Direction finding of bistatic MIMO radar in strong impulse noise

For bistatic multiple-input multiple-output(MIMO)radar,this paper presents a robust and direction finding method in strong impulse noise environment.By means of a new lower order covariance,the method is effective in suppressing impulse noise and achieving superior direction finding performance using the maximum likelihood(ML)estimation method.A quantum equilibrium optimizer algorithm(QEOA)is devised to resolve the corresponding objective function for efficient and accurate direc-tion finding.The results of simulation reveal the capability of the presented method in success rate and root mean square error over existing direction-finding methods in different application situations,e.g.,locating coherent signal sources with very few snapshots in strong impulse noise.Other than that,the Cramér-Rao bound(CRB)under impulse noise environment has been drawn to test the capability of the presented method.

Radio Direction Finding Method to Mitigate Tsunami Risk in Sierra Leone

In this study,the Radio Direction Finding method is proposed for the detection of electromagnetic signals,in the VLF band,to try to anticipate the occurrence of potentially destructive geophysical events.The experimentation concerns the interception of electromagnetic anomalies in Sierra Leone,in the five-day time window,associated with seismic events that could potentially generate tsunamis.The area of investigation is Sierra Leone,whose coastline is subjected to tidal wave hazards triggered by earthquakes generated in the Mid-Atlantic Ridge.Although Sierra Leone is not affected by recurrent earthquakes,there is nevertheless a low probability,estimated at 2 percent,of the occurrence of destructive earthquakes in the next 50 years.Also in estimates,the risk of rogue and potentially damaging waves is estimated to strike the Sierra Leone coast at least once in the next 10 years.The Radio Direction Finding experiment carried out continuously 24/7,has shown a close relationship between increased radio-anomalies,in the frequencies of 6,000 Hz,a time window between electromagnetic anomaly detection and the imminence of an earthquake,and higher frequency times for the risk of earthquake occurrence in the Mid-Atlantic Ridge.

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.

Antenna geometry strategy with prior information for direction-finding MIMO radars

The antenna geometry strategy for direction finding （DF） with multiple-input multiple-output （MIMO） radars is studied. One case, usually encountered is practical applications, is consi- dered. For a directional antenna geometry with a prior direction, the trace-optimal （TO） criterion （minimizing the trace） on the av- erage Cramer-Rao bound （CRB） matrix is employed. A qualitative explanation for antenna geometry is provided, which is a combi- natorial optimization problem. In the numerical example section, it is shown that the antenna geometries, designed by the proposed strategy, outperform the representative DF antenna geometries.

DESIGN OF VIRTUAL UNIFORM LINEAR ARRAYS FOR EFFICIENT DIRECTION FINDING

The problem considered in this paper is to interpolate a virtual uniform array froma real two-dimensional array with arbitrary geometry via an interpolation matrix. The key to thisproblem is how to arrange these virtual sensors. It is shown that the virtual uniform linear arrayshould have the same main-lobe beam-pattern as the real array over an angular sector of interest.Simulation results are presented to illustrate the application of virtual array in direction finding.

Research on Bayesian Method of Direction Finding

In this paper, Bayesian technique of direction finding based on two different priorities is described. Some useful formulas are deduced. The performance of the method and the influence of the priors on direction finding are demonstrated by computer simulations.

Genetic algorithm-based wide-band deterministic maximum likelihood direction finding algorithm

The wide-band direction finding is one of hit and difficult task in array signal processing. This paper generalizes narrow-band deterministic maximum likelihood direction finding algorithm to the wideband case, and so constructions an object function, then utilizes genetic algorithm for nonlinear global optimization. Direction of arrival is estimated without preprocessing of array data and so the algorithm eliminates the effect of pre-estimate on the final estimation. The algorithm is applied on uniform linear array and extensive simulation results prove the efficacy of the algorithm. In the process of simulation, we obtain the relation between estimation error and parameters of genetic algorithm.

Two-Dimensional Direction Finding via Sequential Sparse Representations

The problem of two-dimensional direction finding is approached by using a multi-layer Lshaped array. The proposed method is based on two sequential sparse representations,fulfilling respectively the estimation of elevation angles,and azimuth angles. For the estimation of elevation angles,the weighted sub-array smoothing technique for perfect data decorrelation is used to produce a covariance vector suitable for exact sparse representation,related only to the elevation angles. The estimates of elevation angles are then obtained by sparse restoration associated with this elevation angle dependent covariance vector. The estimates of elevation angles are further incorporated with weighted sub-array smoothing to yield a second covariance vector for precise sparse representation related to both elevation angles,and azimuth angles. The estimates of azimuth angles,automatically paired with the estimates of elevation angles,are finally obtained by sparse restoration associated with this latter elevation-azimuth angle related covariance vector. Simulation results are included to illustrate the performance of the proposed method.

