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.     

Bearings-only tracking of maneuvering targets using abatch-recursive estimator

We present a new batch-recursive estimator for tracking maneuvering targets from bearings-only measurements in clutter (i.e., for low signal-to-noise ratio (SNR) targets), Standard recursive estimators like the extended Kalman Iter (EKF) suffer from poor convergence and erratic behavior due to the lack of initial target range information, On the other hand, batch estimators cannot handle target maneuvers. In order to rectify these shortcomings, we combine the batch maximum likelihood-probabilistic data association (ML-PDA) estimator with the recursive interacting multiple model (IMM) estimator with probabilistic data association (PDA) to result in better track initialization as well as track maintenance results in the presence of clutter. It is also demonstrated how the batch-recursive estimator can be used for adaptive decisions for ownship maneuvers based on the target state estimation to enhance the target observability. The tracking algorithm is shown to be effective for targets with 8 dB SNR     

Enhancements to a multiple model adaptive estimator/image-tracker

Three major enhancements to a previously devised multiple model adaptive estimator (MMAE) for target image tracking are developed and analyzed. These are: allowing some of the elemental filters to have rectangular fields of view and to be tuned for target dynamics that are harsher in one direction than others; considering both Gauss-Markov acceleration models and constant turn-rate models for target dynamics; and devising an initial target acquisition algorithm to remove important biases in the estimated target template to be used in a correlator within the tracker. Particularly good adaptation responsiveness is demonstrated in the multiple model algorithm's ability to handle harsh maneuver onset, yielding performance essentially equivalent to that of the best artificially informed tracking algorithm     

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.     

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     

自适应遗传神经网络算法在推力估计器设计中的应用

为了在全包线内能够准确方便估计出航空发动机推力,提出了一种自适应遗传神经网络算法:将遗传算法和神经网络技术相结合充分发挥遗传算法和神经网络各自的全局收敛性和局部搜索快速性的优点,其中通过自适应概率遗传操作及局部寻优算子直接优化出神经网络拓扑结构及权值(包括阈值),克服了神经网络隐层节点需凭经验尝试的缺点和神经网络对初始权值(包括阈值)敏感的缺点,再应用神经网络对上述优化的权值(包括阈值)进行"精调",最后设计出全包线推力估计器.经验证,此推力估计器具有较高估计精度和良好泛化能力.      

Performance enhancement of a multiple model adaptive estimator

We describe performance improvement techniques for a multiple model adaptive estimator (MMAE) used to detect and identify control surface and sensor failures on an unmanned flight vehicle. Initially failure identification was accomplished within 4 s of onset, but by removing the “β dominance” effects, bounding the hypothesis conditional probabilities, retuning the Kalman filters, increasing the penalty for measurement residuals, decreasing the probability smoothing, and increasing residual propagation, the identification time was reduced to 2 s     

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     

Multiple-model estimation with variable structure. V. Likely-modelset algorithm

For pt. IV see ibid., vol. 35, no. 1, p. 242-254 (1999). A multiple-model (MM) estimator with a variable structure, called likely-model set (LMS) algorithm, is presented, which is generally applicable to most hybrid estimation problems and is easily implementable. It uses a set of models that are not unlikely to match the system mode in effect at any given time. Different versions of the algorithm are discussed. The model set is made adaptive in the simplest version by deleting all unlikely models and activating all models to which a principal model may jump so as to anticipate the possible system mode transitions. The generality, simplicity, and ease in the design and implementation of the LMS estimator are illustrated via an example of tracking a maneuvering target and an example of fault detection and identification. Comparison of its cost-effectiveness with other fixed- and variable-structure MM estimators is given     

Tracking of a Ballistic Missile with A-Priori Information

The paper addresses the problem of estimating the launch and impact points of a ballistic target from radar measurements. The problem has been faced under different hypotheses on the available prior knowledge. The proposed approach combines a nonlinear batch estimator with a recursive MM (multiple model) particle filter in order to attain the estimation goal. Extensive simulations assess the achievable estimation performance.     

