Joint direction of arrival estimation and array calibration for coprime MIMO radar

Compared to large-scale MIMO radar, coprime MIMO radar can achieve approximate estimation performance with reduced antenna number. In this paper, joint direction of arrival (DOA) estimation and array calibration for coprime multiple-input multiple-output (MIMO) radar is considered, and an iterative method for the estimations of DOA and array gain-phase errors is proposed. Based on the received data structure of coprime MIMO radar, trilinear decomposition is firstly adopted to obtain the estimations of transmit and receive direction matrices, which are perturbated by the gain-phase errors. Through equation transformation, the un-perturbated direction matrices and gain-phase errors can be iteratively updated based on Least squares (LS). Finally, the unique DOA estimation is determined from the intersection of transmit and receive direction matrices. The proposed algorithm achieves better DOA estimation and array calibration performance than other methods including estimation of signal parameters via rotational invariance techniques (ESPRIT)-like algorithm, multiple signal classification (MUSIC)-like algorithm and joint angle and array gain-phase error estimation (JAAGE) method, and it performs close to the method with ideal arrays. Multiple simulation results verify the algorithmic effectiveness of the proposed method.     

基于多核DSP的高分辨距离像运动补偿算法实现

弹载毫米波步进频率雷达能 够得到目标具有较高距离分辨率的一维距离像,但当运动目标相对雷达具有径向速度时,会 使 回波信号产生严重的偏移与畸变,故必须进行运动速度补偿。本文首先分析了步进频率雷达 高分辨距离像成像原理与目标运动速度对成像的影响,然后论述了一种基于正负调频法与最 大脉组求和法的二次速度估计算法,并在TMS320C6678多核DSP平台上提出了该算法的一种并 行实现方案,同时描述了该实现方案的任务分配、调度与核间通信过程。最后在多核DSP平 台上对1 000帧回波信号做算法测试。测试结果表明,基于多核DSP的运动补偿算法具有较高 的精度和良好的实时性。     

单基地MIMO雷达中相干目标的波达角和多普勒频率快速联合估计算法

针对单基地MIMO中相干目标的波达角（Direction-of-arrival,DOA）和多普勒频率联合估计问题,提出了一种降维-前向平滑-传播算子算法（Reduced dimension-forward spatial smoothing-propagator method,RD-FSS-PM）。该算法首先通过对接收信号进行降维变换以降低复杂度,继而利用前向平滑技术（Forward spatial smoothing,FSS）实现解相干,最后通过传播算子算法（Propagator method,PM）实现了对相干目标的波达角和多普勒频率联合估计,且无需额外配对。与传统的FSS-PM算法相比,所提算法波达角估计性能提升,多普勒频率估计性能接近而复杂度大大降低。本文同时分析了算法的理论均方误差（Mean squared error,MSE）和单基地MIMO雷达中波达角和多普勒频率联合估计问题的克拉美罗界（Cramer-Rao bound,CRB）。最后提供了详尽的仿真实验以验证算法的性能。     

A sparse direction-of-arrival estimation algorithm for MIMO radar in the presence of gain-phase errors

In this paper, the problem of direction-of-arrival (DOA) estimation for monostatic multiple-input multiple-output (MIMO) radar with gain-phase errors is addressed, by using a sparse DOA estimation algorithm with fourth-order cumulants (FOC) based error matrix estimation. Useful cumulants are designed and extracted to estimate the gain and the phase errors in the transmit array and the receive array, thus a reliable error matrix is obtained. Then the proposed algorithm reduces the gain-phase error matrix to a low dimensional one. Finally, with the updated gain-phase error matrix, the FOC-based reweighted sparse representation framework is introduced to achieve accurate DOA estimation. Thanks to the fourth-order cumulants based gain-phase error matrix estimation, and the reweighted sparse representation framework, the proposed algorithm performs well for both white and colored Gaussian noises, and provides higher angular resolution and better angle estimation performance than reduced-dimension MUSIC (RD-MUSIC), adaptive sparse representation (adaptive-SR) and ESPRIT-based algorithms. Simulation results verify the effectiveness and advantages of the proposed method.     

