天线组阵全频谱合成性能分析

天线组阵FSC(全频谱合成)方案是一种最优的天线组阵信号合成方案。本文分析了相位估计方差对FSC合成性能的影响,对开环方法和闭环方法的相位估计方差进行了进一步分析,给出了FSC合成性能与单路信噪比、相关带宽、积分时间、环路带宽等因素之间的关系。技术验证试验结果表明,在标准TT&C测控和BPSK数传2种体制下,合成效率均达到90%以上。     

天线组阵技术研究及其在我国深空测控通信系统中的应用

研究深空通信系统中天线组阵技术实现方案,并通过数值仿真对几种基本的天线组阵方案和算法的性能进行比较分析。仿真结果表明,采用SUMPLE算法的全频谱合成技术能够获得很好的合成效率,是适合我国深空探测工程测控通信系统设计的有效方案,有望在我国后续的深空探测工程中得到应用。     

基于VLBI的上行组阵标校技术研究

天线组阵能否完全替代大口径天线有一个关键性难题,就是天线阵是否支持上行链路组阵。深空航天器无法将不同地面天线的上行信号对齐,所以上行链路信号的调整必须在地面完成。针对上行组阵发射机相位调整问题,提出一种基于VLBI(Very Long Baseline Interferometry,甚长基线干涉测量)技术的接收模式天线上行组阵标校方案,并对标校精度进行了简要分析。将上行链路时延分解为几何时延和发射系统时延,建立了几何时延模型,通过标定接收时延和发射时延,便可以得到天线阵元间的相位标校值。理论分析结果表明,该方案具有一定的可行性,对上行组阵相位标校的研究具有一定的借鉴意义。     

星载DBF接收天线误差校正算法

 提出了一种改进的基于子空间的星载数字波束形成（DBF）接收天线的误差校正方法。根据阵列互耦效应主要取决于阵元间的几何距离这一特点,利用阵元空间排布的对称性简化了天线的互耦模型,将天线的互耦效应及射频(RF)通道失配统一为通道间的幅相不一致。通过在基带注入特定的阵内相位差降低了子空间算法的计算复杂度。在建立7单元正六边形平面阵仿真模型的基础上,分析不同信噪比(SNR)环境下校正算法的性能,经比较得出可以通过提高发射信号功率改善误差估计的精度,验证了算法的有效性。仿真分析结果表明该算法在卫星通信系统中有一定的工程应用价值。     

运动单阵元被动合成阵列波达方向估计

 提出了一种运动单阵元被动合成阵列波达方向(DOA)估计算法。该算法基于被动合成阵列(PSA)的概念,结合空间谱估计的思想构建了运动单阵元被动合成阵列模型,通过多次不同速度合成阵列过程实现对信号DOA的无模糊估计。通过对单次匀速合成阵列过程进行分析得到,在假设信号频率已知条件下,合成阵列算法能够达到与同孔径实阵列多重信号分类（MUSIC）算法相当的DOA估计性能。仿真验证了被动合成阵列与同孔径实阵列的渐近等效性及算法的有效性。     

深空探测多天线组阵的增益损失研究

为定量分析上行天线组阵增益损失的影响因素,提出一种通过建立数学模型分析合成损耗的估计方法.首先基于天线原理和电磁波传输理论建立了天线组阵上行链路信号的数学模型,以统计学理论对数学模型进行分析,得到了影响上行链路信号的若干因素.通过分析得出,深空探测信号在远距离传输中因大气相位扰动引起的误差是造成天线组阵增益损失的最主要因素.再通过对大气相位扰动误差的进一步分析,构建S频段和X频段下大气相位扰动的空间自相关模型和时间自相关模型,得到组阵基线长度和工作仰角同合成损耗的关系.经过对各误差源的分析可知,在目前的技术水平下,能够满足上行天线组阵工程应用的精度要求.     

深空大规模天线组阵布局设计

针对深空测控通信背景下大规模组阵的天线布局优化问题,给出了优化目标(即单元波束内合成波束附近的旁瓣最小化)以及优化边界条件(包括天线数、天线口径、工作频段、最小间距和最大布局范围等),并在此基础上建立了优化模型,理论分析表明,布局优化后的归一化旁瓣峰值比布局随机化的归一化旁瓣峰值低lnN/N(N为天线数)。最后基于梯度优化算法给出具体的优化流程并进行了仿真,仿真结果表明,在给出的优化边界条件下,优化后的合成波束方向图旁瓣峰值降到约-10dB,与理论分析结果基本一致。     

