连续最小阶奇异滤波器

 本文讨论了当动态噪声统计特性未知时,奇异线性定常连续随机系统最小阶滤波器的设计问题。在系统部分观测量能精确测量的情况下,利用广义逆阵方法选择L矩阵,以消除动态噪声对降阶系统的影响,从而推导出缺动态噪声统计特性时的降阶奇异最优滤波器,其阶数为n-m+r。当量测方程奇异假设条件成立及引理有解时,本文证明了最小阶奇异滤波器必定存在。文中举例说明了这一降阶滤波方法的可行性。     

一种新型模糊自适应Kalman滤波器在组合导航中的应用

针对光纤陀螺捷联惯导(FOG SINS)/GPS组合导航系统实际工作环境中,由于系统噪声与量测噪声模型发生变化而带来的滤波器发散的问题,提出一种新型模糊自适应Kalman滤波器(FSHAKF).通过引入IMU精度因子与GPS水平精度因子,构造模糊推理系统(FIS),实时更新自适应参数,有效地解决了传统Sage-Husa自适应滤波器(SHAKF)估计模型不准确、系统噪声与量测噪声无法同时估计以及滤波器长时间易发散的问题.仿真实验表明,本文提出的FSHAKF算法相较于SHAKF算法,估计精度得到明显提高,且避免了滤波器的发散.     

色噪声情况下微分平滑求速的最优滤波问题

本文针对较接近真实的测量误差模型,讨论了最优微分平滑滤波器与白噪声滤波器这两种滤波器误差传播权系数的差别。结果表明,用对白噪声最优的滤波器微分平滑色噪声数据,可能带来很大的精度损失;从实测数据验算结果来看,精度损失一倍以上。表明了从软件方面提高外测精度的潜力。     

考虑有色噪声影响的脉冲星导航2级强跟踪差分滤波器

为提高X射线脉冲星导航对有色噪声及太阳系内星历误差的鲁棒性，设计了2级强跟踪差分滤波器（TSTDKF）。首先在分析导航原理基础上，导出了中心天体星历误差对导航结果的误差传递关系，并利用扩展卡尔曼滤波器（EKF）进行了仿真验证；在同一运行轨道上，又结合引力摄动模型对第三天体引力摄动数据进行了分析，证明该部分噪声为有色噪声。根据以上结论，将普通2级卡尔曼滤波器（TKF）的无偏滤波器设计为一种改进的差分卡尔曼滤波器以降低有色噪声对导航系统的影响，同时又在其独立偏差滤波器中根据观测残差构建了多重自适应调节因子以增强其跟踪性能，两者共同构成TSTDKF的2个并行滤波器。通过仿真实验证明，TSTDKF的位置误差性能最大可比EKF和TKF改进56.49%和35.18%，速度误差性能改进27.66%和17.07%；对星历误差的跟踪效果也整体好于TKF。     

一种基于前馈网络的组合自适应滤波器及其应用

本文设计了一种以单层或多层前馈神经网络为核心的组合自适应滤波器。该滤波器能够通过离线及在线学习了解输入信号与噪声的某些特性，从而把两者区别开来，从中检出信号。仿真研究表明，本文提出的方法直接用于雷达导引头角跟踪系统的滤波比通常的方法具有更好的性能和效果。     

SINS／GPS／CNS组合导航中的鲁棒滤波算法研究

组合导航系统难以精确建模、噪声统计特性难以获得等问题逐渐突出,严重影响了组合导航系统滤波器的稳定性。为提高SINS／GPS／CNS组合导航系统的鲁棒性,设计了基于H∞滤波理论的鲁棒滤波算法,提高了导航系统对系统模型参数变化的适应能力。通过仿真与线性卡尔曼滤波器进行对比,验证了在系统模型参数变化的情况下,H∞滤波器精度几乎不发生改变,有更好的鲁棒性;提高了组合导航系统在噪声统计特性和模型参数不易确定情况下的导航性能。     

紊流风场下的无人机飞行状态卡尔曼滤波估计

为了模拟紊流风场下的无人机飞行状态响应,减小紊流风场对无人机飞行速度的影响,使用数值法建立了紊流风场有色噪声模型,生成了符合大气紊流相关特性的紊流风场并对其进行数值仿真。在此基础上,通过状态扩增处理将系统噪声白化,设计出一种基于有色噪声的无人机飞行速度卡尔曼滤波器,该滤波器解决了基本卡尔曼滤波仅适用于白噪声的情况。仿真结果表明,使用此滤波器可有效减小紊流风场对无人机飞行速度的影响,进而满足飞行速度控制输入的精度要求。     