Ground target tracking with variable structure IMM estimator

In this paper we present the design of a Variable Structure Interacting Multiple Model (VS-IMM) estimator for tracking groups of ground targets on constrained paths using Moving Target Indicator (MTI) reports obtained from an airborne sensor. The targets are moving along a highway, with varying obscuration due to changing terrain conditions. In addition, the roads can branch, merge or cross-the scenario represents target convoys along a realistic road network with junctions, changing terrains, etc. Some of the targets may also move in an open field. This constrained motion estimation problem is handled using an IMM estimator with varying mode sets depending on the topography, The number of models in the IMM estimator, their types and their parameters are modified adaptively, in real-time, based on the estimated position of the target and the corresponding road/visibility conditions. This topography-based variable structure mechanism eliminates the need for carrying all the possible models throughout the entire tracking period as in the standard IMM estimator, significantly improving performance and reducing computational load. Data association is handled using an assignment algorithm. The estimator is designed to handle a very large number of ground targets simultaneously. A simulated scenario consisting of over one hundred targets is used to illustrate the selection of design parameters and the operation of the tracker. Performance measures are presented to contrast the benefits of the VS-IMM estimator over the Kalman filter and the standard IMM estimator, The VS-IMM estimator is then combined with multidimensional assignment to gain “time-depth.” The additional benefit of using higher dimensional assignment algorithms for data association is also evaluated     

Multiple-model estimation with variable structure. III. Model-groupswitching algorithm

A general multiple-model (MM) estimator with a variable structure (VSMM), railed model-group switching (MGS) algorithm, is presented. It assumes that the total set of models can be covered by a number of model groups, each representing a cluster of closely related system behavior patterns or structures, and a particular group is running at any given time determined by a hard decision. This algorithm is the first VSMM estimator that is generally applicable to a large class of problems with hybrid (continuous and discrete) uncertainties. It is also easily implementable. It is illustrated, via a simple fault detection and identification example, that the MGS algorithm provides a substantial reduction in computation while having identical performance with the fixed-structure Interacting Multiple-Model (FSIMM) estimator     

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     

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     

The split symbol moments SNR estimator in narrow-band channels

The split symbol moments estimator (SSME) is an algorithm that is designed to estimate symbol signal-to-noise ratio (SNR) in the presence of additive white Gaussian noise (AWGN). The performance of the SSME algorithm in bandlimited channels is examined, and the effects of the resulting intersymbol interference (ISI) are quantified. All results obtained are in closed form and can be easily evaluated numerically for performance-prediction purposes. The results are also validated through digital simulations     

Minimal submodel-set algorithm for maneuvering target tracking

The variable structure multiple model (VSMM) approach to the maneuvering target tracking problem is considered. A new VSMM design, the minimal submodel-set switching (MSMSS) algorithm for tracking a maneuvering target is presented. The MSMSS algorithm adaptively determines the minimal set of models from the total model set and uses this to perform multiple models (MM) estimation. In addition, an iterative MSMSS algorithm with improved maneuver detection and termination properties is developed. Simulations results demonstrate that, compared with a standard interacting MM (IMM), the proposed algorithms require significantly lower computation while maintaining similar tracking performance. Alternatively, for a computational load similar to IMM, the new algorithms display significantly improved performance.     

交互作用的双自适应模型估计器

 通过对目标的不确定机动分析和对不确定机动的模式分类(非机动、临界机动、弱机动、强机动),建立了一种新的目标状态自适应估计器——交互作用的双自适应模型估计器。它通过具有机动识别特性的二阶自适应模型和具有机动水平特性的三阶自适应模型,以及它们之间交互作用自适应组合方式的结合,达到了跟踪估计目标各种运动的“全面”自适应能力。应用新估计器对目标的5种基本运动进行了Monto-carlo仿真。仿真表明,它具有所期望的良好性能。     

ALGORITHMS FOR TRACKING MANEUVERING TARGET WITH PHASED ARRAY RADAR

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基于K-均值聚类和约简最小二乘支持向量回归机的推力估计器设计

提出了一种基K-均值聚类和约简最小二乘支持向量回归机的推力估计器设计方法.首先用K-均值聚类法将全包线范围内的数据进行聚类,然后在每一个类当中,用迭代约简最小二乘支持向量回归机设计一个子推力估计器.在用迭代约简最小二乘支持向量回归机设计子推力估计器的过程中,为了使计算数值更稳定,用Cholesky分解代替原来的迭代方法.最后仿真实验表明,此推力估计器能满足直接推力控制的需要,并和其它的方案比较起来,该方案存在一定的优势.