星载全空间可见GPS接收系统定位精度分析及验证

针对低轨空间科学卫星在轨任意姿态无固定对天面造成的无法连续GPS(Global Positioning System,全球定位系统)定位的问题,在HXMT(Hard X-ray Modulation Telescope,硬X射线调制望远镜)卫星中提出了星载全空间可见的GPS接收系统,并指出了双天线多径干扰对定位精度的影响。建立模型,对双天线下主瓣直达信号和后瓣镜像干扰信号叠加合成的信号进行定位精度分析,结果表明,空间中83.3%以上的GPS卫星信号对该系统带来的定位误差影响不大于单天线的定位误差影响。通过半物理动态仿真验证以及外场试验进行验证,实际工程测试数据结果表明采用剔星策略后定位精度提高约13.4%。从而证明了星载全空间可见的GPS接收系统方案的正确性,并可广泛应用于工程实际。     

旋转飞行器应答式速度测量研究

由于受单天线的方向性限制,采用传统的天线模式不能实现对旋转飞行器连续跟踪测量。通过多天线分集和合成2种模式,可以实现信号的全向接收和转发,但同时带来接收信号阶跃以及信号干涉区等新问题,这些问题会导致信号变化剧烈,能否正确描述信号动态下的特征,并分析其对设备多普勒回路的影响是实现连续高精度跟踪的关键。本文推导了多天线动态下的双程应答多普勒测速方程,详细分析了分集、合成2种天线模式下信号的特征及信号对多普勒回路的影响,并根据分析结果对多普勒回路提出改进建议。     

基于分布式地面站天线的空间功率合成

卫星导航系统(GNSS)地面站天线对卫星进行上行注入时,信号到达卫星时较弱,容易受到干扰,故地面站注入天线需同时具备平时多目标注入和干扰时单目标功率增强的能力。利用卫星导航系统中地面站之间能够实现精密时间同步的特点,提出了一种基于分布式卫星导航地面站抛物面天线的空间功率合成方法,使用相位预补偿实现分布式天线阵到达目标卫星信号的相位粗同步;分析了相位误差、辐射功率误差对空间功率合成效率的影响,得到了阵元初始相位标定精度与相对定位精度的约束关系;并对合成信号的抗干扰能力和信号质量进行了研究。理论和仿真结果表明,当相位精度因子小于0.2时,4个等辐射功率天线在10°仰角以上波束扫描范围内的功率合成效率均在75%以上,且可以通过控制初始相位标定精度与相对定位精度实现更高的合成效率;而在合成效率要求75%以上时,天线辐射功率误差对合成效率的影响基本可以忽略。采用分布式波束扫描天线能够对地面站上行注入进行功率增强,可实现注入波束和功率的灵活配置,有效解决制约机动式和小型化地面站功率提升的瓶颈问题。     

Results from a Four-Element Adaptive Antenna Experiment

Experimental results from a four-element, linear, half-wavelength spacing, adaptive-array antenna under the control of the least mean square (LMS) algorithm are presented. The array is found to be capable of nulling a 70-MHz signal to -35 dB below a desired signal over a 5-MHz bandwidth. The antenna processing gain is constant over a desired signal-to-jammer signal power ratio range from -20 dB to 5 dB. A sharp reduction in processing gain is observed for angular separations between jammer and desired signal of less than 10°. Antenna patterns taken with weights set in 300 iterations of the LMS algorithm show that the one strong, one weak jammer combination has a longer weight convergence time and reduced processing gain compared with a two strong jammers combination. Contours of constant desired signal-to-jammer signal power ratio, after adaptive antenna processing, reveal a complex shape for communication between air and ground due to the finite angular resolution of the adaptive antenna.     

调频连续波SAR改进的频率尺度变换算法(英文)

本文提出了一种调频连续波SAR改进的频率尺度变换算法。调频连续波SAR在运动过程中持续不断地发射和接收信号,这种运动导致了回波信号的伸缩,对波前重建产生了严重的影响。推导了回波信号模型,给出了信号的距离-多普勒域表达式,分析了由于天线不断运动而导致的相位变化与方位向频率之间的关系。改进的频率尺度变换算法补偿了这种相位变化,实现了目标的精确成像,仿真结果表明了分析的正确性和算法的有效性。     

Modified Frequency Scaling Algorithm for FMCW SAR Data Processing

This paper presents a modified frequency scaling algorithm for frequency modulated continuous wave synthetic aperture radar （FMCW SAR） data processing. The relative motion between radar and target in FMCW SAR during reception and between transmission and reception will introduce serious dilation in the received signal. The dilation can cause serious distortions in the reconstructed images using conventional signal processing methods. The received signal is derived and the received signal in range-Doppler domain is given. The relation between the phase resulting from antenna motion and the azimuth frequency is analyzed. The modified frequency scaling algorithm is proposed to process the received signal with serious dilation. The algorithm can effectively eliminate the impact of the dilation. The algorithm performances are shown by the simulation results.     

