Sensitivity Analysis of Finite-Lag Receivers

This paper presents the sensitivity analysis of a class of receivers called finite-lag receivers, introduced by the authors in [1] through [3]. Since these receivers are based on the use of fixed-lag smoothing techniques, algorithms for the calculation of large-scale and small-scale sensitivities of fixed-lag smoothing are derived using a state augmentation approach. Steady-state analysis of these algorithms shows that an explicit relation can be obtained between sensitivity coefficients of fixed-lag smoothing and filtering. The specific case of sensitivity to variations in the measurement (channel) noise is considered as an example. These results are applied to study the sensitivity performance of the finite-lag receivers for analog communication. It is shown, for example, that finite-lag receivers for AM signals, besides being superior in performance [1]-[3], [11], in terms of output SNR or error variance, are also much less sensitive to the additive noise power level, compared to zero-lag receivers.     

发动机故障诊断的变记忆约束滤波算法

为提高故障诊断可靠性,给出了3种利用多次测量信息减少测量误差影响的方法,即变记忆约束滤波算法、分段算术平均预平滑算法以及指数顶平滑算法。其中,变记忆约束滤波算法能够有效地避免由于故障方程多重共线性所引起的病态解。证明了多故障诊断问题中滤波算法与分段算术平均算法的等价性,证明了在衰减滤波的条件下（第一变记忆因子小于1）,随测量点数的增加,变记忆滤波算法的第一变记忆因子与指数平滑的平滑系数之和趋近于1。利用计算机仿真方法对于所得结论进行了检验。     

An Approach to Fixed-Point Smoothing Problems

This paper examines the possibility of deriving fixed-point smoothing algorithms through exploitation of the known solutions of a higher dimensional filtering problem. It is shown that a simple state augmentation serves to imbed the given n-dimensional smoothing problem into a 2n-dimensional filtering problem. It is further shown that computation of the smoothed estimate and the corresponding error covariance does not require implementation of the 2n-dimensional filtering equations. Some new results involving systems with or without multiple time delays and having colored observation noise have been derived in order to illustrate the versatility of the proposed technique. It is also demonstrated that the present approach leads to an easier derivation of the continuous-time fixed-point smoothing algorithm reported in the literature.     

Interacting multiple model fixed-lag smoothing algorithm forMarkovian switching systems

We investigate a suboptimal approach to the fixed-lag smoothing problem for Markovian switching systems. A fixed-lag smoothing algorithm is developed by applying the basic Interacting Multiple Model (IMM) approach to a state-augmented system. The computational load is roughly d (the fixed lag) times beyond that of filtering for the original system. In addition, an algorithm that approximates the “fixed-lag” mode probabilities given measurements up to current time is proposed. The algorithm is illustrated via a target tracking simulation example where a significant improvement over the filtering algorithm is achieved at the cost of a time delay (i.e., data up to time k are used to produce the smoothed state estimate at time k-d where the fixed large d>0). the IMM fixed-lag smoothing performance for the given example is comparable to that of an existing IMM fixed-interval smoother. Compared with fixed-interval smoothers, the fixed-lag smoothers can be implemented in real-time with a small delay     

GNSS最优载波相位平滑伪距研究

平滑常数是影响载波相位平滑伪距精度的关键参数,实际数据处理时主要依据经验设定平滑常数。这种主观设定过程缺乏理论依据,无法达到最优平滑效果。针对此问题,以适用于实时GNSS载波相位平滑伪距的经典Hatch递推滤波算法为基础,在连续时间域上分析了载波相位平滑伪距误差的主要构成,给出了总误差估算公式,分析了平滑常数对平滑精度的影响传导机制。进一步,采用令平滑总误差最小为目标的极值法推导给出了最优载波相位平滑常数的计算公式,给出了最优载波相位平滑伪距的完整处理步骤。最优平滑常数算法在数学意义上最优,大幅压缩了伪距测量误差,又不会引入过大的电离层发散误差。通过两个实际算例,证明了算法有效性。     

A novel variable-lag probability hypothesis density smoother for multi-target tracking

It is understood that the forward-backward probability hypothesis density (PHD) smoothing algorithms proposed recently can significantly improve state estimation of targets. However, our analyses in this paper show that they cannot give a good cardinality (i.e., the number of targets) estimate. This is because backward smoothing ignores the effect of temporary track drop- ping caused by forward filtering and/or anomalous smoothing resulted from deaths of targets. To cope with such a problem, a novel PHD smoothing algorithm, called the variable-lag PHD smoother, in which a detection process used to identify whether the filtered cardinality varies within the smooth lag is added before backward smoothing, is developed here. The analytical results show that the proposed smoother can almost eliminate the influences of temporary track dropping and anomalous smoothing, while both the cardinality and the state estimations can significantly be improved. Simulation results on two multi-target tracking scenarios verify the effectiveness of the proposed smoother.     

