下一代航空宽带通信B-VHF系统

将宽带VHF技术应用于地空通信中,可有效缓解当前民航通信中容量不足的问题。在介绍B-VHF的发展历程、系统和业务架构的基础上,指出重叠覆盖是B-VHF的核心思想,进而将干扰抑制和频谱感知作为B-VHF的两大关键技术。重点对射频干扰抑制中的时域和频域处理方法、对频谱感知中的能量检测法和联合检测法进行技术对比分析,进而归纳两大关键技术的一般实现方法。并对B-VHF的发展前景作了展望。     

基于多机协作的认知无人机网络能效联合优化

针对无人机（UAV）通信网络中频谱资源紧缺的问题,构建基于认知无线电的多无人机通信网络,通过多机协作频谱感知有效探索授权频谱。提出一种基于Bisection算法的迭代算法,通过联合优化感知时间和判决门限对构建的复杂非凸问题求解,显著提高了无人机次级认知网络的能量效率（EE）。分析了无人机飞行过程中能效的变化情况,仿真结果表明,存在最优感知时间使能效获得最大值,且判决门限的选择会影响该能效最优值;提出的高能效迭代算法具有较好收敛性,有效提高了认知无人机网络的能量利用率。     

涡扇发动机故障诊断中粒子滤波改进方法

针对标准粒子滤波算法诊断步数多且诊断结果噪声水平高的问题,提出伪协方差和自适应似然分布结合的改进粒子滤波算法。该算法通过使用伪协方差代替了粒子集协方差,保证采样得到的粒子能够更真实地反映突变情况,减少诊断步数;通过对似然分布自适应调整,增加其与先验分布的重叠区域,提高抽样率,增加有效粒子数,降低诊断结果噪声水平。发动机健康参数估计仿真结果表明:与标准粒子滤波算法相比,改进的粒子滤波算法能使诊断速度提高约27%,诊断精度提高约38%,有效地减少了突变故障的诊断步数,并显著降低了诊断结果的噪声水平。     

基于预估-反馈联合处理的射频噪声干扰抑制算法

针对强射频（RF）噪声干扰下脉压雷达目标检测概率较低的问题,提出了一种基于预估-反馈联合处理的射频噪声干扰抑制算法。首先,对回波信号进行盲源分离预处理,并利用分数阶傅里叶变换（FRFT）的特性对目标回波信号进行参数估计以及窄带滤波处理,滤除大部分干扰和噪声的能量;然后,在数据层运用M/N逻辑法进行点迹处理,并结合径向速度方向判决,实现对目标航迹的预估检测;最后,利用数据层对航迹状态的反馈实时修正滤波器参数,从而在信号层更好地滤除干扰能量,并对中断航迹进行剔除,完成射频噪声干扰的抑制。仿真结果表明：与现有射频噪声干扰抑制技术相比,所提算法具有更优的干扰抑制效果。     

基于Kalman滤波的高频CW电报信号自动识别

针对强噪声背景下高频CW电报信号检测算法性能严重下降、误码率较高的问题,文章提出一种基于卡尔曼滤波的高频CW电报信号同步检测识别算法。利用自同步法对CW电报信号实现位同步,进而利用卡尔曼滤波针对时变干扰噪声设置自适应阈值,对信号能量进行软判决,实现CW电报信号的自适应跟踪检测,提取有效信号进行识别。通过短波信道仿真软件和实际短波通信测试表明,该算法能够在强噪声背景下有效检测识别CW电报信号,且算法可由迭代实现。     

包含乘性噪声自适应修正的非合作目标相对导航算法

无人机（UAV）态势感知的任务是利用机载传感器对未知环境进行目标识别和引导，针对无人机与非合作目标间中远距离的相对导航问题，提出了一种基于角度和距离量测的相对状态估计算法。在现有滤波算法的基础上，为了提高精度和稳定性，本文利用了列文伯格-马夸尔特（LM）优化的思想对迭代卡尔曼滤波（IEKF）算法进行改进，提出了一种LM-IEKF算法，并推导该算法在迭代过程中的状态更新方程及协方差阵的递推公式。在此基础上，考虑到距离传感器由于信号相关特性而引入的乘性噪声，现有的加性噪声模型难以适应，因此，进一步提出了基于量测噪声自适应修正的Modified LM-IEKF方法，通过在线实时更新噪声阵提高滤波的精度，并设置渐消记忆指数平滑估计结果。算法验证结果表明，与现有的EKF、IEKF算法相比，在仅含加性噪声的情况下，LM-IEKF算法具有更好的性能；在包含乘性噪声的情况下，Modified LM-IEKF可以有效地估计量测噪声，与目前广泛使用的EKF算法相比，在综合相对位置和相对速度精度上分别提高了10%和23%。     

高速风扇噪声的频谱分析中带宽的影响

叶片通过频率分量是风扇噪声的主要成分,但在对噪声测量信号进行频谱分析中发现选择不同带宽可导致显著不同的叶片通过频率分量谱值.讨论了使用不同带宽对相同风扇噪声记录进行的快速傅里叶变换(FFT)分析过程,由于FFT分析得到的频谱声压级是带宽内谱能量和,当由于转速波动引起的叶片通过频率波动范围超过分析带宽时带宽的变化将引起声压级的差异,本文提出了获得确定声压级频谱的方法.     