Direction finding with a four-element Adcock-Butler matrix antennaarray

The conventional analog Adcock-Butler matrix (ABM) antenna array direction finder suffers from systemic errors, component matching problems, and bandwidth limitations. Three digital bearing estimators are developed as candidates to replace the analog signal processing portion of the ABM. Using the same antenna array, they perform all signal processing in the frequency domain, thereby benefitting from the computational efficiency of the fast Fourier transform (FFT) algorithm. The first estimator requires two analog-to-digital converters (A-D) and three antenna elements. It multiplies the difference between the discrete Fourier transforms (DFTs) of the output signals from two antenna elements with that from a third antenna element. At each frequency component, the phase of this product is a function of the bearing. A weighted least squares (LS) fit through all the phase components then gives a bearing estimate. The second estimator is similar to the first but uses three A-D and all four antenna elements. The output signal from the additional antenna element provides an independent estimate of the weights for the LS fit, giving an improvement in accuracy. The third estimator applies the physical constraint existing between the time-difference-of-arrival (TDOA) of a signal intercepted by two perpendicular sets of antenna elements. This yields a better estimator than simple averaging of the bearing from each set of antenna elements. The simulation studies used sinusoids and broadband signals to corroborate the theoretical treatment and demonstrate the accuracy achievable with these estimators. All three direction finders have superior performance in comparison with the analog ABM     

Approximate Maximum Likelihood Algorithm for Moving Source Localization Using TDOA and FDOA Measurements

 A closed-form approximate maximum likelihood (AML) algorithm for estimating the position and velocity of a moving source is proposed by utilizing the time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measurements of a signal received at a number of receivers. The maximum likelihood (ML) technique is a powerful tool to solve this problem. But a direct approach that uses the ML estimator to solve the localization problem is exhaustive search in the solution space, and it is very computationally expensive, and prohibits real-time processing. On the basis of ML function, a closed-form approximate solution to the ML equations can be obtained, which can allow real-time implementation as well as global convergence. Simulation results show that the proposed estimator achieves better performance than the two-step weighted least squares (WLS) approach, which makes it possible to attain the Cram閞-Rao lower bound (CRLB) at a sufficiently high noise level before the threshold effect occurs.     

Impulse feature extraction method for machinery fault detection using fusion sparse coding and online dictionary learning

Impulse components in vibration signals are important fault features of complex machines. Sparse coding(SC) algorithm has been introduced as an impulse feature extraction method, but it could not guarantee a satisfactory performance in processing vibration signals with heavy background noises. In this paper, a method based on fusion sparse coding(FSC) and online dictionary learning is proposed to extract impulses efficiently. Firstly, fusion scheme of different sparse coding algorithms is presented to ensure higher reconstruction accuracy. Then, an improved online dictionary learning method using FSC scheme is established to obtain redundant dictionary and it can capture specific features of training samples and reconstruct the sparse approximation of vibration signals. Simulation shows that this method has a good performance in solving sparse coefficients and training redundant dictionary compared with other methods. Lastly, the proposed method is further applied to processing aircraft engine rotor vibration signals. Compared with other feature extraction approaches, our method can extract impulse features accurately and efficiently from heavy noisy vibration signal, which has significant supports for machinery fault detection and diagnosis.     

A direct position determination method with combined TDOA and FDOA based on particle filter

The localization of a stationary transmitter using moving receivers is considered. The original Direct Position Determination (DPD) methods, with combined Time Difference of Arrival (TDOA) and Frequency Difference of Arrival (FDOA), do not perform well under low Signal-to-Noise Ratio (SNR), and worse still, the computation cost is difficult to accept when the computational capabilities are limited. To get better positioning performance, we present a new DPD algorithm that proves to be more computationally efficient and more precise for weak signals than the conventional approach. The algorithm partitions the signal received with the same receiver into multiple non-overlapping short-time signal segments, and then uses the TDOA, the FDOA and the coherency among the short-time signals to locate the target. The fast maximum likelihood estimation, one iterative method based on particle filter, is designed to solve the problem of high computation load. A secondary but important result is a derivation of closed-form expressions of the Cramer-Rao Lower Bound (CRLB). The simulation results show that the algorithm proposed in this paper outperforms the traditional DPD algorithms with more accurate results and higher computational efficiency, and especially at low SNR, it is more close to the CRLB.