State estimation using an approximate reduced statistics algorithm

The problem of state estimation using nonlinear additive Gaussian noise measurements is addressed. A geometric model for the posterior state density is assumed based on a multidimensional Haar basis representation. An approximate reduced statistics (ARS) algorithm, suggested by the parameter estimator of Kulhavy is then developed, using successive minimization of relative entropy between model densities and an approximate posterior density. The state estimator thus derived is applied to a bearings-only target tracking problem in a multiple sensor scenario     

On state estimation for an orbiting single tether system

The effects of instrumentation accuracy and configuration on estimation error are studied for the small expandable-tether deployment system (SEDS) using a continuous-discrete extended Kalman filter (CDEKF) state estimator. A twelfth order model that incorporates the rigid body modes of the tether as well as the satellite attitude dynamics is developed. Simulation results using the model and the estimator indicate that the originally planned instrumentation package could not estimate the state vector adequately. Recommendations are made and results presented that reduce the estimation error by adding instruments and increasing selected measurement accuracies     

Design of multi-sensor attitude determination systems

The design of inexpensive multi-sensor attitude determination systems is discussed. The systems discussed fuse information from a triad of solid state rate gyros with an aiding system mechanized using GPS or magnetometers and accelerometers. Euler angle and quaternion-based sensor fusion algorithms are developed. Methods for gain scheduling and estimator pole placement are presented. Using simulation and flight test results, it is shown that quaternion-based algorithms simplify gain scheduling and improve transient response.     

Application of Road Information in Ground Moving Target Tracking

A new algorithm is developed to achieve accurate state estimation in ground moving target tracking by means of using road information. It is an adaptive variable structure interacting multiple model estimator with dynamic models modification （DMM VS-IMM for short）. Firstly, road information is employed to modify the target dynamic models used by filter, including modification of state transition matrix and process noise. Secondly, road information is applied to update the model set of a VS-IMM estimator. Predicted state estimation and road information are used to locate the target in the road network on which the model set is updated and finally IMM filtering is implemented. As compared with traditional methods, the accuracy of state estimation is improved for target moving not only on a single road, but also through an intersection. Monte Carlo simulation demonstrates the efficiency and robustness of the proposed algorithm with moderate computational loads.     

MMAE-Based Control with Space-Time Point Process Observations

A multiple model adaptive estimator (MMAE) has been formulated to estimate the state of a dynamic system modeled by a linear stochastic differential equation, from which measurements, described as a noise-corrupted space-time point process functionally related to that state, are extracted. Assumed certainty equivalence is used to combine such an estimator with the LQ full-state feedback controller to synthesize a practical, implementable controller. Performance of the estimator and resultant controller characteristics are investigated via simulation as a function of approximation method used to limit the full-scale estimator to finite dimensionality and also as a function of important parameters defining the dynamics and observation processes.     

A necessary condition for effective performance of the multiplemodel adaptive estimator

Effective adaptive estimation for a general linear system driven by an input modeled by a randomly switching Gaussian process is considered. The performance of the multiple model adaptive estimator (MMAE) is, in some cases, unexpectedly hampered by a necessary condition not satisfied by the linear system. This key dependency for effective MMAE performance is based on a particular property of the DC gain of the linear system     

Data fusion of decentralized local tracker outputs

An approach for fusing offboard track-level data at a central fusion node is presented. The case where the offboard tracker continues to update its local track estimate with measurement and system dynamics models that are not necessarily linear is considered. An algorithm is developed to perform this fusion at a central node without having access to the offboard measurements, their noise statistics, or the location of the local estimator. The algorithm is based on an extension of results that were originally established for linear offboard trackers. A second goal of this work is to develop an inequality constraint for selecting the proper sampling interval for the incoming state estimates to the fusion node. This interval is selected to allow use of conventional Kalman filter algorithms at the fusion node without suffering error performance degradation due to processing a correlated sequence of track state estimates     

先进武装直升机火控系统研究与仿真

提出了~套适合于武装直升机火控系统的设计方案,特点是采用两自度可动炮,大大地增大了火控系统攻击范围;采用飞行器状态估值器、目标状态估值器,提高了直升机的测量精度。采用闭环火控算法,大大地提高了射击精度。最后,利用蒙特卡洛法对整个火控系统进行了大量仿真,结果表明该火控系统与现有的武装直升机火控系统相比攻击范围大,射击精度高、反应快且具有较强的抗干扰能力,并能在机载计算机上实时工作。     

