Direction and polarization estimation for coherent sources using vector sensors

A two-dimensional direction-of-arrival （DOA） and polarization estimation algorithm for coherent sources using a linear vector-sensor array is presented. Two matrices are first constructed by the receiving data. The ranks of the two matrices are only related to the DOAs of the sources and independent of their coherency. Then the source’s elevation is resolved via the matrix pencil （MP） method, and the singular value decomposition （SVD） is used to reduce the noise effect. Finally, the source’s steering vector is estimated, and the analytics solutions of the source’s azimuth and polarization parameter can be directly computed by using a vector cross-product estimator. Moreover, the proposed algorithm can achieve the unambiguous direction estimates, even if the space between adjacent sensors is larger than a half-wavelength. Theoretical and numerical simulations show the effectiveness of the proposed algorithm.

Direction finding of coexisted independent and coherent signals using electromagnetic vector sensor

The existing direction of arrival （DOA） estimation algorithms based on the electromagnetic vector sensors array barely deal with the coexisting of independent and coherent signals. A two-dimensional direction finding method using an L-shape electromagnetic vector sensors array is proposed. According to this method, the DOAs of the independent signals and the coherent signals are estimated separately, so that the array aperture can be exploited sufficiently. Firstly, the DOAs of the independent signals are estimated by the estimation of signal parameters via rotational invariance techniques, and the influence of the co- herent signals can be eliminated by utilizing the property of the coherent signals. Then the data covariance matrix containing the information of the coherent signals only is obtained by exploiting the Toeplitz property of the independent signals, and an improved polarimetric angular smoothing technique is proposed to de-correlate the coherent signals. This new method is more practical in actual signal environment than common DOA estimation algorithms and can expand the array aperture. Simulation results are presented to show the estimating performance of the proposed method.

Array gain for a conformal acoustic vector sensor array:An experimental study

An acoustic vector sensor can measure the components of particle velocity and the acoustic pressure at the same point simultaneously, which provides a larger array gain against the ambient noise and a higher angular resolution than the omnidirectional pressure sensor. This paper presents an experimental study of array gain for a conformal acoustic vector sensor array in a practical environment. First, the manifold vector is calculated using the real measured data so that the effects of array mismatches can be minimized. Second, an optimal beamformer with a specific spatial response on the basis of the stable directivity of the ambient noise is designed, which can effectively suppress the ambient noise. Experimental results show that this beamformer for the conformal acoustic vector sensor array provides good signal-to- noise ratio enhancement and is more advantageous than the delay-and-sum and minimum variance distortionless response beamformers.

Joint 2D-DOA and polarization estimation for sparse nonuniform rectangular array composed of spatially spread electromagnetic vector sensor

In this paper,a sparse nonuniform rectangular array based on spatially spread electromagnetic vector sensor(SNRASSEMVS)is introduced,and a method for estimating 2D-direction of arrival(DOA)and polarization is devised.Firstly,according to the special structure of the sparse nonuniform rectangular array(SNRA),a set of accurate but ambiguous direction-cosine estimates can be obtained.Then the steering vector of spatially spread electromagnetic vector sensor(SSEMVS)can be extracted from the array manifold to obtain the coarse but unambiguous direction-cosine estimates.Finally,the disambiguation approach can be used to get the final accurate estimates of 2DDOA and polarization.Compared with some existing methods,the SNRA configuration extends the spatial aperture and refines the parameters estimation accuracy without adding any redundant antennas,as well as reduces the mutual coupling effect.Moreover,the proposed algorithm resolves multiple sources without the priori knowledge of signal information,suffers no ambiguity in the estimation of the Poynting vector,and pairs the x-axis direction cosine with the y-axis direction cosine automatically.Simulation results are given to verify the effectiveness and superiority of the proposed algorithm.

Application of frequency estimation algorithm based on a single vector sensor in underwater acoustic communication

A method of high resolution frequency estimation based on a single vector sensor using ESPRIT (Estimating Signal Parameters via Rotational Invariance Techniques) algorithm is proposed and applied to the underwater acoustic (UWA) communication system of frequency modulation. Higher resolution frequency estimation can be obtained by this algorithm using fewer snapshots comparing with the sound intensity frequency estimation. Results of simulation and lake experiment show that the proposed algorithm can improve the communication data rate and reduce the bandwidth of the system. Because higher signal-to-noise ratio (SNR) is demanded, this algorithm can be used in high speed short range UWA communication at present.

