Particle flters for probability hypothesis density flter with the presence of unknown measurement noise covariance

In Bayesian multi-target fltering,knowledge of measurement noise variance is very important.Signifcant mismatches in noise parameters will result in biased estimates.In this paper,a new particle flter for a probability hypothesis density（PHD）flter handling unknown measurement noise variances is proposed.The approach is based on marginalizing the unknown parameters out of the posterior distribution by using variational Bayesian（VB）methods.Moreover,the sequential Monte Carlo method is used to approximate the posterior intensity considering non-linear and non-Gaussian conditions.Unlike other particle flters for this challenging class of PHD flters,the proposed method can adaptively learn the unknown and time-varying noise variances while fltering.Simulation results show that the proposed method improves estimation accuracy in terms of both the number of targets and their states.     

具有未知输入干扰的观测器设计

提出一种全阶比例积分观测器。该观测器包含有估计误差的比例和积分回路,可同时得到状态和未知输入估计。分析了比例积分观测器的结构和估计性能,给出并证明了其存在条件,由于存在条件弱于未知输入观测器,该比例积分观测器可以更广泛地应用于基于观测器的技术方案。通过一个飞行器线性化模型,进行了比例积分观测器设计。仿真结果表明,比例积分观测器的估计性能优于常规 Luenberger观测器     

具有Lipschitz非线性系统的航天器故障检测

对存在未知干扰的Lipschitz非线性系统,探讨了基于滑模观测器的执行器故障检测的方法。通过等价状态变换,可以得到2个子系统,其中一个子系统与执行器故障和未知干扰耦合,而另一个子系统则只与执行器故障耦合。在此基础上,提出了一种免受未知干扰影响,可对执行器故障敏感的滑模观测器设计方法。这种滑模观测器可以作为故障检测工具,而将其输出估计误差作为残差发生器,用于执行器故障检测。相比滑模观测器的故障重构方法,该方法大大放宽了对数学假设条件的要求。最后,对一个大攻角状态下的航天器的简化模型进行了数字仿真,仿真结果表明了所提方法的有效性。     

Probability hypothesis density filter with adaptive parameter estimation for tracking multiple maneuvering targets

The probability hypothesis density (PHD) filter has been recognized as a promising tech-nique for tracking an unknown number of targets. The performance of the PHD filter, however, is sensitive to the available knowledge on model parameters such as the measurement noise variance and those associated with the changes in the maneuvering target trajectories. If these parameters are unknown in advance, the tracking performance may degrade greatly. To address this aspect, this paper proposes to incorporate the adaptive parameter estimation (APE) method in the PHD filter so that the model parameters, which may be static and/or time-varying, can be estimated jointly with target states. The resulting APE-PHD algorithm is implemented using the particle filter (PF), which leads to the PF-APE-PHD filter. Simulations show that the newly proposed algorithm can correctly identify the unknown measurement noise variances, and it is capable of tracking mul-tiple maneuvering targets with abrupt changing parameters in a more robust manner, compared to the multi-model approaches.     

Tracking targets using adaptive Kalman filtering

A simple algorithm for estimating the unknown process noise variance of an otherwise known linear plant, using a Kalman filter is suggested. The process noise variance estimator is essentially dead beat, using the difference between the expected prediction error variance, computed in the Kalman filter, and the measured prediction error variance. The estimate is used to adapt the Kalman filter. The use of the adaptive filter is demonstrated in a simulated example in which a wildly maneuvering target is tracked     

Variational Bayesian labeled multi-Bernoulli filter with unknown sensor noise statistics

It is difficult to build accurate model for measurement noise covariance in complex back-grounds. For the scenarios of unknown sensor noise variances, an adaptive multi-target tracking algorithm based on labeled random finite set and variational Bayesian (VB) approximation is pro-posed. The variational approximation technique is introduced to the labeled multi-Bernoulli (LMB) filter to jointly estimate the states of targets and sensor noise variances. Simulation results show that the proposed method can give unbiased estimation of cardinality and has better performance than the VB probability hypothesis density (VB-PHD) filter and the VB cardinality balanced multi-target multi-Bernoulli (VB-CBMeMBer) filter in harsh situations. The simulations also confirm the robustness of the proposed method against the time-varying noise variances. The computational complexity of proposed method is higher than the VB-PHD and VB-CBMeMBer in extreme cases, while the mean execution times of the three methods are close when targets are well separated.     

基于LMI和离散模型的航空发动机压气机传感器鲁棒故障诊断

针对航空发动机压气机健康监测提出了一种基于线性矩阵不等式(LMI)和H_∞优化理论的航空发动机压气机传感器鲁棒故障诊断的方法.在航空发动机具有模型不确定性和外界噪声的情况下,应用基于神经网络的线性拟合方法实现航空发动机压气机离散模型的建立;并通过LMI和H_∞优化问题的求解得到未知输入观测器的设计参数,实现具有强鲁棒性的传感器故障诊断.该方法比以前研究中未知输入观测器故障诊断方法的优点在于能够同时处理模型不确定性和外界噪声.应用ALSTOM公司提供的燃气涡轮压气机模型进行了仿真验证,在压气机具有白噪声模型误差和正弦外界干扰的情况下,实现对小于测量范围2%的传感器故障的检测和诊断.      