基于约束最小冗余线阵与干扰对消的测向方法

针对有源干扰背景下信号源和干扰源的个数超过线阵的自由度而产生线阵饱和现象,提出一种将约束最小冗余线阵与干扰对消技术相结合的测向方法。通过将无源状态和有源状态下线阵输出数据的协方差矩阵进行对消运算去除有源干扰和噪声分量,并对约束最小冗余线阵的波达方向(DOA)估计算法进行改进,构造了新的协方差Toeplitz矩阵,有效抑制了由阵列非均匀性导致的伪峰,提高了阵列的DOA估计性能。仿真结果表明:该算法在低信噪比背景下具有抗有源干扰能力,扩展了阵列孔径,并具有较高的测向精度和鲁棒性。     

基于先验知识及其定量评估的自适应杂波抑制研究

 为了改善训练样本数受限的非均匀杂波环境中的系统检测性能,研究了基于先验知识及其定量评估的自适应杂波抑制算法。提出了使用经真实杂波信息白化后的先验杂波协方差矩阵与单位矩阵之差的谱范数,来定量评估杂波先验知识的准确程度,并给出了真实杂波协方差矩阵未知时的矩阵谱范数估计方法。结合先验知识定量评估结果,获得了具有先验知识约束时的杂波协方差矩阵最大似然估计方法。分别基于多脉冲相参雷达以及空时自适应雷达进行了杂波建模,在此基础之上分析了算法性能。仿真结果证实了该算法优于使用样本协方差矩阵及先验杂波信息形成杂波抑制权值的性能。     

鲁棒EKF在脉冲星导航系统中的应用

针对脉冲星导航系统的滤波问题,传统的扩展卡尔曼滤波(EKF)算法存在不能克服系统模型存在不确定性参数以及乘性噪声等缺陷,提出一种鲁棒EKF算法。首先,分析了状态预测误差方程和估计误差方程,利用统计学原理,得到了状态预测方差矩阵和状态估计方差矩阵计算等式。由于系统模型存在不确定性参数,状态预测协方差矩阵和状态估计协方差矩阵无法计算;因此,利用4个重要矩阵不等式,分析并找到预测方差矩阵和状态估计方差矩阵的上界。最后,利用状态估计误差协方差矩阵上界设计状态增益矩阵,使得状态估计协方差矩阵的迹最小。将该算法对脉冲星导航系统进行仿真,仿真结果验证了所提算法的有效性。     

基于新息异常检测的改进抗差自适应卡尔曼滤波算法

在工程应用中,量测异常及量测噪声统计特性的时变是引起标准卡尔曼滤波振荡甚至发散的主要原因。经典抗差Sage-Husa自适应滤波方案,对量测中的孤立型异常有所抵抗,并可在线估计量测噪声统计特性改善滤波效果,但当连续型异常值出现时,其滤波效果不佳。针对现有抗差Sage-Husa自适应滤波方案的不足,提出了新的改进滤波方法。在改进算法中,当检测到量测异常时采用模值更大的先验预测方差阵代替原算法中的后验估计方差阵,在估计量测噪声方差时起到放大作用,以降低异常量测权重,提高滤波精度;采用IGG方案构造了新的权函数,可在抑制异常影响的同时调节估计方差阵,以免连续异常时新息持续置零引起的滤波发散;采用标准卡尔曼滤波新息辅助异常检测的双重检测策略,避免了因量测噪声方差阵的调节引起检测阈值变化而导致的漏检率增高。仿真实验表明,与常规抗差自适应滤波算法相比,该方案可更加有效地抑制量测异常值的影响。     

Theory of partially adaptive radar

This work extends the recently introduced cross-spectral metric for subspace selection and dimensionality reduction to partially adaptive space-time sensor array processing. A general methodology is developed for the analysis of reduced-dimension detection tests with known and unknown covariance. It is demonstrated that the cross-spectral metric results in a low-dimensional detector which provides nearly optimal performance when the noise covariance is known. It is also shown that this metric allows the dimensionality of the detector to be reduced below the dimension of the noise subspace eigenstructure without significant loss. This attribute provides robustness in the subspace selection process to achieve reduced-dimensional target detection. Finally, it is demonstrated that the cross-spectral subspace reduced-dimension detector can outperform the full-dimension detector when the noise covariance is unknown, closely approximating the performance of the matched filter.     