Joint multiple parameters estimation for coherent chirp signals using vector sensor array

In this paper, an algorithm is proposed to estimate starting frequency （SF）, chirp rate （CR）, 2-D direction-of-arrivals （DOA） and polarization of coherent chirp signals with vector sensor arrays. The fractional Fourier transformation （FRFT） is used to estimate SF and CR of chirp signals in this method. And a new correlation matrix is reconstructed to suppress the noise. The property of the vector sensor array is employed to solve the problem of insufficient rank from signal coherence. The L-shaped uniform array of expend aperture is used to improve the precision of es- timation, and the method of solving the ambiguity of angle under the condition of coherent signals is presented. The performance of this algorithm is compared with that of spatial smoothing method to verify the efficacy of this approach.

A subspace fitting algorithm of acoustic vector sensor array and corresponding matrix pre-filter design

In order to ease the pass-band response distortion of the matrix pre-filter,a simple approach for designing matrix spatial filter is proposed,which minimizes the sum of the k maximal distortion norm（k is the number of the constraint points）within the pass-band,while constraining the filter response within the stop-band.Considering the costly amount of calculation of the high-resolution methods,an algorithm with small amount of calculation based on matrix pre-filtering and subspace fitting using acoustic vector array（MF-VSSF）is proposed.Through joint processing of signal subspace of both pressure and particle velocity,the pre-filtering matrix and the signal subspace is decreased to M-dimensional（M is the number of array-element）,hence reduces the time-consumption of the matrix pre-filter design and DOA searching.Simulation results show that,the method offers the same performance as MUSIC with pre-filtering,but has much lesser amount of calculation.Moreover,the designed prefilter can efficiently suppress the interference in the stop-band and improve the estimation and resolution performance of successive DOA estimators.

Influences of prolate spheroidal baffle of sound diffraction on spatial directivity of acoustic vector sensor

The directivity of acoustic vector sensor (AVS) will be distorted by the sound diffraction of the AVS carrier. In this paper,the scattering of a plane acoustic wave from a prolate spheroid baffle is considered. At first,the sound diffraction of prolate spheroidal baffle is established,then the mathematical expressions of sound pressure field and particle vibration velocity field of sound diffraction are derived and the characteristic of the directivity of pressure and velocity of sound diffraction field at different frequencies and distances is analyzed. The directivity of AVS is determined by the amplitude and phase difference of diffraction wave and incident wave,which possesses a close relationship with frequency and incident angle. Finally,the calculated results are compared with the experimental results.

Min-norm spatial spectrum based on acoustic vector-sensor linear array

The high resolution of DOA(direction of arrival) estimation could be obtained by using the min-norm algorithm. In this paper, the expression of the min-norm spatial spectrum based on acoustic vector-sensor(AVS) linear arrays was presented and simulation study was carried out. Results of simulations indicated that left/right ambiguity could be removed and better performance for DOA estimation was obtainable when dealing with sources close to endfire than using pressure hydrophone linear arrays, and the interelement spacing was allowed to exceed the half-wavelength upper limit. A three-element vector hydrophone linear array with two meters interspace was designed. The AVS experiment was carried out at Songhua Lake in Jinlin Province. Experimental results show a high resolution tracking of targets can be obtained using the min-norm algorithm.

Fast direction of arrival algorithm based on vector-sensor arrays using wideband sources

An acoustic vector sensor(AVS)can capture more information than a conventional acoustic pressure sensor(APS).As a result,more output channels are required when multiple AVS are formed into arrays,making processing the data stream computationally intense.This paper proposes a new algorithm based on the propagator method for wideband coherent sources that eliminates eigen-decomposition in order to reduce the computational burden.Data from simulations and lake trials showed that the new algorithm is valid:it resolves coherent sources,breaks left/right ambiguity,and allows inter element spacing to exceed a half-wavelength.

