基于扩展传播算子的非圆信号测向方法

现代通信系统大量使用非圆信号,利用经典高分辨算法对其测向没有充分利用非圆信号的信息,因而性能受限.本文提出的扩展传播算子测向算法(EPM)先对数据进行共轭扩展,再利用传播算子方法(PM)测向,充分利用了非圆信号的特点,从而可对多于阵元数的信号进行测向,分辨力和测角精度优于OPM(正交传播算子测向算法)和MUSIC算法,同时保持了PM算法的特点,不需要特征分解(ED)或奇异值分解(SVD).文中还分析了EPM算法的均方误差性能和计算复杂度,得到了均方误差的解析表达式.仿真实验验证了EPM算法的优良性能,均方误差的理论结果与仿真值相符.     

内插阵列变换扩展传播算子算法

扩展传播算子(EPM)算法是首先对数据进行扩展,再利用传播算子(PM)方法进行测向的一种算法,该算法充分利用了非圆信号的特点,分辨力和估计精度优于未充分利用非圆信号信息的经典高分辨算法。但是在实际信号测向中,由于阵元位置误差的存在,算法的估计性能会受到一定的影响。因此提出一种基于内插阵列变换的扩展传播算子(VIA-EPM)算法,该算法利用真实阵列流型与虚拟阵列流型之间的变换矩阵,将真实协方差矩阵变换为虚拟协方差矩阵,再对虚拟协方差矩阵进行分块并得出扩展传播算子,进而得出算法的空间谱函数。仿真实验表明:在存在阵元位置误差的情况下,新算法通过对阵元位置校准数据进行内插阵列变换(VIA),取得与阵元位置校准的EPM算法相当的估计性能,保持了阵列扩展能力以及高估计精度,在低信噪比情况下,基于扩展协方差矩阵的VIA-EPM算法的分辨力以及估计精度均要优于基于扩展数据矩阵的VIA-EPM算法。     

基于实值传播算子的非圆信号DOA估计求根算法

为有效降低非圆信号DOA(direction of arrival)估计算法的计算量,本文提出一种非圆信号DOA估计快速算法,借助实值扩展传播算子和多项式求根方法来降低计算量。首先利用信号非圆特性构造出实值的扩展阵列输出矩阵及扩展协方差矩阵,然后使用扩展传播算子方法代替扩展协方差矩阵的特征分解得到噪声子空间,再利用均匀线阵的多项式求根方法获得目标的DOA估计值。对算法的性能仿真和计算复杂度分析表明,新算法的均方根误差性能与Euler-root-MUSIC、NC-root-MUSIC等快速算法相近,但其计算复杂度小于上述非圆信号DOA估计快速算法。优良的性能和较低的计算量使新算法具有良好的实用价值。      

Low-complexity method for HF direction finding

A new algorithm is presented with low complexity for HF (3-30 MHz) direction finding. This method is applied to different types of signal and to different configurations of circular arrays (homogeneous or heterogeneous)     

A method for direction finding under sensor gain and phaseuncertainties

An eigenstructure method is presented for direction finding in the presence of sensor gain and phase uncertainties. The method provides estimates of the directions of arrival (DOAs) of all the radiating sources as well as calibration of the sensor gains and phases. The method can be applied to low signal-to-noise ratio (SNR) and be suitable for arrays with arbitrary sensor geometries, including linear arrays. Computer simulations are presented to illustrate the performance of the proposed method     

Focusing filters for wide-band direction finding

A time-domain equivalent of the coherent signal-subspace transformations (CST) is established for wideband direction finding in a possible multipath environment using general arrays. Time-domain equivalents of focusing are derived based on the least squared error approach for general transformations, and the Taylor series expansion approach for closed-form transformations. The preprocessor is realized by a multichannel digital finite impulse response filter. For diagonal transformations, the problem reduces to implementing different delays at each sensor, which in turn leads to computational simplicity. Various tapped delay line filters for realizing an arbitrary delay are proposed and compared. Simulation results reveal that low-order filter structures in the time domain achieve similar performance to the frequency domain approach, even at detection and resolution thresholds     