IMM estimator with out-of-sequence measurements

In multisensor tracking systems that operate in a centralized information processing architecture, measurements from the same target obtained by different sensors can arrive at the processing center out of sequence. In order to avoid either a delay in the output or the need for reordering and reprocessing an entire sequence of measurements, such measurements have to be processed as out-of-sequence measurements (OOSMs). Recent work developed procedures for incorporating OOSMs into a Kalman filter (KF). Since the state of the art tracker for real (maneuvering) targets is the interacting multiple model (IMM) estimator, the algorithm for incorporating OOSMs into an IMM estimator is presented here. Both data association and estimation are considered. Simulation results are presented for two realistic problems using measurements from two airborne GMTI sensors. It is shown that the proposed algorithm for incorporating OOSMs into an IMM estimator yields practically the same performance as the reordering and in-sequence reprocessing of the measurements. Also, it is shown how the range rate from a GMTI sensor can be used as a linear velocity measurement in the tracking filter.     

On-line Vehicle Motion Estimation from Visual Terrain Information Part II: Ground Velocity and Position Estimation

An algorithm, combining velocity/height estimates, obtained from an airborne body fixed image shift estimator with auxiliary on-board measurements and sparsely stored terrain profile information constitutes an entirely passive autonomous navigation system suitable for moderate-g flight missions. Two versions are addressed. The "naive estimator," in which altitude estimates are multiplied by velocity/height estimaters, yields ground velocity. Position, obtained by integration, diverges with time. The "extended Kalman filter" (EKF) version, in which velocity and position are defined as state space components, locks on the stored terrain profile and does not diverge with time. It degenerates into the "naive estimator" if the terrain is completely flat. Numerical examples indicate excellent performance potential of the EKF estimator.     

Coordinated target localization base on pseudo measurement for clustered space robot

This paper presents a coordinated target localization method for clustered space robot.According to the different measuring capabilities of cluster members,the master-slave coordinated relative navigation strategy for target localization with respect to slavery space robots is proposed;then the basic mathematical models,including coordinated relative measurement model and cluster centralized dynamics,are established respectively.By employing the linear Kalman flter theorem,the centralized estimator based on truth measurements is developed and analyzed frstly,and with an intention to inhabit the initial uncertainties related to target localization,the globally stabilized estimator is designed through introduction of pseudo measurements.Furthermore,the observability and controllability of stochastic system are also analyzed to qualitatively evaluate the convergence performance of pseudo measurement estimator.Finally,on-orbit target approaching scenario is simulated by using semi-physical simulation system,which is used to verify the convergence performance of proposed estimator.During the simulation,both the known and unknown maneuvering acceleration cases are considered to demonstrate the robustness of coordinated localization strategy.     

Reduced state estimator for systems with parametric inputs

A reduced state estimator is derived for systems with bounded parameters as inputs. Optimal filter gains are derived for minimizing the total covariance of the estimation error due to measurement noise and parameter uncertainty. It is shown that these filter gains for a two-state system with a Gaussian parameter satisfy the Kalata relation in steady state. Equations are also derived for optimally filtering measurements in arbitrary time order. This reduced state estimator offers novelties over a traditional Kalman filter in its application to the class of problems considered. The total error covariance, which is minimized, makes no use of plant noise. Furthermore, the filter is easier to optimize in high dimensional and multiple sensor applications as well as in processing out-of-sequence measurements.     

基于神经网络的自适应状态观测器

利用ＢＰ神经网络动态系统对一类非线性时变系统的状态进行了估计。利用神经网络的“学习－遗忘”特性,提出了非线性时变系统的自适应状态观测器,对其结构及特性进行了讨论。仿真结果表明这种自适应状态观测器能跟踪系统参数及状态的变化。     

Efficient recursive state estimator for dynamic systems withoutknowledge of noise covariances