A noncoherent adaptive detection technique

An adaptive detection technique suitable for both stationary and nonstationary noise environments based upon a generalized likelihood ratio test (GLRT) formulation is presented. The detector, which is statistically equivalent to a special form of the Wilks's lambda test, noncoherently combines the information contained in a pulse train of arbitrary length for decision-making purposes. The probability density function of the test under the noise only hypothesis is shown to be central χ². Under the signal plus noise hypothesis, an exact statistical characterization of the test cannot be obtained, and, therefore, a Chernoff bound is derived. Results in terms of the probability of detection versus signal-to-noise ratio (SNR) obtained from Monte Carlo simulation, the Chernoff bound, and the optimal matched filter case are examined. The performance of the noncoherent detector is shown to be a function of the covariance matrix estimate and the number of data samples     

基于先验门限优化准则的探测阈值自适应选择

针对 2维测量和 4 -sigma确认门 ,把先验检测门限优化准则和修正 Riccati方程的解析近似表示相结合 ,得到了在瑞利起伏环境下使跟踪性能优化的信号探测阈值解析表示式 ,从而使在线求解自适应信号探测阈值能比较容易地实现。通过研究和仿真发现 :在滤波稳定阶段 ,本文给出的自适应信号检测门限方法的跟踪性能优于固定虚警率方法的跟踪性能 ;基于先验检测门限优化准则实现检测 -跟踪的联合优化要求信噪比要大于一定的门限 ,在瑞利起伏环境下 ,对 2维测量和 4 -sigma确认门 ,该门限为 1 .57     

一种变信噪比条件下的恒虚警野值检测方法

野值检测又称异常值检测,是模式识别、机器智能和知识发现等领域经常面临的一个问题。当出现环境失配,数据信噪比（SNR）发生变化时,测试样本和训练样本所含噪声会有不同方差,以往的野值检测方法在虚警控制方面将会失效。针对这一问题,提出一种基于归一化残差（NR）的野值检测方法。该方法首先根据所需虚警概率和噪声方差变化情况确定野值检测门限,其次基于训练样本计算待考查模式的NR值,再比较NR值与检测门限的相对大小,从而判断待考查模式是否为野值。这一方法所依赖的检测门限对所需虚警率和噪声方差变化具有适应能力,因此可以在变信噪比条件下实现恒虚警（CFAR）野值检测。仿真实验验证了所提方法在虚警控制和野值检测方面的优越性能。     

An Adaptive Detection Algorithm

A general problem of signal detection in a background of unknown Gaussian noise is addressed, using the techniques of statistical hypothesis testing. Signal presence is sought in one data vector, and another independent set of signal-free data vectors is available which share the unknown covariance matrix of the noise in the former vector. A likelihood ratio decision rule is derived and its performance evaluated in both the noise-only and signal-plus-noise cases.     

Performance bounds for hot and cold clutter mitigation

A new method is presented for describing the theoretical interference space-time covariance matrix that will be observed in an adaptive airborne radar system under specific topographical conditions. Both hot clutter that is induced by interfering sources and cold clutter that results from the radar transmitter are considered. This method incorporates phenomenology observed under site specific conditions as well as system effects such as array geometry, receiver filtering, and system bandwidth. Use of this formulation rather than sample data analyses that are generally employed enables one to infer performance bounds for site-specific, and thus generally, heterogeneous terrain that are tighter and therefore more meaningful than the thermal noise floor limit     

Distributed target detection in compound-Gaussian noise with Rao and Wald tests

The problem of detecting distributed targets in compound-Gaussian noise with unknown statistics is considered. At the design stage, in order to cope with the a priori uncertainty, we model noise returns as Gaussian vectors with the same structure of the covariance matrix, but possibly different power levels. We also assume that a set of secondary data, free of signal components, is available to estimate the covariance matrix of the disturbance. Since no uniformly most powerful test exists for the problem at hand we devise and assess two detection strategies based on the Rao test, and the Wald test respectively. Remarkably these detectors ensure the constant false alarm rate property with respect to both the structure of the covariance matrix as well as the power levels. Moreover, the performance assessment, conducted also in comparison with the generalized likelihood ratio test based receiver, shows that the Wald test outperforms the others and is very effective in scenarios of practical interest for radar systems.     

Proposals for Simplifying Envelope Normalization in Adaptive Antenna Arrays

In adaptive antenna arrays using control loops the processing is determined under certain conditions by the covariance matrix Bmn = E(f(v*m)vn)?n), where ?1,..., ?N are N-complex Gaussian noise video signals and f is some function of the phase of ?m. Half of the storages and real multiplications could be saved by taking only the real part of ?n. It will be shown that this can be done without changing the performance of the adaptive antenna array significantly.     

Adaptive array CFAR detection

Presented here is a large class of adaptive array detection algorithms with constant false alarm rate (CFAR), so that the false alarm rate can be set to any preassigned number without knowledge of the noise covariance matrix. This class map incorporate any usual method of cell averaging and any method for array weight vector synthesis. A sufficient condition for CFAR is derived, which is easy to satisfy in practice. Basic system parameters are discussed. An example of detection performance for a simple cell-averaging detector, in which the array weight vector is synthesized by the method of diagonal loading, is provided using Monte Carlo simulations