Novel intelligent water drops optimization approach to single UCAV smooth trajectory planning

Trajectory planning of unmanned combat aerial vehicle (UCAV) is a rather complicated global optimum problem in UCAV mission planning. Intelligent water drops (IWD) algorithm is newly presented under the inspiration of the dynamic of river systems and the actions that water drops do in the rivers, and it is easy to combine with other methods in optimization. In this paper, we propose an improved IWD optimization algorithm for solving the single UCAV smooth trajectory planning problems in various combating environments. The water drops can act as an agent in searching the optimal UCAV trajectory. The detailed realization procedure for this novel approach is also presented. In order to make the optimized UCAV trajectory more feasible for exact flying, an efficient path smoothing method called the κ-trajectory is adopted for smoothing the single UCAV trajectory. Series experimental comparison results show the proposed IWD optimization algorithm is more effective and feasible in the single UCAV smooth trajectory planning than the basic IWD model.     

一种基于组合导航系统的新融合滤波算法

本文设计了一种可用于地面用户的低成本组合导航系统，提出了基于该系统的新信息融合方法，即模糊卡尔曼滤波算法和地图匹配技术联合起来。仿真结果表明模糊卡尔曼滤波算法相当于一数据平滑处理窗口，具有比常规卡尔曼滤波算法更高的精度。     

双频双星座地基增强系统精度和完好性算法

针对单频单星座地基增强系统（GBAS）无法满足飞机III类精密进近与着陆导航性能需求的问题，提出了将北斗导航卫星系统（BDS）与全球定位系统（GPS）进行融合，构建一种新型的基于GPS/BDS的双频双星座GBAS。首先，分析了GBAS的工作原理，并对Hatch滤波器的误差进行了分析，给出了一种适用于双频GBAS的无码载偏离载波相位平滑伪距算法；然后，对机载完好性算法进行了研究，给出了H0和H1假设下的机载保护级计算方法；最后，进行了系统验证实验，实验结果表明，单星座GBAS不能满足飞机III类精密进近与着陆导航的性能需求，GPS和BDS融合后可见卫星个数得到提升，优化了卫星几何分布，进而使得系统的可用性由80.6081%提升到大于99.9999%。     

Variable structure multiple model fixed-interval smoothing

This paper focuses on fixed-interval smoothing for stochastic hybrid systems. When the truth-mode mismatch is encountered, existing smoothing methods based on fixed structure of model-set have significant performance degradation and are inapplicable. We develop a fixed-interval smoothing method based on forward- and backward-filtering in the Variable Structure Multiple Model (VSMM) framework in this paper. We propose to use the Simplified Equivalent model Interacting Multiple Model (SEIMM) in the forward and the backward filters to handle the difficulty of different mode-sets used in both filters, and design a re-filtering procedure in the model-switching stage to enhance the estimation performance. To improve the computational efficiency, we make the basic model-set adaptive by the Likely-Model Set (LMS) algorithm. It turns out that the smoothing performance is further improved by the LMS due to less competition among models. Simulation results are provided to demonstrate the better performance and the computational efficiency of our proposed smoothing algorithms.     

Modeling and Estimation for Tracking Maneuvering Targets

A new approach to the three-dimensional airborne maneuvering target tracking problem is presented. The method, which combines the correlated acceleration target model of Singer [3] with the adaptive semi-Markov maneuver model of Gholson and Moose [8], leads to a practical real-time tracking algorithm that can be easily implemented on a modern fire-control computer. Preliminary testing with actual radar measurements indicates both improved tracking accuracy and increased filter stability in response to rapid target accelerations in elevation, bearing, and range.     

一种基于盲同态解卷积的SAR成像自聚焦算法

针对大气扰动及飞行平台不稳引起机载合成孔径雷达(SAR)图像散焦的问题,提出了一种新的自聚焦算法来估算运动误差。该算法是基于运动误差函数及场景散射函数平滑特性的差异来进行估计的,利用复对数变换将图像的幅度与相位信息分离,进而分别对相位及幅度信息进行处理。运动误差通常为慢变函数,而场景的散射信息具有某种随机特性。因此,经过复对数变换后,运动误差及散射信息可以通过滤波器进行分离,将相位中的随机噪声去除,从而保留了慢变的运动误差函数。为了去除噪声信息,需要建立一个平滑滤波器,利用Daubechies小波的尺度函数构造Riesz基向量,从而建立了正交子空间,通过所建立信号子空间及噪声子空间组建平滑滤波器,最终可以获得准确的运动误差。在实验部分,分别利用仿真数据及实测数据对本文方法进行验证,最终结果分析表明该方法具有很高的估计精度及执行效率。     

基于阵列天线的一种GPS多径抑制方法

为了抑制多径效应对GPS的严重影响,在分析了多径效应对伪码跟踪环路影响的基础上,提出了一种基于空间平滑的改进稳健波束形成算法来抑制GPS多径效应。以直线阵为基础,采用前后向空间平滑算法对阵列接收信号进行预处理,针对各种失配,采用改进的稳健波束形成算法进行处理。该方法既可以有效地抑制GPS多径效应,又可以有效地改善伪码跟踪环路的跟踪精度。仿真实验结果验证了所提方法的正确性和有效性,具有较高的工程实践价值。     