异常值和未知观测噪声鲁棒的非线性滤波器

针对含异常观测值的非线性系统滤波问题,以Huber损失函数替代推导滤波器最大后验准则中观测误差的l₂范数,构造出了一种新的优化准则函数,从而给出了一种对异常值鲁棒的非线性后验线性化滤波器。分析表明：由于Huber损失函数兼具l₁和l₂范数的性质,从而使得由这个新准则推导出的滤波器,不仅具有l₂范数的低误差拟合性,也具备l₁范数对异常值的鲁棒性。而当观测噪声的分布未知时,通过引入箱线图法检测异常值,并对噪声统计分布的参数进行估计,进一步提出了对异常值和未知观测噪声分布鲁棒的非线性后验线性化滤波器。仿真实验验证了分析结果的有效性,并表明本文算法的性能优于现有文献报道的非线性滤波算法。     

改进故障隔离的容错联邦滤波

为了解决重置模式下联邦滤波器中子系统故障对导航系统污染的问题,提出利用故障检测函数构建时变量测噪声的容错联邦滤波结构。通过将故障子滤波器等价为量测噪声趋于无穷大的正常系统,来取代传统的故障隔离方法;推导出了子滤波器对应的最优估计值,用以消除子滤波器估计次优性对故障检测的影响;采用动态信息分配系数,以减少故障信息对全局估计的影响。采用惯性/天文/景象/地形(INS/CNS/SMNS/TERCOM)的组合导航系统进行了仿真验证,结果表明该容错联邦滤波方法在子系统发生故障时的估计性能优于故障隔离方法。因此,所提方法具有提高故障子滤波器精度、保证无故障子滤波器鲁棒性以及全局估计精度的优势,具有较高的实用价值。     

风扇/压气机转子叶片定常力气动噪声数值研究

从考虑了固体边界影响和均匀介质影响后的广义 Lighthill方程出发 ,采用管道声学模型导出了风扇 /压气机转子叶片定常力气动噪声传播方程 ,并通过涡喷 11压气机单转子声学实验进行了数值模拟。结果表明 ,叶片定常力产生的气动噪声对风扇 /压气机总噪声级有重要作用。     

Third-Order Loop Filter Design for Acceleration-Rate Tracking

A perfect third-order loop filter design that can be implemented as a digital filter is obtained which minimizes the noiseless steady-state acceleration rate (jerk) error for a fixed loop noise bandwidth. Simulations were performed to obtain transient responses of the third-order loop plus a sample fourth-order loop under a jerk input. The results enable one to obtain a loop design that minimizes the loop noise bandwidth required for a given steady-state jerk error and thus obtain better noise jitter performance.     

Performance Bounds for Continuous-time Filters in the Presence of Modeling Errors

We are concerned with obtaining bounds on the performance of Kalman-type, linear, continuous-time filters susceptible to modeling errors. Limiting the discussion to stationary performance, we obtain bounds on the performance index, the mean square error of estimates for suboptimal and optimal (Kalman) filters. The bounds are expressed in terms of the model matrices and the range of errors of the matrices. The results are useful to a designer in comparing the performance of a suboptimal filter with that of the optimal filter when he has information on the range of modeling errors. The tightness of the bounds is shown by an application of the results in the estimation of the motion of an aircraft carrier at sea.     

Improved Range Resolution Filters for Rectangular-Pulse Radar Systems

The performance of several new clutter-reduction filters suitable for rectangular-pulse radar systems is investigated. The new filters consist of various approximations and modifications of two filters known to be optimal for certain criteria: the well-known Urkowitz filter which optiizes the clutter improvement ratio, and the newer sidelobe reduction filter which minimizes output noise power subject to peak sidelobe constaints. The new filters are compared usig five basic criteria: clutter improvement ratio, signal-to-noise ratio, sidelobe peak ratio, pulse compression ratio, and filter complexity. The results are summarized in tabular and graphical form.     