Optimal and self-tuning information fusion Kalman multi-step predictor

Based on the optimal fusion algorithm weighted by matrices in the linear minimum variance (LMV) sense, a distributed optimal information fusion for the steady-state Kalman multi-step predictor is given for discrete linear stochastic control systems with multiple sensors and correlated noises, where the same sample period is assumed. When the noise statistics information is unknown, the distributed information fusion estimators for the noise statistics parameters are presented based on the correlation functions and the weighting average approach. Further, a self-tuning information fusion multi-step predictor is obtained. It has a two-stage fusion structure. The first-stage fusion is to obtain the fused noise statistics information. The second-stage fusion is to obtain the fused multi-step predictor. A simulation example shows the effectiveness.     

Identification Method for RLG Random Errors Based on Allan Variance and Equivalent Theorem

An identification method using Allan variance and equivalent theorem is proposed to identify non-stationary sensor errors mixed out of different simple noises. This method firstly derives the discrete Allan variances of all component noises inherent in noise sources in terms of their different equations; then the variances are used to estimate the parameters of all component noise models; finally, the original errors are represented by the sum of the non-stationary component noise model and the equivalent model mixed out of the stationary and critically stationary component noises. Results of two examples for identification confirm the superiority of this approach regardless of the errors being stationary or not. The comparison of results of real ring laser gyro (RLG) errors processed by various methods shows that the proposed approach is more suited to depict the original noises than common ones.     

远场假设对喷流噪声预测中格林函数求解的影响

目前基于雷诺平均Navier-Stokes（RANS）的喷流噪声预测方法在格林函数求解时, 为简化求解过程,通常对喷流流动做平行流假设, 对观测点做远场假设。随着格林函数求解方法发展,近年来的研究表明平行流假设对下游观测点格林函数的计算会引起较大偏差,而目前远场假设对格林函数求解的影响仍不清楚。为研究远场假设对喷流格林函数求解的影响,以二维喷流为例, 采用计算气动声学方法（CAA）分别数值求解了观测点远场假设条件与实际条件下90°~150°方向喷流内伴随格林函数,进而分析远场假设对格林函数求解的影响。研究结果表明,对于不同方向的观测点,由观测远场假设导致的伴随格林函数求解偏差不尽相同,且对于越靠近喷流中心线方向的观测点,远场假设导致的偏差越大,其中150°方向观测点,采用远场假设后,格林函数计算结果最大偏差达到-15 dB以上。因此,对于靠近喷流中心线方向的噪声观测点而言,为避免预测偏差,应采用实际观测条件求解喷流格林函数。     

Self-Tuning Multisensor Weighted Measurement Fusion Kalman Filter

For the multisensor systems with unknown noise variances, based on the solution of the matrix equations for the correlation function, the on-line estimators of the noise variance matrices are obtained, whose consistency is proved using the ergodicity of sampled correlation function. Further, two self-tuning weighted measurement fusion Kalman filters are presented for the multisensor systems with identical and different measurement matrices, respectively. Based on the stability of the dynamic error system, a new convergence analysis tool is presented for a self-tuning fuser, which is called the dynamic error system analysis (DESA) method. A new concept of convergence in a realization is presented, which is weaker than the convergence with probability one. It is rigorously proved that the proposed self-tuning Kalman fusers converge to the steady-state optimal Kalman fusers in a realization or with probability one, so that they have asymptotic global optimality. A simulation example for a target tracking system with 3 sensors shows their effectiveness.     

一种基于冗余测量的自适应卡尔曼滤波算法

针对目前自适应滤波算法的不足,在测量系统量测噪声方差未知的情况下,设计了一种基于冗余测量的自适应卡尔曼滤波(RMAKF)算法。通过对系统冗余测量值的一阶、二阶差分序列进行有效的统计分析,可以准确估计系统量测噪声统计特性,进而在滤波过程中自适应调节噪声方差阵R,提高滤波精度。以全球定位系统/惯性导航系统(GPS/INS)松组合导航系统为对象进行了仿真实验,结果表明该算法在测量系统噪声特性未知或发生改变时,可对其进行准确估计,在采用低精度惯性器件情况下,滤波结果较其他主要自适应卡尔曼滤波算法有较明显的改进。     

基于UIO的航空发动机执行机构故障诊断

为了提高航空发动机诊断过程中对噪声干扰和模型参数变化的鲁棒性,应用UIO(Unknown Input Ob-server)理论估计发动机动态系统的工作状态,通过干扰正交投影弱化外界干扰对状态估计的影响,处理了航空发动机执行机构的故障诊断问题。对航空发动机执行机构设计一组UIO观测器,其中每个UIO用于监测估计对应执行机构的故障偏移量,计算系统输出理论估计值与发动机实际测量信号间的残差数据,分析残差队列的幅值特性,实现航空发动机执行机构系统的故障检测和隔离。某型涡扇发动机上的实验结果表明,与Kalman滤波算法相比,UIO诊断方法更能鲁棒地检测和隔离出执行机构故障。     

