Linear Constraint Minimum Variance Research Articles

Sparsity-Based Nondestructive Evaluations of Downhole Casings Technique Using the Uniform Linear Array

Borehole pulsed eddy-current (PEC) systems based on uniform linear multicoil arrays (ULMAs) perform efficient nondestructive evaluations (NDEs) of metal casings. However, the limited physical space of the borehole restricts the degrees of freedom (DoFs) of ULMAs to be less than the number of constraints, which leads to the difficulty of compensating for the differences in signals acquired by different receivers with different transmitting-to-receiving distances (TRDs), and thus limits the effectiveness of the ULMA system. To solve this problem, this paper proposes sparse linear constraint minimum variance (S-LCMV) for NDEs of downhole casings employing ULMAs. By transforming and characterizing the original PEC signal, it was observed that the signal power dramatically decreased with increasing Legendre polynomial stage, confirming that the signal was sparsely distributed over the Gauss–Legendre stages. Using this property, the S-LCMV cost function with reduced constraints was constructed to provide enough DoFs to accurately calculate the weight coefficients, thus improving the detection performance. The effectiveness of the proposed method was verified through field experiments on an 8-element ULMA installed in a borehole PEC system for NDEs of oil-well casings. The results demonstrate that the proposed method could improve the weighting effect by reducing the number of constraints by 70% while ensuring the approximation accuracy, which effectively improved the signal-to-noise ratio of the measured signals and reduced the computational cost by about 87.9%.

Numerical model of the QiTai radio Telescope PAF receiver signal and simulation of interference mitigation

Xinjiang Qitai is constructing a 110-m fully steerable radio telescope (QiTai radio Telescope [QTT]) equipped with a phased array feeds (PAF) receiver, which will install the focal plane operating from 0.7 to 1.8 GHz. In this article, we introduce a PAF receiver model for beamforming, and the numerical model of the internal and external noise for this PAF receiver is provided using electromagnetic field simulation software. The linear constraint minimum variance (LCMV) algorithm is used to simulate the interference mitigation. The interference mitigation rate is from 0.581 to 0.921, and the signal restoration rate is from 0.998 to 1.512 within the error range of an interference arrival angle of 10°. This conclusion can be used in the signal correction of the PAF receiver for interference mitigation.

Key technologies of smart antenna in WLAN based on adaptive array

In order to improve the communication quality and transmission rate of WLAN, the key technologies of smart antenna in WLAN based on adaptive array are proposed. The adaptive array is used to suppress the WLAN interference signal, and the feedback from the output is used to adjust the weight loaded on the signal, and the optimal weight corresponding to the linear constraint minimum variance is obtained to achieve beamforming optimization. On this basis, the optimal antenna selection algorithm and the sub-optimal antenna selection algorithm are designed, and the sub-channel matrix with the maximum capacity can be obtained by repeated iterative calculation, so as to realize the smart antenna selection and further optimize the performance of WLAN. Experimental results show that the smart antenna signal has 100% coverage in WLAN compared with conventional methods. When the transmission rate is greater than 35 mb/s, the delay of the study method is not jitter compared to the contrast method. When the transmission rate is greater than 35 mb/s, the packet loss rate of the proposed method remains stable, while the packet loss rate of the two comparison methods begins to increase. When the transmission rate is greater than 12 mb/s, the throughput of the proposed method is more stable than the other methods. In conclusion, this method has the characteristics of large signal coverage, low delay, low packet loss rate and high throughput, and can be widely used in practice. The contribution of this study is that the weight of the antenna array can be dynamically adjusted through the adaptive algorithm, thus optimizing the quality of the received signal and improving the receiving sensitivity.

