DOA estimation using one-bit quantized measurements

The effects 1-bit quantization of the input samples has on the direction-of-arrival (DOA) estimation accuracy are considered. The signal model assumes a single stochastic Gaussian point source that is embedded in white Gaussian noise (WGN). The inherent limitations governed by the extreme clipping of the input data are analyzed using the Cramer-Rao bound (CRB) that is derived for a two-sensor array. In addition, several estimators for the I-bit estimation are discussed. Numerical and analytical analyses of the estimation error reveal weak dependency on signal-to-noise ratio (SNR) with singular behavior of the estimation error in certain DOA angles.     

相关色噪声下无冗余累积量稀疏表示DOA估计

针对传统均匀线阵中四阶累积量计算复杂度大、对快拍数敏感的问题,提出了一种快速去冗余的高分辨波达方向估计新方法。该方法首先通过构造选择矩阵对四阶累积量矩阵进行第1次降维处理,摒弃传统四阶累积量中大量冗余数据,然后对无冗余累积量矩阵进行矢量化并通过二次降维得到统计性能更优的向量观测模型,最后在相应的过完备基下建立观测模型的稀疏表示进行波达方向(Direction of Arrival,DOA)估计。同时将方法推广到L型阵列2维DOA估计,扩展了其应用范围。与传统的四阶累积量方法相比,该方法大大地减小了计算量,对快拍数要求不高,并且能够有效地抑制相关色噪声。理论分析和仿真实验验证了该方法对1维和2维DOA估计都具有较高的估计精度和分辨率。     

DOA estimation under unknown mutual coupling and multipath

We propose a new method for direction of arrival (DOA) estimation in the presence of multipath propagation and mutual coupling for a frequency hopping (FH) system. With the use of pilot symbols and assuming perfect time-frequency synchronization for a linear array, we take mutual coupling and multipath propagation into account, and derive a maximum likelihood (ML) estimator for both the mutual coupling matrix and DOA estimation. We then formulate an iterative alternating minimization (AM) algorithm for finding the mutual coupling and DOA parameters in an alternate manner. Simulation results illustrating the performance of the algorithm and comparison with the Cramer-Rao bound (CRB) are also presented.     

Simultaneous spacecraft attitude and orbit estimation using magnetic field vector measurements

Based on magnetometer measurements only, three-axis attitude, rate, and orbit estimation are successfully achieved. A single Augmented Dynamics Extended Kalman Filter (ADEKF) is configured by combining the spacecraft nonlinear attitude dynamics and quaternion kinematics with orbital mechanics. The filter design is adopted for three-axis stabilized spacecraft in low Earth orbits where the aerodynamic drag is the dominant source of disturbances in addition to the spacecraft magnetic residuals. To reduce the computational burden, another Interlaced Extended Kalman Filter (IEKF) is developed to uncouple the attitude/rate from the orbit dynamics. Both filters are implemented using the magnetometer measurements and their corresponding time derivatives. As a part of EgyptSat-1 flight scenario, detumbling and standby modes are used for performance testing of the ADEKF. The concept of local observability is applied to the basic filter and the stability is investigated by incorporating extensive Monte Carlo simulations with uniformly distributed initial conditions. The filter shows the capability of estimating the attitude better than 5 deg and rate of order 0.03 deg/s in each axis. In orbit estimation, the filter is capable of estimating the position with accuracy less than 8 km and velocity upto 5 m/s in each axis.     

最小二乘支持向量回归机在发动机推力估计中的应用

实现直接推力控制的首要问题就是要估计出推力.基于最小二乘支持向量回归机提出了一种Wrapper算法进行特征选择,此算法不仅能降低计算的复杂度,而且能增强模型的泛化能力.另外,在对最小二乘支持向量回归机进行稀疏性建模的时候,用QR分解法代替传统的协方差法,增强了数值的稳定性.最后,推力估计器设计的应用实例,验证了本文提出的特征选择法和QR分解法进行稀疏性建模的可行性和实用性.      