Direction of arrival estimation with antenna arrays based on fuzzy cerebellar model articulation controller neural network

Direction of arrival (DOA) estimation has been a challenging problem in many applications such as wireless communication, radar, sonar, and navigation. However, it is difficult to improve the angle resolution and reduce the computational complexity of super‐resolution methods. To solve these problems, the DOA estimation is viewed as a mapping problem, which can be modeled using a suitable artificial neural network trained with input‐output pairs. This article presents the use of a fuzzy cerebellar model articulation controller (FCMAC) neural network for the DOA estimation under a linear antenna array. The FCMAC neural network is a special feedforward neural network based on local approximation that can be adapted to solve the multidimensional nonlinear fitting problem. A new preprocessing scheme has been used in both training and test phase. It use magnitude and phase angles instead of the real and imaginary parts of the array covariance matrix to be the input of neural network. The proposed method avoids complex matrix eigen‐decomposition, such as multiple signal classification, and offers fast computation rate. The performance of FCMAC neural network is compared with the conventional subspace methods and the radial basis function neural network in the cases of noisy environment and coherent signal. Simulation results indicate that FCMAC neural network produces up to 61% lower error, 60% higher angle resolution, and 99% lower calculation time than other three methods, which indicates the superior performance of the proposed DOA estimation method under coherent signals and different noise levels.     

一种基于FastICA的波达方向估计新方法

独立分量分析是一种新颖的盲源分离技术，该方法作为目前信号处理领域的一项新技术，具有非常重要的理论意义和实用价值，已广泛应用于通讯、雷达信号处理、生物医学图像处理、模式识别等众多领域。简要介绍了独立分量分析的基本原理和算法，并提出将快速独立分量分析（FastICA）方法应用于波达方向估计（DOA），通过仿真实验和分析，可以得到DOA的一种简单估计，实验结果亦表明该算法在波达方向估计应用中的可行性和有效性。     

Two-dimensional angle estimation for monostatic MIMO arbitrary array with velocity receive sensors and unknown locations

In this paper, the problem of two-dimensional angle estimation for monostatic multi-input multi-output (MIMO) array is studied, and an algorithm based on the usage of velocity receive sensors is proposed. The algorithm applies the estimation method of signal parameters via rotational invariance technique (ESPRIT) algorithm to obtain automatically paired two-dimensional angle estimation. By utilizing the relationship within the outputs of velocity sensors, the rotational invariance property of ESPRIT does not depend on the array geometry any more. Hence, the proposed algorithm can provide two-dimensional DOA estimation for the MIMO array without the knowledge of sensor locations in the array. The algorithm requires no peak searches, so it has low complexity. Furthermore, it has better angle estimation performance than propagator method using the same sensor configuration. Error analysis and Cramér–Rao bound (CRB) of angle estimation in MIMO radar are derived. Simulation results verify the usefulness of the algorithm.     

A coherent direction of arrival estimation method using a single pulse

This paper addresses the problem of coherent direction of arrival (DOA) estimation in monostatic multi-input multi-output (MIMO) radar using a single pulse, and links the trilinear model to derive a coherent DOA estimation method. We use the received data to construct a set of Toeplitz matrices through which a trilinear model is formed, and then the trilinear decomposition is used to attain the DOAs of sources. The proposed algorithm is effective for a single pulse. Compared to the forward backward spatial smoothing estimation method of signal parameters via rotational invariance techniques (ESPRIT), the ESPRIT-like of Han, and the ESPRIT-like of Li algorithms, our method has better angle estimation performance. Numerical simulations present the effectiveness and improvement of our approach.     

Error associated with the direction of arrival estimation in the presence of material bodies

In many adaptive processing it is assumed that the direction of arrival of (DOA) of the signals is known. For a radar problem this is true as we know along which direction we transmitted the beam and therefore we expect the radar return to be arriving from that direction. However, this assumption is questionable when there are material bodies near or along its path. Here, we address the question as to what happens to the direction of propagation when the electromagnetic wave encounters a material body near or along its path for the noise free case. Thus the objective is to calculate the error associated with the prediction of DOA when the free space is not empty. We illustrate the error associated with the estimation of the DOA when there is a perfect electric conducting (PEC) sphere and a dielectric sphere along/near the path of propagation. A PEC and dielectric will diffract the incident electromagnetic energy. We evaluate the scattered far fields at a few points away from the obstacle. From the measured field points we predict the DOA of the signal of interest. The simulations have been carried out using an electromagnetic simulator and a DOA estimation algorithm using the Matrix Pencil Method. The examples deal with the case of both one- and two-dimensional antenna arrays and how they interpret the diffracted signals.     