In silico study of the effects of cerebral circulation on source localization using a dynamical anatomical atlas of the human head

Objective. This study focuses on the effects of dynamical vascular modeling on source localization errors in electroencephalography (EEG). Our aim of this in silico study is to (a) find out the effects of cerebral circulation on the accuracy of EEG source localization estimates, and (b) evaluate its relevance with respect to measurement noise and interpatient variation. Approach. We employ a four-dimensional (3D + T) statistical atlas of the electrical properties of the human head with a cerebral circulation model to generate virtual patients with different cerebral circulatory conditions for EEG source localization analysis. As source reconstruction techniques, we use the linearly constraint minimum variance (LCMV) beamformer, standardized low-resolution brain electromagnetic tomography (sLORETA), and the dipole scan (DS). Main results. Results indicate that arterial blood flow affects source localization at different depths and with varying significance. The average flow rate plays an important role in source localization performance, while the pulsatility effects are very small. In cases where a personalized model of the head is available, blood circulation mismodeling causes localization errors, especially in the deep structures of the brain where the main cerebral arteries are located. When interpatient variations are considered, the results show differences up to 15 mm for sLORETA and LCMV beamformer and 10 mm for DS in the brainstem and entorhinal cortices regions. In regions far from the main arteries vessels, the discrepancies are smaller than 3 mm. When measurement noise is added and interpatient differences are considered in a deep dipolar source, the results indicate that the effects of conductivity mismatch are detectable even for moderate measurement noise. The signal-to-noise ratio limit for sLORETA and LCMV beamformer is 15 dB, while the limit is under 30 dB for DS. Significance. Localization of the brain activity via EEG constitutes an ill-posed inverse problem, where any modeling uncertainty, e.g. a slight amount of noise in the data or material parameter discrepancies, can lead to a significant deviation of the estimated activity, especially in the deep structures of the brain. Proper modeling of the conductivity distribution is necessary in order to obtain an appropriate source localization. In this study, we show that the conductivity of the deep brain structures is particularly impacted by blood flow-induced changes in conductivity because large arteries and veins access the brain through that region.

Broadband beamforming of multiplet line arrays using subband optimal beamformers eliminating port/starboard ambiguity

Development of beamforming methods for towed sonar multiplet line arrays is stimulated by their increased use in undersea surveillance. Such methods employ various forms of narrowband optimal beamforming to resolve port/starboard ambiguity present when the array is conventionally beamformed. In this paper, we depart from the purely narrowband approach and discuss methods for port/starboard ambiguity rejection (PSAR) using a broadband formulation. The proposed methodology relies on subband beamforming for which we use non-adaptive variants of beamspace Minimum Variance Distortionless Response (MVDR) and the Linear Constraint Minimum Variance (LCMV) beamformers. We provide detailed description and comparison of both subband methods and establish a connection between them. The paper provides assessment of PSAR properties of the developed broadband beamformers using outputs of signal processing of both simulated and experimental sonar data. Experimental data for this work were obtained through the participation in the Littoral Continuous Active Sonar (LCAS) trials.

A low complexity STPAP algorithm based on an alternating polarization-sensitive array

BackgroundSpace–time adaptive processing (STAP) has been widely used in the fields of communication, radar, and navigation anti-jamming. However, the traditional scalar arrays used with STAP have limitations because they can only obtain spatial information. To improve the performance, joint space–time filtering is proposed using an alternating polarization-sensitive array (APSA). Compared to a dual-polarization sensitive array (DPSA), this array can provide polarization information and reduce the computational complexity.MethodsAn alternating polarization-sensitive array space–time polarization adaptive processing (APSA-STPAP) algorithm is proposed based on the linear constraint minimum variance (LCMV) criterion. Different from the traditional LCMV criterion, the space–time polarization joint steering vector of the desired and interference signals is used as the constraint matrix, and the “set 1” and “set 0” conditions are used as the constraint conditions to effectively suppress the interference signals and enhance the desired signals.ResultsSimulation results are presented which show the following. (1) Filtering with the proposed APSA-STPAP algorithm is similar to that with the DPSA-STPAP algorithm. From the perspective of the spatial, time, and polarization domains, it can form nulls in the directions of the interference and realize space–time-polarization adaptive processing. (2) The APSA-STPAP algorithm has lower computational complexity than the DPSA-STPAP algorithm. Moreover, the dipole of the alternating polarization sensitive array is halved, which reduces the coupling effect between electric dipoles and makes implementation easier. (3) The APSA-STPAP algorithm maintains good anti-interference performance even when the electric dipole and anti-jamming degrees of freedom are reduced by half, and the anti-jamming performance is similar to that of a polarization-sensitive array. There is little difference between the anti-interference performance of the APSA-STPAP and DPSA-STPAP algorithms when SNR ≥ − 10 dB.

Magnetoencephalography for epileptic focus localization based on Tucker decomposition with ripple window.