THE FOURTH-ORDER CUMULANTS BASED SPECTRALESTIMATION METHOD AND ITS APPLICATIONTO DIRECTION-FINDING

Traditional approaches of spatial spectral estimation are usually based on the second-order statistics. The higher-order cumulants and the poly-spectrum contain more information and are capable of reducing the Gaussian noise. In this paper, we present a new spectrum estimation method for direction-finding, the FOMUSIC algorithm, which is based on the eigen-structure analysis of the fourth-order cumulants. The derivation of the algorithm is given in detail and its performance is illustrated by both the computer simulations and the experiments of a direction-finding system. The obtained results demonstrate that the fourth-order cumulants based method outperforms the traditional methods, especially when the noise is an unknown colored one.

DEVELOPMENT OF FAST TWO-DIMENSIONAL HIGH-RESOLUTION DIRECTION-FINDING TECHNIQUES

A fast separable approach based on a cross array is presented, which has coarsegrained parallelism. Its computational load is far less than that of the two-dimensional (2-D) direct processing method and other existing separable approaches. In order to compensate for the performance degradation due to separable processing, two postprocessing schemes are also proposed. Some computer simulation results are provided for illustration in the end.

SIGNAL SUBSPACE METHOD FOR CALIBRATING SENSOR LOCATION OF DIRECTION FINDER

Sensor location uncertainty of array degrades severely the performance of eigenstruc-ture based direction finding system.A new calibration method of sensor location is presentedwith three far field sources whose directions are not known accurately.A signal subspace basediteration algorithm for sensor location calibration is developed and its convergence to the globaloptimal point has been shown.The guide line for selecting directions of calibrating sources isgiven.Simulation results illustrate that the new method is successful and practicable.

Super-resolution DOA estimation for correlated off-grid signals via deep estimator

This paper develops a deep estimator framework of deep convolution networks(DCNs)for super-resolution direction of arrival(DOA)estimation.In addition to the scenario of correlated signals,the quantization errors of the DCN are the major challenge.In our deep estimator framework,one DCN is used for spectrum estimation with quantization errors,and the remaining two DCNs are used to estimate quantization errors.We propose training our estimator using the spatial sampled covariance matrix directly as our deep estimator’s input without any feature extraction operation.Then,we reconstruct the original spatial spectrum from the spectrum estimate and quantization errors estimate.Also,the feasibility of the proposed deep estimator is analyzed in detail in this paper.Once the deep estimator is appropriately trained,it can recover the correlated signals’spatial spectrum fast and accurately.Simulation results show that our estimator performs well in both resolution and estimation error compared with the state-of-the-art algorithms.

POLYNOMIAL COEFFICIENT FINDING FOR ROOT-MUSIC

Abs Root-MUSIC （MUltiple Signal Classification） is the polynomial rooting form of MUSIC, namely, the spectrum peak searching is resplaced by the polynomial rooting in MUSIC implementation. The coefficients finding of the polynomial is the critical problem for Root-MUSIC and its improvements By analyzing the Root-MUSIC algorithm thoughly, the finding method of the polynomial coefficient is deduced and the concrete calculation formula is given, so that the speed of polynomial finding roots will get the bigger exaltation. The particular simulations are given and attest correctness of the theory analysis and also indicate that the proposed algorithm has preferable estimating performance.

A NOVEL UNIVERSAL PREPROCESSING APPROACH FOR HIGH-RESOLUTION DIRECTION-OF-ARRIVAL ESTIMATION

A novel universal preprocessing method is proposed to estimate angles of arrival,which is applicable to one-or two-dimensional high resolution processing based on arbitrarycenter-symmetric arrays (such as uniform linear arrays, equal-spaced rectangular planar arraysand symmetric circular arrays). By mapping the complex signal space into the real one, the newmethod can effectively reduce the computation needed by the signal subspace direction findingtechniques without any performance degradation. In addition, the new preprocessing scheme itselfcan decorrelate the coherent signals received on the array. For regular array geometry such asuniform linear arrays and equal-spaced rectangular planar arrays, the popular spatial smoothingpreprocessing technique can be combined with the novel approach to improve the decorrelatingability. Simulation results confirm the above conclusions.