平滑重构稀疏贝叶斯学习测向算法

针对二级嵌套阵列中的紧凑阵元结构易受互耦效应影响的问题,提出了两种不同的嵌套阵列结构改进方法：连续平移嵌套阵列和间隔平移嵌套阵列。通过对原有二级嵌套阵列阵元位置进行调整,形成了两种不同的平移嵌套阵列结构,这两种结构对应的差分共阵均"无孔",并且测向自由度和阵列稀疏度均大于原二级嵌套阵列。针对嵌套阵列的差分共阵测向模型为单测量矢量模型,稀疏贝叶斯学习测向算法复杂度高的问题,提出了平滑重构稀疏贝叶斯学习算法。该算法通过空间平滑重构将单测量矢量模型变为多测量矢量模型,降低了观测矩阵的维度,减小了计算复杂度。算法求解时,通过对变换后的观测矩阵进行奇异值分解,进一步降低了观测矩阵维度,利用稀疏贝叶斯学习算法估计辐射源角度。仿真表明,在信噪比和采样数相同的条件下,该算法收敛速度比单测量矢量稀疏贝叶斯学习（SMV-SBL）算法快,且测向精度高于SMV-SBL算法和空间平滑多重信号分类（MUSIC）算法;存在互耦影响时,两种平移嵌套阵列比原嵌套阵列受互耦影响小。     

多目标约束的精锻叶片几何重构优化算法

新型航空发动机已部分采用精密锻造工艺提高叶片的制造精度和效率。然而,精锻后的叶身型面与设计模型存在几何偏差,导致依据设计模型加工后的进/排气边与精锻的叶身型面不能很好地匹配。为了解决该问题,提出一种面向精锻叶片自适应加工的几何重构方法。首先,建立基于公差约束配准的目标函数,并采用粒子群优化（PSO）算法求解目标函数。其次,提出基于叶身变形趋势预测进/排气边轮廓的光顺重构算法。最后,通过精锻叶片自适应数控加工实验对提出的方案和算法进行验证。实验结果表明,该方案可以有效重构出合格的工艺模型,满足精锻叶片的精密数控加工要求。     

Improved multi-target tracking in clutter by PDA smoothing

An algorithm is presented for the recursive tracking of multiple targets in cluttered environment by making use of the joint probabilistic data association fixed-lag smoothing (JPDAS) techniques. It is shown that a significant improvement in the accuracy of track estimation of both nonmaneuvering and maneuvering targets may be achieved by introducing a time lag of one or two sampling periods between the instants of estimation and latest measurement. Results of simulation experiments for a radar tracking problem that demonstrate the effects of fixed-lag smoothing are also presented     

用于非线性跟踪问题的一种新的粒子滤波器

机动目标跟踪系统通常是非线性而且不完全观测的 ,所以问题的关键在于每一时刻的目标机动性都是高度不确定的。提出了一种新的平滑粒子滤波算法 ,该算法在粒子滤波器中加入了对系统模型的概率分布密度的平滑处理 ,从而很好的解决了目标的机动性估计问题。在仿真研究中 ,与辅助粒子滤波器的比较验证了本文算法处理非线性跟踪问题的优越性     

Suboptimal Algorithms for Nonlinear Smoothing

This paper presents second-order algorithms for fixed- point and fixed-lag smoothing problems for nonlinear systems. The algorithms are obtained on the basis of the state augmentation approach, used earlier by the authors, for dealing with linear smoothing problems. Both discrete-and continuous-time forms of solutions are presented and the results are illustrated by considering a radar tracking problem. Some of the advantages of the proposed smoothing solutions compared to existing ones are also discussed.     

Angle and polarization estimation in a coherent signal environment

It is shown how a uniform linear array of crossed dipoles may be used with the ESPRIT algorithm and spatial smoothing techniques to estimate the arrival directions and polarizations of incoming coherent plane waves. Some examples showing typical performance are presented. One method of smoothing can be used where it is necessary to estimate both the arrival angles and polarizations of signals. Two other methods can be used when only the arrival angles are of interest     

基于改进TLS-ESPRIT算法的相干定位信号波达方向空间平滑

针对室内角度定位,在定位信号相干条件下无法实现准确估计波达方向(DOA)的问题,提出了一种基于TLS-ESPRIT算法的双向空间平滑方法。该算法先对定位相干信号进行双向空间平滑处理,然后用TLS-ESPRIT算法进行DOA的精确估计,使其最大限度地利用信号子空间的信息,从而解决了算法计算过程中输出信号的协方差矩阵秩亏损情况。该算法能够准确实现定位信号相干条件下DOA估计,与传统的空间平滑结合其他改进ESPRIT算法相比,具有更好的平稳性及抗干扰能力。最后通过仿真实验验证了算法的有效性和稳定性。