughness estimation model, and the estimation error correction model. In the road roughness estimation model, the unbiased minimum variance with unknown input (UMV-UI) method is proposed for the first to estimate the strengthened road roughness, and thus the first three sub-models are also integrated as the UMV-UI framework. In the estimation error correction model, the Sage–Husa method is introduced to further correct the potential errors of the UMV-UI method. The AUMV-UI framework is validated and compared to the UMV-UI framework by simulations and experiments. The results show that the AUMV-UI framework can significantly improve the estimation accuracy, which can serve as an elegant tool to accurately acquire the strengthened road roughness.

Comparison of conventional and adaptive acoustic beamforming algorithms using a tetrahedral microphone array in noisy environments

In situ acoustic measurements are often plagued by interfering sound sources that occur within the measurement environment. Both adaptive and conventional beamforming algorithms, when applied to the outputs of a microphone array arranged in a tetrahedral geometry, are able to capture sound sources in desired directions and reject sound from unwanted directions. Adaptive algorithms may be able to measure a desired sound source with greater spatial precision, but require more calculations and, therefore, computational power. A conventional frequency-domain phase-shift algorithm and a modified adaptive frequency-domain Minimum Variance Distortionless Response (MVDR) algorithm were applied to simulated and recorded signals from a tetrahedral array of omnidirectional microphones. The algorithms are described mathematically and demonstrated on both deterministic and real-world sound data, to quantitatively validate and compare their performance and to provide listening examples of their outputs in a variety of acoustically replicated environments. [Work supported by Portland State University.]

Ultrafast Plane Wave Imaging Using Tensor Completion-Based Minimum Variance Algorithm.

Coherent plane wave compounding (CPWC) imaging is an efficient technique in high-frame-rate ultrasound imaging. To improve the image quality obtained from the CPWC, the adaptive minimum variance (MV) algorithm can be used. However, the high computational complexity of this algorithm negatively affects the frame rate. In other words, achieving a high frame rate and high-quality features simultaneously remains a challenge in medical ultrasound imaging. The aim of the work described here was to develop an algorithm to tackle this challenge and improve the frame rate while preserving the good quality of the resulting image. A tensor completion (TC)-based MV algorithm is proposed to simultaneously improve the frame rate and image quality in CPWC. In the proposed method, the MV algorithm is applied to a limited number of pixels in the beamforming grid. Then, the appropriate values are assigned to the remaining unprocessed pixels by using the TC algorithm. The proposed algorithm speeds up the beamforming process, and consequently, improves the frame rate. The computational complexity of the proposed TC-based MV algorithm is reduced compared with that of the conventional MV algorithm while the good quality of this algorithm is preserved. The results indicate that, in particular, by processing 40% of the beamforming grid using the MV beamformer followed by the TC algorithm, a reconstructed image comparable to that in the case in which the MV algorithm is performed on the full beamforming grid is obtained; the difference between the contrast-to-noise ratio evaluation metric between these two cases is about 0.16 dB for the experimental-resolution phantom. Also, the resulting images obtained from the MV algorithm and the TC-based MV method have the same resolution, indicating that the TC-based MV algorithm can successfully achieve the quality of the MV algorithm with a lower computational complexity. The TC-based MV algorithm is proposed in CPWC with the goal of improving frame rate and image quality. Qualitative and quantitative results reveal that by use of the proposed algorithm, the quality of the reconstructed image will be comparable to that of the conventional MV algorithm, and the frame rate will be improved.

Synthetic Aperture Ultrasound Imaging through Adaptive Integrated Transmitting-Receiving Beamformer.

Synthetic aperture (SA) technique is very attractive for ultrafast ultrasound imaging, as the entire medium can be insonified by a single emission. It also permits applying the dynamic focusing as well as adaptive beamforming both in transmission and reception, which results in an enhanced image. In this paper, we firstly show that the problem of designing the transmit and receive beamformers in SA structure can be formulated as a problem of designing a one-way beamformer on a virtual array with a lateral response equal to that of the two-way beamformer on SA. It is also demonstrated that the length of the virtual aperture is increased to the sum of the transmit aperture length and the receive one, which can result in an enhanced resolution. Moreover, a better estimation of the covariance matrix can be obtained which can be utilized for applying adaptive minimum variance (MV) beamforming method on the virtual array, and consequently the resolution and contrast properties would be enhanced. The performance of the new method is compared with other existing MV-based methods and is quantified by some metrics such as the full width at half maximum (FWHM) and generalized contrast to noise ratio (GCNR). Our validations on simulations and experimental data have shown that the new method is capable of obtaining higher GCNR values while retaining or decreasing FWHM values almost all the time. Moreover, for the same subarray length for estimating the covariance matrices, the computational burden of the new method is significantly lower than those of the existing rival methods.