小波分析在外测数据滤波中的应用研究

为提高弹道估计精度,提出了一种基于小波分析的滤波方法,滤除外测数据中AR（自回归）模型的随机误差。分析讨论了滤波过程中的几个关键问题,提出了利用基于偏自相关系数截尾检验的方法来确定分解层数,然后采用GCV（广义交叉确认）准则来确定均方差意义下最优阈值的方法。本文提出的外测数据滤波方法计算简单,不需要估计噪声的方差。仿真结果表明,该方法能有效滤除外测数据中的AR噪声。     

Optimal FIR filters for time-varying state-space models

An optimal FIR (finite impulse response) filter and smoother is introduced for the time-varying state-space model. The suggested filter has an FIR structure and utilizes finite observation. It is shown that the impulse response of the optimal FIR filter can be obtained by a simple Riccati-type matrix differential equation. Especially for time-invariant systems, this FIR filter reduces to previously known simple forms. For implementation, a recursive form of the optimal FIR filter and smoother is derived by using adjoint variables, and computational algorithms are suggested. It is also shown by sensitivity analysis that the proposed optimal FIR filter alleviates potential divergence characteristics of the standard Kalman filter     

改进的Sage-Husa自适应滤波算法在MEMS航姿参考系统中的应用

针对经典Kalman滤波和扩展Kalman滤波融合算法存在的计算量大、精度低、实时性差的缺点，引入了改进的Sage-Husa自适应扩展Kalman滤波算法。该算法对经典扩展Kalman滤波算法进行了自适应改进，并在此基础上利用加权渐消记忆法获取了遗忘因子，并通过预测残差得出了最优解。同时，用调整有偏增益估计的措施来保证系统噪声预测方差矩阵与噪声预测方差矩阵的对称性和正定性，对滤波器发散进行了有效的抑制，减少了算法的计算量。实验结果表明，该算法有效改善了可靠性、精确性及自适应能力。     

The effect of the clutter-to-noise ratio on Doppler filterperformance

The effect of the clutter-to-noise ratio on the performance of a Doppler filter is considered. Clutter is assumed to have a power level which is unknown and varies in range. The assessment of the performance of a Doppler filter is based on the gain of the filter, which is the normalized output signal-to-interference ratio improvement at a given Doppler. The gain is generally a complex function of the statistics of the clutter. New upper and lower bounds on the gain differential between the expected design point clutter-to-noise ratio and the actual clutter-to-noise ratio are found. These bounds are independent of the clutter covariance matrix and are only a function of the unknown clutter-to-noise ratio. The bounds are valid for both Gaussian and non-Gaussian noise and for arbitrary linear filters. The upper and lower bounds differ by the theoretical coherent integration gain, 10 logN dB, where N is the number of pulses. A tighter lower bound is found for the case when the filters are matched filters. A simple exact expression is found for matched filters assuming a Gaussian Markov clutter model as the clutter spectral width approaches zero. An easily implementable adaptive procedure is given which improves performance due to the unknown clutter-to-noise ratio. This work extends a previous result, valid for the Emerson filter, that shows the effect of clutter-to-noise ratio on performance in terms of an average quantity, the improvement factor     

Hierarchical adaptive Kalman filtering for interplanetary orbitdetermination

A modular and flexible approach to adaptive Kalman filtering has recently been introduced using the framework of a mixture-of-experts regulated by a gating network. Each expert is a Kalman filter modeled with a different realization of the unknown system parameters. The unknown or uncertain parameters can include elements of the state transition matrix, observation mapping matrix, process noise covariance matrix, and measurement noise covariance matrix. The gating network performs on-line adaptation of the weights given to individual filters based on performance. The mixture-of-experts approach is extended here to a hierarchical architecture which involves multiple levels of gating. The proposed architecture provides a multilevel hypothesis testing capability. The utility of the hierarchical architecture is illustrated via the problem of interplanetary navigation (Mars Pathfinder) using simulated radiometric data. It serves as a useful tool for assisting navigation teams in the process of selecting the parameters of the navigational filter over various operating regimes. It is shown that the scheme has the capability of detecting changes in the system parameters and switching filters appropriately for optimal performance. Furthermore, the expectation-maximization (EM) algorithm is shown to be applicable in the proposed framework     

An Optimal Control Approach to Designing Constant Gain Filters

This paper applies optimal control theory to designing constant gain filters which minimize a weighted average of the filtered variances. Uniaxial second-order motion is studied in detail, and an example is given which indicates that a constant gain filter may be designed with performance comparable to a Kalman filter. An appendix is included which shows how the approach may be extended to higher order systems.     

On a decoupled multitarget tracking algorithm

An analysis is conducted of the optimality of a decoupled tracking filtering algorithm for addressing the problem of tracking multiple targets with correlated measurements and maneuvers. It is proved that the decoupled filters are, in general, suboptimal and are not in fact Kalman filters. However, it is shown also that if the standard Kalman filter is asymptotically stable, the decoupled filters will converge asymptotically to the stable version of the standard Kalman filter. For the case of time-invariant measurement and process noise covariance, a simple sufficient condition guaranteeing the asymptotical stability of the decoupled filters are given