联合ADS-B的最小二乘雷达系统误差估计方法

为了解决两坐标雷达系统误差估计问题,提出一种联合ADS-B的最小二乘雷达系统误差估计方法.提出方法首先将ADS-B量测从地理坐标系转换到雷达局部直角坐标系,建立统一的配准空间;其次以雷达航迹的采样时间为基准,对ADS-B航迹进行插值,构造新的ADS-B航迹;然后采用直线拟合算法分别计算雷达航迹和ADS-B航迹的直线方程,并计算两条直线的夹角,再利用该夹角补偿雷达航迹的航向角数据;最后采用最小二乘算法估计雷达系统误差.实测数据实验结果表明,与传统直线拟合方法和最小二乘方法相比,提出方法能够更有效地估计雷达系统误差;经过提出方法配准处理后,雷达航迹数据的平均斜距离误差和方位角误差分别降低71.7%和52.7%.     

基于实数遗传算法的波达方向最大似然估计算法

对空间多个窄带信号源的高分辨波达方向估计是雷达,块纳和地震等信号处理中的重要问题之一,为克服一些获取波达方向估计最大似然解算法存在的局部极值问题,提高估计精度,本文以作者提出的实数遗传算法为搜索工具,寻求波达方向最大似然估计的非线性全局最优解,所提出的实数遗传算法由含实数域结构和目标函数信息的实数交叉和变异算子构成,是较理想的获取非线性实变量函数全局最优解的方法,对非相参和全相参信源波达方向估计问题的数字仿真结果表明,本方法的估计精度明显优于交替极值等一些常规方法。     

基于概率估计的舰载DOA无源定位的仿真

在方位测量定位原理的基础上,提出了一种利用概率谱来估计目标位置的方法.该方法利用初始DOA测量数据建立扇形网格,在随后的各个测量点对已有网格进行加权,最后对生成的网格进行概率最大搜索,估计目标位置.采用这种方法既能保持低的运算量,以较快的速度实现目标估计,同时又具有较高的目标检测性能.建立了该方法的数学模型,给出了具体情况下的定位误差与测量点个数、目标位置、斜距、测角精度以及平台速度之间的关系.通过计算机仿真,分析了实际应用的可行性,并就提高定位精度的方法进行了讨论.     

A modular neural network for direction-of-arrival estimation of two sources

This work addresses the problem of estimating the direction-of-arrival (DOA) of two sources using an array of sensors. This problem is mostly useful in radar applications, where we have few targets at each range bin. Super-resolution algorithms, such as maximum likelihood (ML) estimation and multiple signal classification (MUSIC), have been applied to this problem, but the former involves high computation efforts, while the later has poor estimation performance for coherent sources. In this work, we propose a DOA estimation network, named RBF-AML, which combines the approximated ML (AML) estimator and a radial basis function (RBF) neural network (NN). In the proposed RBF-AML network, the entire two dimensional DOA space is divided into multiple sectors covered by RBF experts. The AML function is then used as a mediator among the experts and selects the most suitable one as the final output of the system. The performance of the RBF-AML network for a two coherent sources case in a Y shape array configuration is evaluated. We show that the performance of the RBF-AML network is similar to the performance of the classical AML DOA estimation for various signal-to-noise ratios (SNRs), phase of the correlation coefficient and signal-to-interference ratios (SIRs). Furthermore, the RBF-AML network requires fewer computational efforts than the classical AML DOA estimation and therefore is an attractive choice for real-time applications.     