Optimal exploration strategies for finite horizon regret minimization in some adaptive control problems

In this work, we consider the problem of regret minimization in adaptive minimum variance and linear quadratic control problems. Regret minimization has been extensively studied in the literature for both types of adaptive control problems. Most of these works give results of the optimal rate of the regret in the asymptotic regime. In the minimum variance case, the optimal asymptotic rate for the regret is log(T) which can be reached without any additional external excitation. On the contrary, for most adaptive linear quadratic problems, it is necessary to add an external excitation in order to get the optimal asymptotic rate of √T. In this paper, we will actually show from a theoretical study, as well as, in simulations that when the control horizon is pre-specified a lower regret can be obtained with either no external excitation or a new exploration type termed immediate.

Plane wave ultrasound imaging using compressive sensing and minimum variance beamforming

Coherent plane-wave compounding (CPWC) is a widely used technique in medical ultrasound imaging due to its high frame rate property. It is well-known that increasing the plane waves leads to improving the image quality. However, the image quality still needs to be further improved in CPWC. In this regard, a variety of methods have been proposed. In this paper, a new compressive sensing (CS) based approach is introduced with the combination of the adaptive minimum variance (MV) algorithm to further improve the image quality in terms of resolution and contrast. In the proposed method, which is called the CS-based MV technique, the CS method is used in the receive direction to produce the beamformed data for each plane wave. Then, the MV algorithm is performed in the plane wave transmit angle direction to coherently compound the images and improve the resolution. Moreover, to deal with the high computational complexity and also, the needing for high memory space during the CS method implementation, an approximation is considered which results in considerably reduced computational burden and memory space. The results obtained from the simulated point targets show that the proposed method leads to resolution improvement for about 71%, 5.5%, and 37% respectively, compared to DAS, DAS+MV, and CS+DAS beamformers. Also, the quantitative results obtained from the experimental contrast phantom in plane wave imaging challenge in medical ultrasound (PICMUS) data show a 3.02 dB, 2.57 dB, and 2.24 dB improvement of the contrast ratio metric using the proposed method compared to DAS, DAS+MV, and double-MV methods, respectively, indicating the good performance of the proposed method in image quality improvement.

Fault analysis of photovoltaic based DC microgrid using deep learning randomized neural network

The implementation of solar photovoltaic (PV) based Direct Current (DC) microgrid is limited due to lack of well-defined protection standards. These low voltage DC distribution networks are facing implementation challenges especially under multi-distributed generations subjected to various events such as PV arc faults, load switching faults, PV partial shading, change in PV irradiance and DC cable faults, etc. Thus a new deep learning neural network model is proposed for the accurate fault classification and distance calculation for an effective monitoring and protection coordination of photovoltaic based DC microgrid. The proposed model is the combination of an Adaptive variational mode decomposition (AVMD) and deep minimum variance random vector functional link network (DRVFLN). The optimal parameters of AVMD are selected using chaotic sine–cosine firefly algorithm (CSCFA) and efficient weighted kurtosis index (EWKU). Further, a novel non-iterative DRVFLN network is applied for accurate fault classification and location. In the DRVFLN direct connections are present from preceding layers to the forward layers of the network like random vector functional link network. These connections help to reduce the model complexity and also regularize the randomization. Further, the denoising criterion is also introduced in this network where the uncorrupted input can be recovered from the corrupted versions by using the autoencoder to obtained better results than the traditional networks. The performances of various models are evaluated using some performance index such as overall accuracy and sensitivity. Results conclude that the proposed AVMD-DRVFLN model classified the faults with an accuracy and sensitivity of 100% for all the events. The model performance is also tested and validated against the unwanted noise by considering different signal-to-noise ratio to ensure the robustness of the proposed model. Results conclude that the proposed model correctly classified the faults against such incorporation of noise. Similarly, the performances of various models for the estimation of fault distance are validated by computing the relative error. Results conclude that the proposed AVMD-DRVFLN model produces promising result in terms of relative error(which is less than 5%) for all the events. Comparisons with some existing models like extreme learning machine, support vector machine, random vector functional link network and deep extreme learning machine are included to validate the efficacy of the proposed AVMD-DRVFLN model. Results conclude that proposed method outperforms all the other methods. The effectiveness of the proposed AVMD-DRVFLN model for fault classification and distance estimation is established through rigorous case studies in MATLAB augmented by some real time test results.