AimsTo improve the Magnetoencephalography (MEG) spatial localization precision of focal epileptic.Methods306‐channel simulated or real clinical MEG is estimated as a lower‐dimensional tensor by Tucker decomposition based on Higher‐order orthogonal iteration (HOOI) before the inverse problem using linearly constraint minimum variance (LCMV). For simulated MEG data, the proposed method is compared with dynamic imaging of coherent sources (DICS), multiple signal classification (MUSIC), and LCMV. For clinical real MEG of 31 epileptic patients, the ripples (80–250 Hz) were detected to compare the source location precision with spikes using the proposed method or the dipole‐fitting method.ResultsThe experimental results showed that the positional accuracy of the proposed method was higher than that of LCMV, DICS, and MUSIC for simulation data. For clinical real MEG data, the positional accuracy of the proposed method was higher than that of dipole‐fitting regardless of whether the time window was ripple window or spike window. Also, the positional accuracy of the ripple window was higher than that of the spike window regardless of whether the source location method was the proposed method or the dipole‐fitting method. For both shallow and deep sources, the proposed method provided effective performance.ConclusionTucker estimation of MEG for source imaging by ripple window is a promising approach toward the presurgical evaluation of epileptics.

基于线性约束最小方差准则和合成孔径连续声束合成的内镜超声成像算法

基于线性约束最小方差准则和合成孔径连续声束合成的内镜超声成像算法

A Clutter-Analysis-Based STAP for Moving FOD Detection on Runways.

Security risks and economic losses of civil aviation caused by Foreign Object Debris (FOD) have increased rapidly. Synthetic Aperture Radars (SARs) with high resolutions potentially have the capability to detect FODs on the runways, but the target echo is hard to be distinguished from strong clutter. This paper proposes a clutter-analysis-based Space-time Adaptive Processing (STAP) method in order to obtain effective clutter suppression and moving FOD indication, under inhomogeneous clutter background. Specifically, we first divide the radar coverage into equal scattering cells in the rectangular coordinates system rather than the polar ones. We then measure normalized RCSs within the X-band and employ the acquired results to modify the parameters of traditional models. Finally, we describe the clutter expressions as responses of the scattering cells in space and time domain to obtain the theoretical clutter covariance. Experimental results at 10 GHz show that FODs with a reflection higher than −30 dBsm can be effectively detected by a Linear Constraint Minimum Variance (LCMV) filter in azimuth when the noise is −60 dBm. It is also validated to indicate a −40 dBsm target in Doppler. Our approach can obtain effective clutter suppression 60dB deeper than the training-sample-coupled STAP under the same conditions.

P5. Virtual lesion model to investigate dysphagia resulting from pharyngeal hypesthesia in healthy volunteers

Objective Dysphagia is a frequent and outcome-relevant complication of various neurological disorders, whose treatment options are still limited. Lately, neuromodulatory treatment devices have come into focus to enhance adaptive plasticity in the cortical swallowing network. However, performing neurophysiological and functional imaging studies to investigate these novel neurostimulation techniques for the treatment of neurogenic dysphagia in severely affected patients is difficult. Therefore, basic research needs to be conducted in healthy subjects instead. Here, we propose a virtual peripheral sensory lesion model to mimic pharyngeal sensory impairment, which is known to be a major contributor to dysphagia in neurological disease, as swallowing is a motor function highly dependent on sensory afferent input. Methods In this randomized crossover study on 11 healthy volunteers, cortical activation during pneumatic pharyngeal stimulation was measured applying 275-channel magnetoencephalography (MEG) in two separate sessions, with and without pharyngeal surface anesthesia. Individual source reconstruction was performed applying a linear constraint minimum variance (LCMV-) beamformer algorithm and significant differences between conditions were assessed with a nonparametric permutation test. Results Pneumatic pharyngeal stimulation evoked bilateral event-related desynchronization (ERD) mainly in the primary and secondary sensorimotor areas located in the caudolateral pericentral cortex, the insula, the prefrontal cortex and the supramarginal gyrus. In comparison to the no-anesthesia condition, topical pharyngeal anesthesia led to a virtual lesion in this network, indicated by a reduction of ERD in beta (13–30 Hz) and low gamma (30–60 Hz) frequency ranges (p Conclusions Withdrawal of sensory afferent information by topical surface anesthesia leads to reduced response to pneumatic pharyngeal stimulation in a distributed cortical sensorimotor network in healthy subjects thereby mimicking the situation in patients with dysphagia related to sensory impairment. Significance The proposed virtual sensory lesion paradigm may serve to investigate the effect of neuromodulatory treatments specifically on pharyngeal sensory impairment as relevant cause of neurogenic dysphagia.