Fast orthogonal search for direction finding

An efficient modelling algorithm known as fast orthogonal search (FOS) is applied to the estimation of the directions of plane waves incident on an array of sensors. An iterative approach which decreases the mean-squared error of the original FOS model is introduced. The methods are demonstrated and compared with Root-MUSIC in computer simulations.<>     

旋转基线相位干涉仪测向精度提高方法

旋转基线相位干涉仪测向法利用阵元间相位差最大值与最小值进行来波角度估计,存在噪声干扰时会产生较大测向误差。针对这一问题,采用三参数最小二乘法对解模糊后的相位差曲线进行幅度、初相和偏移量的估计,进而对相位差曲线进行拟合处理。该方法提高了旋转基线相位干涉仪测向的精度,仿真结果证明了该方法的有效性。     

被动雷达导引头旋转式相位干涉仪测向方法

研究了在超宽频段一种被动雷达导引头旋转式干涉仪无模糊测向方法。在2个相邻雷达脉冲到达时,干涉仪2次测量相位差变化不超过π的情况下,可用基于数字积分器解决测向模糊问题。而在干涉仪测量相位差变化超过π的情况下,基于相位连续特性,通过对数字积分器的结果作修正可实现无模糊测向。本文设计并实现了一种针对8 mm波段被动雷达导引头旋转式相位干涉仪测向系统,系统仿真和硬件测试结果证明了该算法的正确性和有效性。     

A parametric method for determining the number of signals innarrow-band direction finding

A novel and more accurate method to determine the number of signals in the multisource direction finding problem is developed. The information-theoretic criteria of Y. Yin and P. Krishnaiah (1988) are applied to a set of quantities which are evaluated from the log-likelihood function. Based on proven asymptotic properties of the maximum likelihood estimation, these quantities have the properties required by the criteria. Since the information-theoretic criteria use these quantities instead of the eigenvalues of the estimated correlation matrix, this approach possesses the advantage of not requiring a subjective threshold, and also provides higher performance than when eigenvalues are used. Simulation results are presented and compared to those obtained from the nonparametric method given by H. Wax and T. Kailath (1985)     

基于CNN特征的宽频段智能测向方法

当前电磁环境日益复杂,利用机器学习方法实现快速且精确的宽频段无线电测向逐渐成为研究的热点。使用卷积神经网络基于端到端的方式完成宽频段测向的方法能够在一定程度上解决宽频段相位模糊的问题,但卷积运算后特征维数大大增加,稀疏的特征影响了最后一层全连接前馈神经网络的分类效果。针对这一问题,提出将无线电测向分为特征学习任务和方向预测任务,使用卷积神经网络作为特征提取器,将通过多层卷积运算得到的结果视为二次提取的特征,作为方向预测任务的输入;针对二次提取特征的稀疏性,提出使用主成分分析算法对特征进行降维,并将稀疏性降低后的特征作为后续分类器的输入。此外,针对特征的特点,探索了几种分类模型作为分类器的效果,包括决策树、随机森林、径向基函数神经网络和K-近邻。实验结果表明,使用主成分分析算法对特征进行降维能够提升训练和测试效率;采用K-近邻构成分类器的准确度明显高于原卷积神经网络的准确度;若需要兼顾准确度和测向效率,采用随机森林构成分类器的效果最好。     

Single-snapshot robust direction finding

The paper presents a novel approach for recursively estimating the directions of arrival of incident signals as measurements are received along a sensor array. Using a single snapshot and without any statistical assumptions, the proposed method employs a robust performance criterion, which is based on worst-case gain minimization. The criterion aims to reduce the estimation error induced by worst-case amplitude and phase perturbations as well as additive noise in the array model. An algorithm that guarantees the criterion-within a first-order approximation-is developed and shown to converge. Moreover, instead of using a trial-and-error method to find a constant, minimum worst-case gain, the minimum worst-case gain is updated as each sensor measurement is processed. A step-by-step implementation of the algorithm is presented, and its computational complexity is analyzed. The performance of the new approach is evaluated by simulating the estimation algorithm for a linear array and comparing its performance with that of an existing single-snapshot algorithm.