Angular Superresolution of Real Aperture Radar With High-Dimensional Data: Normalized Projection Array Model and Adaptive Reconstruction

Angular resolution of real aperture radar (RAR) can be improved using deconvolution methods to achieve enhanced target information based on the convolution relationship between target scatterings and an antenna pattern. However, depending on the wide scanning scope and dense sampling angular interval, the computational complexity of the deconvolution methods will drastically increase as the dimension of azimuthal data increases. In this paper, to efficiently improve the angular resolution of RAR, a generalized adaptive asymptotic minimum variance (GAAMV) estimator that relies on a normalized projection array (NPA) model is proposed. On the one hand, the traditional convolution model of RAR is transformed into an NPA model to compress the data dimension. The proposed NPA model can normalize the signal model to make it independent of the sampling parameters. On the other hand, based on the NPA model, a GAAMV estimator is proposed to efficiently reconstruct the targets by adaptively updating each grid. Moreover, the penalty parameter is extended as a generalized case to improve its adaptability to different scenes. Based on the proposed model and method, the computational complexity can be decreased, especially for high-dimensional azimuthal data. Simulations and experimental data verify the proposed model and method.

A Modified Anti-Jamming Method Using Dual-Polarized Ellipsoid Minimum Variance Distortionless Response to Predict the Coverage Ratio of Global Positioning System Signal

The dual-polarized array is considered as one of the key anti-jamming techniques due to twice counts of the jamming signals suppressed relative to a monopolar array. This paper proposes an adaptive anti-jamming dual-polarized ellipsoid minimum variance distortionless response (EMVDR) method to evaluate the carrier-to-noise ratio (<inline-formula> <tex-math notation="LaTeX">${C}$ </tex-math></inline-formula> a/<inline-formula> <tex-math notation="LaTeX">${N}_{{0}}$ </tex-math></inline-formula>) for global positioning system (GPS) coverage. Firstly in GPS L1 band, the dual-polarized gains obtained by controlled reception pattern antenna (CRPA) array are investigated as the signal model of GPS coverage evaluation. Secondly, a novel EMVDR method based the Hermitian tridiagonal loading technique is proposed to obtain the smooth anti-jamming weight matrix, which is against the system error by using the highpass filtering performance of Hermitian tridiagonal matrix. Finally, <inline-formula> <tex-math notation="LaTeX">${C}\text{a}/{N}_{{0}}$ </tex-math></inline-formula> based the proposed EMVDR is used to evaluate the coverage percentage of the GPS signal, namely <inline-formula> <tex-math notation="LaTeX">${C}\text{a}/{N}_{{0}}$ </tex-math></inline-formula> needs to be beyond a threshold of <inline-formula> <tex-math notation="LaTeX">$28~\text {dB}\cdot \text {Hz}$ </tex-math></inline-formula> by recovering GPS signal. Through many Monte Carlo simulations, the results indicate that the anti-jamming performance of proposed EMVDR method outperforms the classic minimum variance distortionless response (MVDR) based the space adaptive processing (SAP) and the space time adaptive processing (STAP), and the GPS coverage ratio can keep from 60&#x0025; to 70&#x0025; until degrees of freedom (DOFs) are exhausted to zero.

Low-complexity adaptive minimum variance ultrasound beam-former based on diagonalization

BackgroundDespite the high capability of the minimum variance (MV) beam-former in improving image quality compared to the delay and sum (DAS), its high complexity, which is due to L × L covariance matrix inversion and calculation, limits its usage for real-time applications. The complexity of MV is in the order of O(L3). MethodA low-complexity adaptive MV (LCAMV) is suggested in to reduce the complexity of the MV beam-former via the covariance matrix diagonalization. So, the proposed method focuses on two aspects: One is to reduce the complexity due to covariance matrix calculation and the other to reduce the MV complexity caused by the inversion. Thus, the MV complexity is significantly reduced, although the resulted image of LCAMV is similar to that of MV. ResultsThe LCAMV technique is applied to the simulated phantoms and real data to verify its performance and to compare it with other popular beamforming techniques. Statistical parameters of the image quality for all experiments were calculated to check the image quality. Also, the run time analysis showed that the difference between the values of the MV and LCAMV statistical image quality parameters was less than 2%, while the speed of LCAMV was about 4 times faster. ConclusionResults indicate that using LCAMV, not only the resolution and contrast are not changed significantly, but also the complexity is reduced from O(L3) to O(L×log(L)). Thus, this simple technique can be a big step towards adaptive real-time imaging.