An iterative approach for sparse direction-of-arrival estimation in co-prime arrays with off-grid targets

This paper addresses the problem of direction of arrival (DOA) estimation by exploiting the sparsity enforced recovery technique for co-prime arrays, which can increase the degrees of freedom. To apply the sparsity based technique, the discretization of the potential DOA range is required and every target must fall on the predefined grid. Off-grid target can highly deteriorate the recovery performance. To the end, this paper takes the off-grid DOAs into account and reformulates the sparse recovery problem with unknown grid offset vector. By introducing a convex function majorizing the given objective function, an iterative approach is developed to gradually amend the offset vector to achieve final DOA estimation. Numerical simulations are provided to verify the effectiveness of the proposed method in terms of detection ability, resolution ability and root mean squared estimation error, as compared to the other state-of-the-art methods.     

基于高速定点DSP的雷达信号处理实验系统

本文介绍了一个基于高速定点DSP的雷达信号处理实验系统的设计和研制．其中包括雷达中频信号采集．多种雷达信号的设计和产生。雷达回波的脉冲压缩处理、动目标显示、动目标检测和恒虚警处理。实验系统以ADI公司最新推出的Blackiln系列高速定点DSP芯片BF53x为核心，在多任务管理软件VDK的控制下．各软件模块以独立线程的方式并行工作．每个模块的参数及相互之间的连接方式通过PC上的LabView软件界面进行设置。     

一种V型阵列的二维波达方向估计的新方法

波达方向估计是阵列信号处理的一个重要研究方向,在雷达、通信、声纳、地震勘测等领域都有着广泛的应用前景.它已成为阵列无源探测和智能天线中的关键技术.针对二维信号,本文研究一种基于V型阵二维波达方向估计的新算法.该算法根据阵列结构的特点形成多个需要的相关矩阵,构造一个特殊大矩阵并经特征分解获得信号子空间的估计,最后利用2D-ESPRIT方法实现二维角度估计,可以解决β角兼并信号的波达方向估计问题,无需谱峰搜索且信号参数自动配对.最后用计算机仿真验证了该算法的有效性.     

ADSP21160实现CDMA信号波达方向估计

本文针对CDMA系统,提出使用DSP器件ADSP21160实现对感兴趣用户的波达方向和估计的算法。该算法是对传统MUSIC方法的推广与变形,克服了要求接收信号数小于阵元数的局限,算法运算量小,能有效估计CDMA信号的到达方向。     

激光雷达集成电路存储单元上电复位状态机设计

为了提高激光雷达集成电路存储单元上电复位的稳定性,提出基于集成DSP的激光雷达集成电路存储单元上电复位状态机设计方法。构建激光雷达集成电路存储单元复位状态机的总体结构模型,采用内核电源电路进行单极点高通滤波控制,通过由DSP集成信息模块等组成的上电复位机控制的方法,进行激光雷达集成电路存储单元的程序加载和基线恢复控制,通过基线恢复器实现激光雷达集成电路存储单元的掉电复位和连通性测试,实现激光雷达集成电路的逻辑时序控制和上电状态机设计,实现激光雷达集成电路存储单元的硬件优化设计。仿真结果表明,设计的激光雷达集成电路存储单元上电复位状态机稳定性较好,集成控制性能较强,提高了激光雷达集成信号采集能力。     

多核DSP的SF与PD联合运动补偿算法实现

对脉组间调频步进信号进行处理可以直接获得合成超带宽距离像及目标速度信息,获得的距离像具有较高的分辨率。然而目标的运动将会导致成像产生距离迁徙及波形失真,所以必须对目标的运动进行速度补偿。本文在论述了脉组间参差脉冲重复周期(SPRT)的速度补偿新方法及PD+调频步进体制联合工作模式的基础上,提出了基于TMS320C6678多核DSP的目标运动补偿算法的并行实现方法,该方法论述了目标运动补偿的任务级并行流水与核间高效通信。研究结果表明,基于多核DSP的复杂运动补偿算法具有较好的实时性及较高的目标成像精度。     

DSP实现CDMA信号波达方向与多径时延联合估计

本文针对CDMA系统,提出使用DSP器件ADSP21160实现对感兴趣用户的波达方向和多径时延进行联合估计的算法。该算法是对传统MUSIC方法的推广与变形,克服了要求接收信号数小于阵元数的局限,能有效估计时延不同、波达方向相差很小的多径信号的参数。