An Efficient and Robust Fall Detection System Using Wireless Gait Analysis Sensor with Artificial Neural Network (ANN) and Support Vector Machine (SVM) Algorithms

In this work, a total of 322 tests were taken on young volunteers by performing 10 different falls, 6 different Activities of Daily Living (ADL) and 7 Dynamic Gait Index (DGI) tests using a custom-designed Wireless Gait Analysis Sensor (WGAS). In order to perform automatic fall detection, we used Back Propagation Artificial Neural Network (BP-ANN) and Support Vector Machine (SVM) based on the 6 features extracted from the raw data. The WGAS, which includes a tri-axial accelerometer, 2 gyroscopes, and a MSP430 microcontroller, is worn by the subjects at either T4 (at back) or as a belt-clip in front of the waist during the various tests. The raw data is wirelessly transmitted from the WGAS to a near-by PC for real-time fall classification. The BP ANN is optimized by varying the training, testing and validation data sets and training the network with different learning schemes. SVM is optimized by using three different kernels and selecting the kernel for best classification rate. The overall accuracy of BP ANN is obtained as 98.20% with LM and RPROP training from the T4 data, while from the data taken at the belt, we achieved 98.70% with LM and SCG learning. The overall accuracy using SVM was 98.80% and 98.71% with RBF kernel from the T4 and belt position data, respectively.

Soft sensor design for hydrodesulfurization process using support vector regression based on WT and PCA

A novel method for developing a reliable data driven soft sensor to improve the prediction accuracy of sulfur content in hydrodesulfurization(HDS) process was proposed. Therefore, an integrated approach using support vector regression(SVR) based on wavelet transform(WT) and principal component analysis(PCA) was used. Experimental data from the HDS setup were employed to validate the proposed model. The results reveal that the integrated WT-PCA with SVR model was able to increase the prediction accuracy of SVR model. Implementation of the proposed model delivers the best satisfactory predicting performance(EAARE=0.058 and R2=0.97) in comparison with SVR. The obtained results indicate that the proposed model is more reliable and more precise than the multiple linear regression(MLR), SVR and PCA-SVR.

Influences on the Spatial Directivity of Acoustic Vector Sensor by Finite Cylinder Baffle and Future Prospects About Eliminating the Effects

The directivity of acoustic vector sensor can be distorted by the sound diffraction wave of baffle.According to Helmholtz integral equation,the directivity of acoustic vector sensor under the condition of finite cylinder baffle is calculated by using boundary element method(BEM).Considering the problem of nearly singular integrals of BEM,the exponent parts of fundamental solutions are expanded in trigonometric functions.The singular and the nonsingular parts are separated:the nonsingular parts are calculated by Gaussian integral method;the singular parts are regularized by subsection integral method.Then the surface integrals are reduced into line integrals along the elements' contour which can be calculated by Gaussian integral method.The sound diffraction field of a plane wave under the condition of finite cylinder baffle at different frequencies and incident angles is calculated,and the characteristics of directivity of pressure and vibration velocity at different frequencies are analyzed.The experimental data are treated and the errors between the experimental and theoretical results are analyzed.Finally,according to the research results about the influences on the directivity of acoustic vector sensor by baffle at present,some future prospects about eliminating the effects of sound diffraction field by baffle are presented.

Improvement of the prediction performance of a soft sensor model based on support vector regression for production of ultra-low sulfur diesel

A novel data-driven, soft sensor based on support vector regression （SVR） integrated with a data compression technique was developed to predict the product quality for the hydrodesulfurization （HDS） process. A wide range of experimental data was taken from a HDS setup to train and test the SVR model. Hyper-parameter tuning is one of the main challenges to improve predictive accuracy of the SVR model. Therefore, a hybrid approach using a combination of genetic algorithm （GA） and sequential quadratic programming （SQP） methods （GA-SQP） was developed. Performance of different optimization algorithms including GA-SQP, GA, pattern search （PS）, and grid search （GS） indicated that the best average absolute relative error （AARE）, squared correlation coefficient （R2）, and computation time （CT） （AARE = 0.0745, R2 = 0.997 and CT = 56 s） was accomplished by the hybrid algorithm. Moreover, to reduce the CT and improve the accuracy of the SVR model, the vector quantization （VQ） technique was used. The results also showed that the VQ technique can decrease the training time and improve prediction performance of the SVR model. The proposed method can provide a robust, soft sensor in a wide range of sulfur contents with good accuracy.