Performance Comparison of FA, PSO and CS application in SINR Optimisation for LCMV Beamforming Technique

A beamforming technique called Linearly Constraint Minimum Variance (LCMV) allows directing a radiation beam towards the desired direction to minimise interference of the signal through weight vectors that are computed by LCMV. Generally, to achieve a favourable beam shape, LCMV’s weights are not exactly steered towards the user’s direction. In addition, traditional methods are not equipped well to seamlessly improve the weights of LCMV. This paper employs Particle Swarm Optimisation (PSO), Firefly Algorithm (FA) and Cuckoo Search (CS) to optimise the weights of LCMV. The key anticipated goal in LCMV optimisation is the power reduction on the interferences’ side to achieve a favourable beam shape and better SINR output. A common metaheuristic algorithm is Particle Swarm Optimisation (PSO), which deals with the social behaviour of creatures such as bird flocking. A population and attraction-based algorithm is employed in Firefly algorithm; the flashing characteristics of fireflies are the inspiration of the swarm intelligence metaheuristic algorithm. Also, a novel equation-based nature inspired algorithm is Cuckoo Search (CS), which is based on the brood parasitism of a few cuckoo species combined with the so-called Levy flights. Based on simulation results, FA showed enhanced ability to precisely determine power allocation’s optimal direction when compared with CS and PSO. Thus, better SINR results could be achieved with FA. For SINR optimisation using the LCMV technique, the effectiveness of CS in comparison with FA and PSO algorithms was simulated employing MATLAB®.

Deep Source Localization with Magnetoencephalography Based on Sensor Array Decomposition and Beamforming.

In recent years, the source localization technique of magnetoencephalography (MEG) has played a prominent role in cognitive neuroscience and in the diagnosis and treatment of neurological and psychological disorders. However, locating deep brain activities such as in the mesial temporal structures, especially in preoperative evaluation of epilepsy patients, may be more challenging. In this work we have proposed a modified beamforming approach for finding deep sources. First, an iterative spatiotemporal signal decomposition was employed for reconstructing the sensor arrays, which could characterize the intrinsic discriminant features for interpreting sensor signals. Next, a sensor covariance matrix was estimated under the new reconstructed space. Then, a well-known vector beamforming approach, which was a linearly constraint minimum variance (LCMV) approach, was applied to compute the solution for the inverse problem. It can be shown that the proposed source localization approach can give better localization accuracy than two other commonly-used beamforming methods (LCMV, MUSIC) in simulated MEG measurements generated with deep sources. Further, we applied the proposed approach to real MEG data recorded from ten patients with medically-refractory mesial temporal lobe epilepsy (mTLE) for finding epileptogenic zone(s), and there was a good agreement between those findings by the proposed approach and the clinical comprehensive results.

Source modeling of ElectroCorticoGraphy (ECoG) data: Stability analysis and spatial filtering

BackgroundElectrocorticography (ECoG) measures the distribution of the electrical potentials on the cortex produced by the neural currents. A full interpretation of ECoG data requires solving the ill-posed inverse problem of reconstructing the spatio-temporal distribution of the neural currents. This study addresses the ECoG source modeling developing a beamformer method. New methodWe computed the lead-field matrix by using a novel routine provided by the OpenMEEG software. We performed an analysis of the numerical stability of the ECoG inverse problem by computing the condition number of the lead-field matrix for different configurations of the electrodes grid. We applied a Linear Constraint Minimum Variance (LCMV) beamformer to both synthetic data and a set of real measurements recorded during a rapid visual categorization task. ResultsFor all considered grids the condition number indicates that the ECoG inverse problem is mildly ill-conditioned. For realistic SNR we found a good performance of the LCMV algorithm for both localization and waveforms reconstruction. Comparison with existing methodThe flow of information reconstructed by analyzing real data seems consistent with both invasive monkey electrophysiology studies and non-invasive (MEG and fMRI) human studies. ConclusionsDespite a growing interest from the neuroscientific community, solving the ECoG inverse problem has not quite yet reached the level of systematicity found for EEG and MEG. Starting from an analysis of the numerical stability of the problem we considered the most widely utilized method for modeling neurophysiological data based on the beamformer method in the hope to establish benchmarks for future studies.