A Novel Synchronously Rotating Reference Frame Based Adaptive Control Architecture for Enhanced Grid Support Functions of Single-Phase Inverters

This article presents a novel adaptive control architecture for-connected single-phase inverters (SPIs) that can dynamically regulate active and reactive power, thus enabling grid support functions effectively. The base controller framework is that of the active and reactive power control in the dq-domain. The proposed controller is based on an adaptive minimum variance framework and utilizes an online parametric identifier. The advantage of the proposed scheme is that the architecture can concurrently provide voltage support and line power balancing capability with a performance factor greater than a static proportional-integral (PI) and proportional-resonant (PR) controller. First, the controller capability is evaluated on a proof of concept aggregated power grid model or a single machine infinite bus system. Then, the tests for concurrently providing line balancing and voltage support are conducted by connecting SPIs at several locations on an unbalance three-phase IEEE 123 node distribution system model. It has been demonstrated that the proposed architecture is adaptable and performs better (3% to 5% improvement in error) when compared to static PI and PR controller during varying operating conditions.

Capon–Bartlett cross-spectrum and a perspective on robust adaptive filtering

Adaptive filtering / beamforming (ABF) optimized for maximum signal-to-interference-plus-noise ratio (SINR) results in filter weights that depend on the data covariance and the signal array response vector. The effectiveness of practical application of such solutions, however, is limited by (i) extent of data stationarity and (ii) imperfect knowledge of the true signal array response vector. Robust ABF methods attempt to address these two critical issues via a slight reformulation of the ABF problem statement. Many robust ABF solutions result in some form of diagonal loading of the data covariance, and can be interpreted as a hybrid beamformer that engages the tradespace between data adaptive beamforming and conventional beamforming (CBF). In view of this interpretation, an exact joint probability distribution is derived for 1) the data adaptive Capon minimum variance distortionless response (MVDR) spectral estimator, and 2) the conventional Bartlett spectral estimator (a.k.a. the smoothed periodogram) when based on the same data covariance estimate. The resulting joint distribution motivates (i) proposal of a robust adaptive filtering design that constrains filter weight cross coherence (a metric quantifying the statistical coupling between the Capon and Bartlett spectral statistics), and (ii) introduction of a coherence estimate called the Capon–Bartlett cross spectrum.

A Comparison of Optimal SONAR Array Amplitude Shading Coefficients

This paper compares two different approaches to deriving shading coefficients (weights) for optimal first order and second order directional sensors (that is; sonobuoys, vectors and dyadic sensors). The first approach is an analytical or a physics-based derivation, involving computations with gradients and linearized momentum; the second is an adaptive minimum variance distortionless response (MVDR) derivation, which finds weights that minimize the cross spectral density (CSD) matrix. The two approaches are shown to be equivalent. In other words, the adaptive MVDR processing procedure does indeed converge to a physics-based solution, without any pre-existing physical knowledge of the behavior of the acoustic field. This suggests that adaptive algorithms innately seek physics-based solutions when these solutions are optimum. The intent of this short communication is not to advocate for one type of adaptive processing method over another. The observation that is presented here is important though, it confirms that at least in an idealized noise field, adaptive processing converges on an optimal set of shading coefficients, similarly derived based on well-established physical acoustics.

A Scheme for Multitarget Lateral Velocity Measurement With High-Frequency Monostatic Radar

This paper proposes a method for extracting the lateral velocity in multitarget cases to improve the ship tracking capability of high-frequency surface wave radar (HFSWR). The method exploits the phase interferometry between two antenna arrays that is done in the time domain after joint frequency–azimuth filtering. First, a frequency filter based on generalized S transform with a variable factor is applied to eliminate the background noise. The variable factor is linked to the target's radial velocity and can control the time and frequency resolutions. We then use two identical subarrays as two receiving channels to implement the azimuth filtering. Based on adaptive minimum-variance distortionless response beamforming, the extraneous signals can be suppressed. Subsequently, the varying slope of the phase difference of the target of interest signal from two channels can be extracted to compute the lateral velocity in a low signal-to-noise ratio scenario. The interference of noise and extraneous target signals is analyzed using simulations, and the effectiveness of the frequency–azimuth filtering method is validated with both simulations and experimental data. In addition, the high spatial resolution ship tracking results over an hour match well with automatic identification system data, demonstrating the feasibility of tracking targets with HF monostatic radar.

Accurate diffraction imaging for detecting small-scale geologic discontinuities

Seismic diffractions contain valuable information regarding small-scale inhomogeneities or discontinuities, and therefore they can be used for seismic interpretation in the exploitation of hydrocarbon reservoirs. Velocity analysis is a necessary step for accurate imaging of these diffractions. A new method for diffraction velocity analysis and imaging is proposed that uses an improved adaptive minimum variance beamforming technique. This method incorporates the minimum variance, coherence factor, and correlation properties to improve the signal-to-noise ratio and enhance correlations. Our method can make seismic diffractions become better focused in semblance panels, allowing for the optimal migration velocity for diffractions to be accurately picked. Synthetic and field examples demonstrate that the migration velocity for the diffractions can differ from that for the reflections. The results suggest that the diffraction velocity analysis and imaging method is feasible for accurately locating and identifying small-scale discontinuities, which leads to the possibility of using this approach for practical application and seismic interpretation.