嵌套阵列最大似然估计测向算法

针对在辐射源个数未知的条件下嵌套阵列难以估计多个辐射源角度的问题,提出了基于最大似然估计(MLE)的嵌套阵列角度估计算法。算法在嵌套阵列模型的基础上,首先通过推导阵列截获多辐射源信号的最大似然函数及其梯度,利用最速下降法估计出空域中所有潜在辐射源的角度;然后,通过多元假设检验,利用最大似然比与门限进行比较,确定出空域中所有潜在辐射源中某一时刻发射信号的活跃辐射源角度,排除其余噪声形成的虚假辐射源角度,解决了在辐射源个数未知条件下嵌套阵列对多个辐射源角度估计问题。仿真结果表明:与传统多重信号分类(MUSIC)算法相比,该算法在辐射源数目未知、存在相干信号、低信噪比(SNR)、低快拍数条件下,均具有较好的角度估计精度,并且算法形成的虚拟阵列自由度是空间平滑MUSIC算法的2倍;多元假设检验法比传统信源数目估计算法在低信噪比条件下和处理相干信号方面具有明显优势。     

DOA estimation for attitude determination on communication satellites

In order to determine an appropriate attitude of three-axis stabilized communication satellites, this paper describes a novel attitude determination method using direction of arrival （DOA） estimation of a ground signal source. It differs from optical measurement, magnetic field measurement, inertial measurement, and global positioning system （GPS） attitude determination. The proposed method is characterized by taking the ground signal source as the attitude reference and acquiring attitude information from DOA estimation. Firstly, an attitude measurement equation with DOA estimation is derived in detail. Then, the error of the measurement equation is analyzed. Finally, an attitude determination algorithm is presented using a dynamic model, the attitude measurement equation, and measurement errors. A developing low Earth orbit （LEO） satellite which tests mobile communication technology with smart antennas can be stabilized in three axes by corporately using a magnetometer, reaction wheels, and three-axis magnetorquer rods. Based on the communication satellite, simulation results demonstrate the effectiveness of the method. The method could be a backup of attitude determination to prevent a system failure on the satellite. Its precision depends on the number of snapshots and the input signal-to-noise ratio （SNR） with DOA estimation.     

Monopulse DOA estimation of two unresolved Rayleigh targets

This paper provides for new approaches to the processing of unresolved measurements as two direction-of-arrival (DOA) measurements for tracking closely spaced targets rather than the conventional single DOA measurement of the centroid. The measurements of the two-closely spaced targets are merged when the target echoes are not resolved in angle, range, or radial velocity (i.e., Doppler processing). The conditional Cramer Rao lower bound (CRLB) is developed for the DOA estimation of two unresolved Rayleigh targets using a standard monopulse radar. Then the modified CRLB is used to give insight into the boresight pointing for monopulse DOA estimation of two unresolved targets. Monopulse processing is considered for DOA estimation of two unresolved Rayleigh targets with known or estimated relative radar cross section (RCS). The performance of the DOA estimator is studied via Monte Carlo simulations and compared with the modified CRLB     

DOA estimation and mutual coupling calibration with the SAGE algorithm

In this paper, a novel algorithm is presented for direction of arrival（DOA） estimation and array self-calibration in the presence of unknown mutual coupling. In order to highlight the relationship between the array output and mutual coupling coefficients, we present a novel model of the array output with the unknown mutual coupling coefficients. Based on this model, we use the space alternating generalized expectation-maximization（SAGE） algorithm to jointly estimate the DOA parameters and the mutual coupling coefficients. Unlike many existing counterparts, our method requires neither calibration sources nor initial calibration information. At the same time,our proposed method inherits the characteristics of good convergence and high estimation precision of the SAGE algorithm. By numerical experiments we demonstrate that our proposed method outperforms the existing method for DOA estimation and mutual coupling calibration.     