Null steering of adaptive beamforming using linear constraint minimum variance assisted by particle swarm optimization, dynamic mutated artificial immune system, and gravitational search algorithm.

Linear constraint minimum variance (LCMV) is one of the adaptive beamforming techniques that is commonly applied to cancel interfering signals and steer or produce a strong beam to the desired signal through its computed weight vectors. However, weights computed by LCMV usually are not able to form the radiation beam towards the target user precisely and not good enough to reduce the interference by placing null at the interference sources. It is difficult to improve and optimize the LCMV beamforming technique through conventional empirical approach. To provide a solution to this problem, artificial intelligence (AI) technique is explored in order to enhance the LCMV beamforming ability. In this paper, particle swarm optimization (PSO), dynamic mutated artificial immune system (DM-AIS), and gravitational search algorithm (GSA) are incorporated into the existing LCMV technique in order to improve the weights of LCMV. The simulation result demonstrates that received signal to interference and noise ratio (SINR) of target user can be significantly improved by the integration of PSO, DM-AIS, and GSA in LCMV through the suppression of interference in undesired direction. Furthermore, the proposed GSA can be applied as a more effective technique in LCMV beamforming optimization as compared to the PSO technique. The algorithms were implemented using Matlab program.

Design and Simulation of Self-Adapting Anti-Interference Algorithm with Information Technology in Satellite Navigation

Satellite navigation system has been widely used in various fields, because of the signal which is received on the ground is weak and the low ratio of signal to interference, the signal is susceptible to be interfered. The adaptive antenna system using power inversion algorithm with information technology is very suitable for the weak signal and strong interference. The paper presents a detailed analysis on the principle with information technology. By building the hardware circuit in the QuartusII, the self-adapting anti-interference system of power inversion algorithm with information technology which based on Linearly Constraint Minimum Variance (LCMV) criterion can be achieved. The correctness of the theoretical analysis and correctness of the design are verified by experiment, the requirements of satellite navigation receiver on the side of anti-interference has been also proved.

Avoiding Self Nulling by Using Linear Constraint Minimum Variance Beamforming in Smart Antenna

The beam forming technique is important in smart antenna systems to enhance the data rates, null steering and coverage. This study presents two methods of beamformeing algorithm; Minimum Variance Distortion less Response (MVDR) and Linear Constraint Minimum Variance (LCMV). MVDR and LCMV techniques form radiation beams based on the received weight vector of the desired signal. The LCMV technique is found to be efficient than the MVDR in self-null even if the interference signal is closer from the desired signal. Simulation has been carried out to validate these two techniques. The four elements of the linear array smart antenna are used in our simulation program with the operation frequency around 2.3 GHz, noise power 0.5dB and the spacing between elements is 0.5 λ. The result of the simulation reveals that both the modes are capable of providing high output power; however they need the direction of all the incoming sources, which is practically difficult to obtain. Nevertheless the MVDR beam forming minimizes the multi-path fading problem, by adding the multi-path signal, which increases the strength of the desired signal and nullifies the interference.

Robust LCMV beamforming based on phase response constraint

The minimum variance distortionless response is known to be sensitive to covariance matrix error and steering vector mismatch. An extension of the conventional linear constraint minimum variance (LCMV) method is proposed where the desired response vector is complex numbers instead of real numbers. Through adding a phase constraint on the desired output response, this method maintains a distortionless magnitude response together with phase response. Numerical results demonstrate that the method usually gives good robustness against the covariance error and steering vector mismatch.