Spatio-temporal Reconstruction of Neural Sources Using Indirect Dominant Mode Rejection

Adaptive minimum variance based beamformers (MVB) have been successfully applied to magnetoencephalogram (MEG) and electroencephalogram (EEG) data to localize brain activities. However, the performance of these beamformers falls down in situations where correlated or interference sources exist. To overcome this problem, we propose indirect dominant mode rejection (iDMR) beamformer application in brain source localization. This method by modifying measurement covariance matrix makes MVB applicable in source localization in the presence of correlated and interference sources. Numerical results on both EEG and MEG data demonstrate that presented approach accurately reconstructs time courses of active sources and localizes those sources with high spatial resolution. In addition, the results of real AEF data show the good performance of iDMR in empirical situations. Hence, iDMR can be reliably used for brain source localization especially when there are correlated and interference sources.

Variable selection through adaptive MAVE

Adaptive minimum average variance estimation (MAVE) is an efficient approach for dimension reduction as it can adapt to different error distributions. In this paper, we combine the ideas of adaptive estimation and regression shrinkage, and propose the sparse adaptive MAVE (saMAVE). The saMAVE can estimate the central mean subspace and select informative covariates simultaneously, without assuming any particular model or distribution on the predictor variables. The efficacy of saMAVE is verified through both theoretical results and simulation studies.

Effect of low complexity adaptive beamforming on seafloor backscatter statistics

In contrast with the common delay-and-sum (DAS) beamformer, adaptive beamformers can be used to increase the resolution of acoustic measurement systems without sacrificing sidelobe levels. The non-linear nature of adaptive beamforming methods may cause changes to measured scattered amplitude distributions. For example, large differences in speckle statistics have been observed in some cases when using the adaptive minimum variance distortionless response (MVDR) beamformer in medical ultrasound, unless preventative measures were implemented. Low complexity adaptive (LCA) beamforming techniques have shown similar, but smaller, differences. Recently, experiments have been conducted in the Oslofjord, Norway, to determine whether low-complexity adaptive (LCA) beamforming techniques can improve the performance of bathymetric echosounders. When using LCA with these types of sonar systems, it is important to understand any changes to the backscatter statistics, as these changes may affect, e.g., seafloor classification methods. In this talk we will present comparisons between backscatter statistics estimated using a weighted delay-and-sum beamformer and those estimated using a low complexity adaptive beamformer. We will discuss any differences seen, possible mechanisms causing these differences and potential remedies. [Research supported by The Norwegian Research Council and Kongsberg Maritime. The first author is employed in Kongsberg Maritime.]

Application of Adaptive Minimum-variance Control Technique for Cyclic Production Machine

In this paper, we consider the control problem of cyclic production machine. There are many mechanical parts produced by cyclic production machine. For example, springs are usually made of long stock wire which is fed into cyclic forming machine. A spring forming machine can force wire into a coiled shape during the cyclic motion, but the length of the produced spring in each cycle is varying according to the internal stress of wire, mechanical uncertainty of the forming machine etc. To improve the accuracy of free length of spring produced by forming machine, we propose an adaptive minimum-variance control strategy for cyclic production system and successfully applied to commercial spring forming machine.

Combining the APES and Minimum-variance Beamformers for Adaptive Ultrasound Imaging.

In recent years, adaptive minimum-variance (MV) beamforming has been successfully applied to medical ultrasound imaging, resulting in simultaneous improvement in imaging resolution and contrast. MV has high resolution and hence can provide accurate estimates of the target locations. However, the MV amplitude estimates are significantly biased downward, especially when occurring the errors in model parameters. The amplitude and phase estimation (APES) beamformer gives much more accurate amplitude estimates at the target locations, but at the cost of lower resolution. To reap the benefits of both MV and APES, we have proposed a modified APES (MAPES) beamformer by adding a parameter which controls the trade-off between spatial and amplitude resolutions. We have also proposed an adaptive beamformer which combines the MV and APES. The proposed beamformer first estimates the peak locations using the MV estimator and then refines the amplitude estimates at these locations using the MAPES estimator. By using simulated and experimental data-point targets as well as cyst phantoms-we show the efficacy of the proposed beamformers.