Sensitivity analysis of DOA estimation algorithms to sensor errors

A unified statistical performance analysis using subspace perturbation expansions is applied to subspace-based algorithms for direction-of-arrival (DOA) estimation in the presence of sensor errors. In particular, the multiple signal classification (MUSIC), min-norm, state-space realization (TAM and DDA) and estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithms are analyzed. This analysis assumes that only a finite amount of data is available. An analytical expression for the mean-squared error of the DOA estimates is developed for theoretical comparison in a simple and self-contained fashion. The tractable formulas provide insight into the algorithms. Simulation results verify the analysis     

一种稀疏阵列下的二维DOA估计方法

研究了稀疏阵列下二维波达方向（DOA）的估计问题,提出一种基于不动点迭代的空间谱估计（FPC-MUSIC）算法。首先建立基于矩阵填充的DOA估计信号模型,并验证该信号模型满足零空间性质（NSP）,其次通过不动点迭代算法将稀疏阵列信号恢复为完整信号,最后利用恢复信号估计二维DOA。该算法可在稀疏阵列下大幅度降低谱估计平均副瓣,在大幅度降低阵元数的同时具有较高的估计精度。计算机仿真表明：FPC-MUSIC算法可在稀疏阵列下准确估计二维DOA,验证了该算法的有效性和优越性。     

Accurate single-observer passive coherent location estimation based on TDOA and DOA

This paper investigates the problem of target position estimation with a single-observer passive coherent location(PCL) system. An approach that combines angle with time difference of arrival(ATDOA) is used to estimate the location of a target. Compared with the TDOA-only method which needs two steps, the proposed method estimates the target position more directly. The constrained total least squares(CTLS) technique is applied in this approach. It achieves the Cramer–Rao lower bound(CRLB) when the parameter measurements are subject to small Gaussian-distributed errors. Performance analysis and the CRLB of this approach are also studied. Theory verifies that the ATDOA method gets a lower CRLB than the TDOA-only method with the same TDOA measuring error. It can also be seen that the position of the target affects estimating precision.At the same time, the locations of transmitters affect the precision and its gradient direction.Compared with the TDOA, the ATDOA method can obtain more precise target position estimation.Furthermore, the proposed method accomplishes target position estimation with a single transmitter,while the TDOA-only method needs at least four transmitters to get the target position. Furthermore,the transmitters' position errors also affect precision of estimation regularly.     

DOA and polarization estimation via signal reconstruction with linear polarization-sensitive arrays

This paper addresses the problem of direction-of-arrival(DOA) and polarization estimation with polarization sensitive arrays(PSA), which has been a hot topic in the area of array signal processing during the past two or three decades. The sparse Bayesian learning(SBL) technique is introduced to exploit the sparsity of the incident signals in space to solve this problem and a new method is proposed by reconstructing the signals from the array outputs first and then exploiting the reconstructed signals to realize parameter estimation. Only 1-D searching and numerical calculations are contained in the proposed method, which makes the proposed method computationally much efficient. Based on a linear array consisting of identically structured sensors, the proposed method can be used with slight modifications in PSA with different polarization structures. It also performs well in the presence of coherent signals or signals with different degrees of polarization. Simulation results are given to demonstrate the parameter estimation precision of the proposed method.     

Robust adaptive radar detection in the presence of steering vector mismatches

In this paper, we consider the problem of robust radar detection in the presence of Gaussian disturbance with unknown covariance matrix. We design and assess three new robust adaptive detectors, capable of operating in the presence of unknown discrepancies between the nominal and the actual steering vector. Remarkably the new decision rules exhibit a bounded constant false alarm rate (CFAR) behavior and allow, through the regulation of a design parameter, to trade off target sensitivity with sidelobes energy rejection. Finally, computer simulations show that the proposed detectors achieve a visible performance improvement, in many situations of practical interest, over the traditional adaptive detection algorithms, especially in the presence of severe steering vector mismatches.     

DOA estimation by exploiting the amplitude modulation induced by antenna scanning

We propose a beamsplitting-like approach to estimate the directions of arrival (DOA) of multiple radar targets present in the mainlobe of a rotating antenna. The proposed method is based on the maximum likelihood (ML) technique and it avoids the need for a difference channel by exploiting knowledge of the antenna main beam pattern. Two scenarios are considered: multiple targets with unknown deterministic complex amplitudes and multiple targets with Gaussian distributed random complex amplitudes. The performance of the proposed estimator is investigated through Monte Carlo simulation and it is compared with the Cramer-Rao lower bound (CRLB).     