K-space reconstruction of magnetic resonance inverse imaging (K-InI) of human visuomotor systems

Using simultaneous measurements from multiple channels of a radio-frequency coil array, magnetic resonance inverse imaging (InI) can achieve ultra-fast dynamic functional imaging of the human with whole-brain coverage and a good spatial resolution. Mathematically, the InI reconstruction is a generalization of parallel MRI (pMRI), which includes image space and k-space reconstructions. Because of the auto-calibration technique, the pMRI k-space reconstruction offers more robust and adaptive reconstructions compared to the image space algorithm. Here we present the k-space InI (K-InI) reconstructions to reconstruct the highly accelerated BOLD-contrast fMRI data of the human brain to achieve 100 ms temporal resolution. Simulations show that K-InI reconstructions can offer 3D image reconstructions at each time frame with reasonable spatial resolution, which cannot be obtained using the previously proposed image space minimum-norm estimates (MNE) or linear constraint minimum variance (LCMV) spatial filtering reconstructions. The InI reconstructions of in vivo BOLD-contrast fMRI data during a visuomotor task show that K-InI offer 3 to 5 fold more sensitive detection of the brain activation than MNE and a comparable detection sensitivity to the LCMV reconstructions. The group average of the high temporal resolution K-InI reconstructions of the hemodynamic response also shows a relative onset timing difference between the visual (first) and somatomotor (second) cortices by 400 ms (600 ms time-to-peak timing difference). This robust and sensitive K-InI reconstruction can be applied to dynamic MRI acquisitions using a large-n coil array to improve the spatiotemporal resolution.

Linear constraint minimum variance beamformer functional magnetic resonance inverse imaging

Accurate estimation of the timing of neural activity is required to fully model the information flow among functionally specialized regions whose joint activity underlies perception, cognition and action. Attempts to detect the fine temporal structure of task-related activity would benefit from functional imaging methods allowing higher sampling rates. Spatial filtering techniques have been used in magnetoencephalography source imaging applications. In this work, we use the linear constraint minimal variance (LCMV) beamformer localization method to reconstruct single-shot volumetric functional magnetic resonance imaging (fMRI) data using signals acquired simultaneously from all channels of a high density radio-frequency (RF) coil array. The LCMV beamformer method generalizes the existing volumetric magnetic resonance inverse imaging (InI) technique, achieving higher detection sensitivity while maintaining whole-brain spatial coverage and 100 ms temporal resolution. In this paper, we begin by introducing the LCMV reconstruction formulation and then quantitatively assess its performance using both simulated and empirical data. To demonstrate the sensitivity and inter-subject reliability of volumetric LCMV InI, we employ an event-related design to probe the spatial and temporal properties of task-related hemodynamic signal modulations in primary visual cortex. Compared to minimum-norm estimate (MNE) reconstructions, LCMV offers better localization accuracy and superior detection sensitivity. Robust results from both single subject and group analyses demonstrate the excellent sensitivity and specificity of volumetric InI in detecting the spatial and temporal structure of task-related brain activity.

Annihilation-reordering look-ahead pipelined CORDIC-based RLS adaptive filters and their application to adaptive beamforming

The novel annihilation-reordering look-ahead technique is proposed as an attractive technique for pipelining of Givens rotation (or CORDIC)-based adaptive filters. Unlike the existing relaxed look-ahead, the annihilation-reordering look-ahead does not depend on the statistical properties of the input samples. It is an exact look-ahead based on CORDIC arithmetic, which is known to be numerically stable. The conventional look-ahead is based on multiply-add arithmetic. The annihilation-reordering look-ahead technique transforms an orthogonal sequential adaptive filtering algorithm into an equivalent orthogonal concurrent one by creating additional concurrency in the algorithm. Parallelism in the transformed algorithm is explored and different implementation styles including pipelining, block processing, and incremental block processing are presented. Their complexities are also studied and compared. The annihilation-reordering look-ahead is employed to develop fine-grain pipelined QR decomposition-based RLS adaptive filters. Both QRD-RLS and inverse QRD-RLS algorithms are considered. The proposed pipelined architectures can be operated at arbitrarily high sample rate without degrading the filter convergence behavior. Stability under finite-precision arithmetic are studied and proved for the proposed architectures. The pipelined CORDIC-based RLS adaptive filters are then employed to develop high-speed linear constraint minimum variance (LCMV) adaptive beamforming algorithms. Both QR decomposition-based minimum variance distortionless response (MVDR) realization and generalized sidelobe canceller (GSC) realization are presented. The complexity of the pipelined architectures are analyzed and compared. The proposed architectures can be operated at arbitrarily high sample rate and consist of only Givens rotations, which can be scheduled onto CORDIC arithmetic-based processors.