An Adaptive Two-Dimensional Kalman Tracking Filter

A continuously adaptive two-dimensional Kalman tracking filter for a low data rate track-while-scan (TWS) operation is introduced which enhances the tracking of maneuvering targets. The track residuals in each coordinate, which are a measure of track quality, are sensed, normalized to unity variance, and then filtered in a single-pole filter. The magnitude Z of the output of this single-pole filter, when it exceeds a threshold Z1 is used to vary the maneuver noise spectral density q in the Kalman filter model in a continuous manner. This has the effect of increasing the tracking filter gains and containing the bias developed by the tracker due to the maneuvering target. The probability of maintaining track, with reasonably sized target gates, is thus increased, The operational characteristic of q versus Z assures that the tracker gains do not change unless there is high confidence that a maneuver is in progress.     

航天测量船外测数据的复杂误差特性

使用三次样条最小二乘拟合残差法研究了航天测量船外测数据误差的统计特性,分析了其误差的相关特性,建立了时序模型。研究结果表明,航天测量船外测数据误差具有强自相关性、非正态性、时变方差性和分段平稳性,其特性可以用高阶AR模型描述。     

Radar Measurement Noise Variance Estimation with Several Targets of Opportunity

A number of methods exist to track a target's uncertain motion through space using inherently inaccurate sensor measurements. A powerful method of adaptive estimation is the interacting multiple model (IMM) estimator. In order to carry out state estimation from the noisy measurements of a sensor, however, the filter should have knowledge of the statistical characteristics of the noise associated with that sensor. The statistical characteristics (accuracies) of real sensors, however, are not always available, in particular for legacy sensors. A method is presented of determining the measurement noise variances of a sensor, assumed to be constant, by using multiple IMM estimators while tracking targets whose motion is not known---targets of opportunity. Combining techniques outlined in [2] and [6], the likelihood functions are obtained for a number of IMM estimators, each with different assumptions on the measurement noise variances. Then a search is carried out over a varying grid of IMMs to bracket the variances of the sensor measurement noises. The end result consists of estimates of the measurement noise variances of the sensor in question.     

有色噪声作用下的机载INS/GPS组合导航方法

针对机载导航过程中有色噪声模型系数难以精确获取的问题,提出了一种基于滤波残差处理有色噪声的方法,并将其应用到INS/GPS组合导航系统中。首先分析了有色状态噪声和有色量测噪声对状态参数估值的影响,接着分别将状态残差和量测残差作为有色状态噪声和有色量测噪声的样本观测值,通过滤波所得的多个历元的残差序列获取拟合模型参数,然后计算有色噪声的预报值并将其进行补偿,从而得到有色噪声修正后的组合导航模型。最后设计了转台试验验证提出的有色噪声作用下的INS/GPS组合导航方法,结果表明该方法能有效地减小有色噪声对组合系统的影响,且当GPS暂时失效时,能显著提高系统的导航精度。     

Switching control of morphing aircraft based on Q-learning

This paper investigates a switching control strategy for the altitude motion of a morphing aircraft with variable sweep wings based on Q-learning. The morphing process is regarded as a function of the system states and a related altitude motion model is established. Then, the designed controller is divided into the outer part and inner part, where the outer part is devised by a combination of the back-stepping method and command filter technique so that the ‘explosion of complexity’ problem is eliminated. Moreover, the integrator structure of the altitude motion model is exploited to simplify the back-stepping design, and disturbance observers inspired from the idea of extended state observer are devised to obtain estimations of the system disturbances. The control input switches from the outer part to the inner part when the altitude tracking error converges to a small value and linear approximation of the altitude motion model is applied. The inner part is generated by the Q-learning algorithm which learns the optimal command in the presence of unknown system matrices and disturbances. It is proved rigorously that all signals of the closed-loop system stay bounded by the developed control method and controller switching occurs only once. Finally, comparative simulations are conducted to validate improved control performance of the proposed scheme.     

Temporally staggered sensors in multi-sensor target tracking systems

For a multi-sensor target tracking system, the effects of temporally staggered sensors on system performance are investigated and compared with those of synchronous sensors. To capture system performance over time, a new metric, the average estimation error variance (AEV), is proposed. For a system that has N sensors with equal measurement noise variance, numerical results show that the optimal staggering pattern is to use N uniformly staggered sensors. We have also shown analytically that the AEV of the system with N uniformly staggered sensors is always smaller than that of the system with N synchronous sensors. For sensors with different measurement noise variances, the optimal staggering pattern can be found numerically. Practical guidelines on selecting the optimal staggering pattern have been presented for different target tracking scenarios. Due to its simplicity, uniform staggering can be used as an alternative scheme with relatively small performance degradation.     

Limitations of radiometer performance in spherically invariantnoise

The radiometer is a common method for detection of unknown signals in noise. Most analyses of radiometer performance are based on assumptions of stationary Gaussian noise with known marginal statistics. In this note, we use a spherically invariant noise model to derive simple expressions for radiometer performance degradation in noise variance uncertainty. Numerical examples are provided to show that channel uncertainty imposes a substantial penalty in detection performance