Antenna array beam steering using time-varying weights

The author presents a new technique of steering an array antenna by introducing time-varying phase weights. It is shown that the technique is equivalent to placing a linear phase and linear frequency offset across the array when transmitting a linear frequency modulated (LFM) waveform. The technique reduces array dispersion and increases the operating bandwidth. An element level signal generator is presented as a possible way of implementing the time-varying weights. A possible array implementation is also shown.<>     

Performance analysis for DOA estimation algorithms: unification,simplification, and observations

Subspace based direction-of-arrival (DOA) estimation has motivated many performance studies, but limitations such as the assumption of an infinite amount of data and analysis of individual algorithms generally exist in these performance studies. The authors have previously proposed a unified performance analysis based on a finite amount of data and achieved a tractable expression for the mean-squared DOA estimation error for the multiple signal classification (MUSIC). Min-Norm, estimation of signal parameters using rotational invariance techniques (ESPRIT), and state-space realization algorithms. However, this expression uses the singular values and vectors of a data matrix, which are obtained by the highly nonlinear transformation of the singular value decomposition (SVD). Thus the effects of the original data parameters such as numbers of sensors and snapshots, source coherence and separations were not explicitly analyzed. The authors unify and simplify this previous result and derive a unified expression based on the original data parameters. They analytically observe the effects of these parameters on the estimation error     

回溯降维相干分布式非圆信号DOA快速估计

在相干分布式非圆(CDNC)信号波达方向(DOA)估计中,针对阵列输出矩阵扩展后维数增加带来的较大运算量问题,基于降维的多级维纳滤波(MSWF)技术,引入回溯优化思想,提出了一种快速估计算法。该算法首先利用信号非圆特性扩展阵列输出矩阵,然后通过MSWF递推分解快速求出信号子空间,避免了计算阵列协方差矩阵及特征分解,并且在递推过程中引入回溯优化机制提高了各级匹配滤波器的估计性能,最后由最小二乘(LS)或者总体最小二乘(TLS)得到DOA估计。仿真分析表明,所提算法与相干分布式非圆信号旋转不变子空间算法(CDNC-ESPRIT)性能相当,但复杂度得到了大幅度降低,相比于基于MSWF的非圆信号快速子空间(NC-MSWF-FS)算法,在较小的复杂度代价下大幅度提升了低信噪比时的估计性能,并且对初始参考信号的选取具有了较强的鲁棒性。     

基于稀疏表示和近似(e)0范数约束的宽带信号DOA估计

针对宽带信号的波达方向(DOA)估计问题,在稀疏框架下提出一种近似(e)0范数约束的宽带信号DOA估计新算法.首先对宽带信号进行预处理,得到同一参考频率点下的接收数据,然后对其协方差矩阵元素进行加和平均运算,得到一个低维的观测向量,并在稀疏框架下进行稀疏表示,最后利用截断(e)1函数设定权值,构造逼近(e)0范数约束的稀疏重构方法,进而重构信号,获得宽带信号的DOA估计.仿真结果表明,相比于传统的宽带信号DOA估计算法,所提算法具有更高的分辨率和估计精度.     

Synchronized perturbation elimination and DOA estimation via signal selection mechanism and parallel deep capsule networks in multipath environment

State-of-the-art model-driven Direction-Of-Arrival (DOA) estimation methods for multipath signals face great challenges in practical application because of the dependence on the precise multipath model. In this paper, we introduce a framework, based on deep learning, for synchronizing perturbation auto-elimination with effective DOA estimation in multipath environment. Firstly, a signal selection mechanism is introduced to roughly locate specific signals to spatial subregion via frequency domain filters and compressive sensing-based method. Then, we set the mean of the correlation matrix’s row vectors as the input feature to construct the spatial spectrum by the corresponding single network within the parallel deep capsule networks. The proposed method enhances the generalization capability to untrained scenarios and the adaptability to non-ideal conditions, e.g., lower SNRs, smaller snapshots, unknown reflection coefficients and perturbational steering vectors, which make up for the defects of the previous model-driven methods. Simulations are carried out to demonstrate the superiority of the proposed method.