UNI-VECTOR-SENSOR DIRECTION FINDING WITH REGULARIZED ESPRIT

The regularized Least-Squares Estimation method of Signal Parameters via Rotational Invariance Techniques (LS-ESPRIT) is herein proposed for Direction-Of-Arrival (DOA) estimation of non-Gaussian sources with only one acoustic vector-sensor. The Second-Order Statistics (SOS) and Higher-Order Statistics (HOS) of data are fused within a regularization framework. The steering vectors can be blindly identified by the regularized ESPRIT, from which the aim of DOA estimation can be achieved. Several variants of the regularized ESPRIT are discussed. A suboptimal scheme for deter- mination of the regularization parameters is also given.

Nonlinear correction of photoelectric displacement sensor based on least square support vector machine

A model of correcting the nonlinear error of photoelectric displacement sensor was established based on the least square support vector machine.The parameters of the correcting nonlinear model,such as penalty factor and kernel parameter,were optimized by chaos genetic algorithm.And the nonlinear correction of photoelectric displacement sensor based on least square support vector machine was applied.The application results reveal that error of photoelectric displacement sensor is less than 1.5%,which is rather satisfactory for nonlinear correction of photoelectric displacement sensor.

基于沙地猫群优化–最小二乘支持向量机的动态NOx排放预测

针对火电机组频繁调峰导致机组燃烧状态不稳,进而导致锅炉出口NOx浓度波动范围大的问题,提出一种基于沙地猫群优化(sand cat sarm optimization,SCSO)的最小二乘支持向量机(leastsquaressupportvectormachine,LSSVM) NOx动态预测模型。首先利用k近邻互信息计算时间延迟的同时筛选辅助变量。然后,基于SCSO算法进行输入变量阶次的选择。使用包含辅助变量时间延迟和阶次的信息作为模型的输入,SCSO算法优化最小二乘支持向量机参数,建立动态NOx排放最小二乘支持向量机预测模型(SCSO-LSSVM动态软测量模型)。最后将模型与未加入迟延的LSSVM模型,加入迟延的LSSVM模型和粒子群优化算法(particle swarm optimization,PSO)优化最小二乘支持向量机参数的动态软测量模型进行对比验证。结果表明,相较于其他模型,该文建立SCSO-LSSVM动态软测量模型均方根误差、平均绝对误差、平均绝对误差最小,预测精度最高,而且在NOx浓度剧烈波动时也能够较好地预测NOx浓度,具有很好的动态特性。

基于改进麻雀搜索算法的无线传感器网络定位研究

针对距离矢量跳(DV-Hop)算法在无线传感器网络中存在的定位误差大的问题,提出了一种基于改进麻雀搜索的无线传感器网络定位算法。首先,针对传统DV-Hop算法,修正了节点平均跳距和节点最小跳数。其次,引入Sine混沌映射、自适应惯性权重和双样本学习策略提高算法的搜索能力;最后,利用MATLAB构建仿真模型进行性能对比实验。实验结果表明:与其他节点定位算法相比,所提算法可有效提高对未知节点的定位精度和收敛速度。

定向接收低冲突率水声网络媒体接入控制协议

在全向水声通信网络场景中,较大的传播时延和较高的数据包碰撞率严重影响了网络性能。相比全向接收技术,声矢量传感器的声压和振速通过线性加权组合可以形成单边指向性,实现定向接收某个方向上的信号,进而提高网络的空间复用率。该文首先分析了声矢量传感器定向接收模式下的网络中断概率,验证定向接收技术网络应用的可行性。然后,提出了定向接收低冲突概率媒体接入控制协议(DRLCP-MAC)。该协议利用指向性接收波束握手机制建立稳定的数据传输链路,通过状态转移策略构建多对并行通信链路,缩小虚拟载波监听范围,提高网络的空间复用度。仿真结果表明,与水下冲突避免多址接入协议(MACA-U)和时隙地面多址接入协议(Slotted-FAMA)相比,DRLCP-MAC协议使信道接入成本降低了约50%和60%,网络吞吐量提升了约60%和400%,端到端时延降低了约50%和85%。