An efficient recursive state estimator for dynamic systems without knowledge of noise covariances is suggested. The basic idea for this estimator is to incorporate the dynamic matrix and the forgetting factor into the least squares (LS) method to remedy the lack of knowledge of noises. We call it the extended forgetting factor recursive least squares (EFRLS) estimator. This estimator is shown to have similar asymptotic properties to a completely specified Kalman filter state estimator. More importantly, the performance of EFRLS greatly exceeds that of existing filtering techniques when the noise variance is misspecified. In addition, EFRLS also performs well when there is cross-correlation between the process and measurement noise streams or temporal dependencies within those streams. Some discussions and a number of simulations are made to provide practical guidance on the choice of an optimal forgetting factor and evaluate the performance of the EFRLS algorithms, which strongly dominates that of the standard forgetting factor recursive least squares (FRLS) and some misspecified Kalman filtering     

Interacting multiple model methods in target tracking: a survey

The Interacting Multiple Model (IMM) estimator is a suboptimal hybrid filter that has been shown to be one of the most cost-effective hybrid state estimation schemes. The main feature of this algorithm is its ability to estimate the state of a dynamic system with several behavior modes which can “switch” from one to another. In particular, the IMM estimator can be a self-adjusting variable-bandwidth filter, which makes it natural for tracking maneuvering targets. The importance of this approach is that it is the best compromise available currently-between complexity and performance: its computational requirements are nearly linear in the size of the problem (number of models) while its performance is almost the same as that of an algorithm with quadratic complexity. The objective of this work is to survey and put in perspective the existing IMM methods for target tracking problems. Special attention is given to the assumptions underlying each algorithm and its applicability to various situations     

Fusion of distributed extended forgetting factor RLS state estimators

For single-target multisensor systems, two fusion methods are presented for distributed recursive state estimation of dynamic systems without knowledge of noise covariances. The estimator at every local sensor embeds the dynamics and the forgetting factor into the recursive least squares (RLS) method to remedy the lack of knowledge of noise statistics, developed before as the extended forgetting factor recursive least squares (EFRLS) estimator. It is proved that the two fusion methods are equivalent to the centralized EFRLS that uses all measurements from local sensors directly and their good performance is shown by simulation examples.     

Multistage integration model for human egomotion perception

Human computational vision models that attempt to account for the dynamic perception of egomotion and relative depth typically assume a common three-stage process: first, compute the optical flow field based on the dynamically changing image; second, estimate the egomotion states based on the flow; and third, estimate the relative depth/shape based on the egomotion states and possibly on a model of the viewed surface. We propose a model more in line with recent work in human vision, employing multistage integration. Here the dynamic image is first processed to generate spatial and temporal image gradients that drive a mutually interconnected state estimator and depth/shape estimator. The state estimator uses the image gradient information in combination with a depth/shape estimate of the viewed surface and an assumed model of the viewer's dynamics to generate current state estimates; in tandem, the depth/shape estimator uses the image gradient information in combination with the viewer's state estimate and assumed shape model to generate current depth/shape estimates. In this paper, we describe the model and compare model predictions with empirical data.     

A new approach to robust fault detection and identification

A methodology for instrument fault detection and identification (FDI) in linear dynamical systems subject to plant parameter variations or uncertainties is presented. At the heart of this approach is a robust estimator for which the necessary and sufficient conditions to its existence are outlined. The robust estimator can simultaneously estimate the unmeasurable state variables of the system for the purpose of control and provide necessary information for FDI purposes. A novel feature of this approach is that it can actually identify the shape and magnitude of the failures. The scheme allows for fast and accurate FDI, and can account for structural uncertainties and variations in the parameters of the dynamical model of the system. The overall fault tolerant control system strategy proposed is verified through simulation studies performed on the control of a vertical takeoff and landing (VTOL) aircraft in the vertical plane     

A Correlation Polarimeter for Noise-Like Signals

Optimum estimation (tracking) of the polarization plane of a linearly polarized electromagnetic wave is determined when the signal is a narrow-band Gaussian random process with a polarization plane angle which is also a Gaussian random process. This model is Compared to previous work and is applicable to space communication. The estimator performs a correlation operation similar to an amplitude -comparison monopulse angle tracker, giving the name correlation polarimeter. Under large signal-to-noise ratio (SNR), the estimator is causal. Performance of the causal correlation polarimeter is evaluated for arbitrary SNR. Optimum precorrelation filtering is determined. With low SNR, the performance of this system is far better than that of previously developed systems. Practical implementation is discussed. A scheme is given to reduce the effect of linearly polarized noise.