多机动站交叉定位系统精度分析及优化

针对多机动站交叉定位系统观测站具有机动能力的应用特点,从测向交叉定位技术的定位原理入手,分析了影响定位精度的因素,以4站交叉定位系统为例,按目标大致分布区域已知且布设阵地条件不受限制、基于任务需求观测站需机动的随机布站两种典型应用场景,提出了根据目标分布区域优化布站阵型、根据既定阵型优化数据处理算法两种定位优化方法。在不额外增加观测站的条件下,有效提升定位精度、增大有效定位区域。该方法成本代价小、技术难度低、定位效果好,具有较好的应用推广价值。

基于球面模型的单运动平台无源定位方法

受地球曲率影响,传统基于平面直角坐标系的定位模型随着侦测距离的增大,定位误差显著增大,严重影响远距离目标定位跟踪精确度。对此,提出一种基于球面模型的单运动平台定位方法,将平面直角函数方程转换为球面三角函数方程,降低地球曲率误差影响;利用无轨迹卡尔曼滤波(UKF)算法实现复杂非线性观测方程迭代求解及远距离目标高精确度位置估计。仿真试验结果表明,基于球面模型的定位方法具有较高的定位精确度,定位精确度提升0.3%R~0.6%R。

一种长基线被动传感器数据关联方法

为了提高测向交叉定位系统多目标环境下的去鬼点性能,提出了一种长基线被动传感器数据关联方法.首先利用被动传感器量测的视线矢量信息进行交叉定位并计算定位误差协方差矩阵,然后基于两个定位点构造了一个卡方分布的检验统计量,将多目标条件下的去鬼点问题转换为假设检验问题.为了分析该方法的性能,定义了真实目标的正确关联概率和鬼点目标的错误关联概率,并在使用两部和三部被动雷达的情况下分别与倾斜角方法进行了仿真对比.仿真结果表明所提出的方法在使用三部被动雷达或多目标与被动雷达共面时关联性能更好.

一种基于三角基线干涉仪的天基定位技术

测向定位是一种计算量小、成熟度高的定位技术,常用于干涉仪等测向系统。测向定位传统采用二维正交基线测量目标入射方位角和俯仰角,通过计算方位角、俯仰角锥面与地球曲面交点完成定位计算。提出了一种基于三角基线干涉仪的天基定位技术,充分利用不同基线测向精度差异与锥面交线的构型特征,使测向精度与锥面交线构型更好地匹配,从而实现更高的定位精度。最后通过仿真验证了该方法的可行性和有效性。

基于空间频率估计的稀疏阵列测向

针对有互质型和嵌套型特征的稀疏阵列提出一种基于空间频率估计的测向方法,利用快速傅里叶变换(Fast Fourier Transform, FFT)就可以快速对同时同频多角度入射信号进行测向分辨。介绍了该方法的测向原理和处理步骤,给出了仿真结果。针对非均匀稀疏阵列,提出一种基于虚拟扩展和空间频率估计的测向方法,对该方法进行了理论分析和仿真验证。这两种方法运算量小,简单易用,工程实现非常方便。

电磁点源辐射线法用于复杂二维干涉仪测向研究

战斗机综合电子战系统采用二维干涉仪实现方位和俯仰同时测向,用于实现高精度无源定位或引导火控雷达等快速截获目标等。方位干涉仪天线通常安装在机翼前缘等位置,由于受重力或在飞行中颤动等影响,天线出现下倾而不在飞机机体水平面内,现有基于电磁辐射源远场近似平行投射原理进行测向建模过程方法非常复杂且困难。针对非正交二维干涉仪测向难题,提出了一种简洁高效的测向模型,将投射电磁波假设为空间中的辐射点源,由辐射点源到二维干涉仪天线距离差与相位差的关系,再利用辐射点源对应的空间方向线上超过一定距离时的任意点到干涉仪天线相位差近似相等的前提,得到辐射点源空间三维位置的三个方程,求解得到空间三维位置进一步得到电磁波方位俯仰的二维测向公式。对算法模型及其误差进行理论、仿真分析和实际工程验证,结果证明了该方法的有效性。