Mixed H/sub 2//H/sub /spl infin// filtering for uncertain systems with regional pole assignment

The mixed H/sub 2//H/sub /spl infin// filtering problem for uncertain linear continuous-time systems with regional pole assignment is considered. The purpose of the problem is to design an uncertainty-independent filter such that, for all admissible parameter uncertainties, the following filtering requirements are simultaneously satisfied: 1) the filtering process is asymptotically stable; 2) the poles of the filtering matrix are located inside a prescribed region that compasses the vertical strips, horizontal strips, disks, or conic sectors; 3) both the H/sub 2/ norm and the H/sub /spl infin// norm on the respective transfer functions are not more than the specified upper bound constraints. We establish a general framework to solve the addressed multiobjective filtering problem completely. In particular, we derive necessary and sufficient conditions for the solvability of the problem in terms of a set of feasible linear matrix inequalities (LMIs). An illustrative example is given to illustrate the design procedures and performances of the proposed method.     

Reduced-order H/sub /spl infin// and L/sub 2/-L/sub /spl infin// filtering via linear matrix inequalities

Necessary and sufficient conditions are derived for the existence of a solution to the continuous-time and discrete-time reduced-order H/sub /spl infin// and L/sub 2/-L/sub /spl infin// filtering problems. These conditions are expressed in terms of linear matrix inequalities (LMIs) and a coupling nonconvex matrix rank constraint. Convex LMI problems are obtained for the full-order and the zeroth-order filtering. An explicit parametrization of all reduced-order filters that correspond to a feasible solution is derived in terms of a contractive matrix, and iterative algorithms are proposed to solve the reduced-order filtering problems using alternating projections.     

Robust neuro-H/sub /spl infin// controller design for aircraft auto-landing

A robust neuro-control scheme is presented for aircraft auto-landing under severe wind conditions and partial loss of control surfaces. In the scheme, a dynamic radial basis function network (RBFN) called minimal resource allocating network (MRAN), that incorporates a growing and pruning strategy, is utilize to aid an H/sub /spl infin// controller using a feedback-error-learning mechanism. The neural network uses only online learning and is not trained "a priori". Specifically, the performance of this neuro-controller for aircraft auto-landing in a microburst along with a partial loss of control effectiveness is analyzed and compared with other control schemes. Simulation studies show that the performance obtained by the neuro-H/sub /spl infin// control scheme is better than the other control schemes under failure and extreme wind conditions.     

Robust H/sub /spl infin// filtering of fuzzy dynamic systems

Robust H/sub /spl infin// filtering of complex nonlinear systems which can be represented by a fuzzy dynamic model is presented. Based on a nominal model, a common positive definite matrix P, and a piecewise continuous Lyapunov function respectively, three kinds of new filtering design methods are proposed using quadratic stability theory and linear matrix inequalities (LMIs). It is shown that the filtering system is quadratically stable with disturbance attenuation if there exists a positive definite matrix solution to a LMI or a set of LMIs. An example is also given to demonstrate the performance of the proposed filtering design methods.     

Discrete-time min-max tracking

The problem of optimal state estimation of linear discrete-time systems with measured outputs that are corrupted by additive white noise is addressed. Such estimation is often encountered in problems of target tracking where the target dynamics is driven by finite energy signals, whereas the measurement noise is approximated by white noise. The relevant cost function for such tracking problems is the expected value of the standard H/sub /spl infin// performance index, with respect to the measurement noise statistics. The estimator, serving as a tracking filter, tries to minimize the mean-square estimation error, and the exogenous disturbance, which may represent the target maneuvers, tries to maximize this error while being penalized for its energy. The solution, which is obtained by completing the cost function to squares, is shown to satisfy also the matrix version of the maximum principle. The solution is derived in terms of two coupled Riccati difference equations from which the filter gains are derived. In the case where an infinite penalty is imposed on the energy of the exogenous disturbance, the celebrated discrete-time Kalman filter is recovered. A local iterations scheme which is based on linear matrix inequalities is proposed to solve these equations. An illustrative example is given where the velocity of a maneuvering target has to be estimated utilizing noisy measurements of the target position.     

Problem of precision missile guidance: LQR and H/sup /spl infin// control frameworks

Addressed here is the precision missile guidance problem where the successful intercept criterion has been defined in terms of both minimizing the miss distance and controlling the missile body attitude with respect to the target at the terminal point. We show that the H/sup /spl infin// control theory, when suitably modified, provides an effective framework for the precision missile guidance problem. The existence of feedback controllers (guidance laws) is investigated for the case of finite horizon and non-zero initial conditions. Both state feedback and output feedback implementations are explored.     

Nonlinear robust observer design for strapdown INS in-flight alignment

A nonlinear observer is proposed for a strapdown inertial navigation system (SDINS) in-flight alignment problem using an H/sub /spl infin// filter Riccati equation and a freedom parameter. The proposed observer improves the filtering stability, convergence, and performance. The characteristics of the observer are analyzed using a Lyapunov function. Simulation results demonstrate a significant reduction in alignment errors by employing the proposed nonlinear observer. The observer is developed in general such that it can be applied to estimating nonlinear systems other than the SDINS in-flight alignment.     

Tracker design based on target perceivability

Theoretical design of a tracker with respect to the so-called target perceivability is presented. Basic rules and analytic formulas for the determination of tracker parameters are presented for making better tracking decisions (i.e., track initiation, confirmation, and termination) toward enhancing tracking performance. Simulation results are provided that support the theoretical design and demonstrate the enhancement of the tracker in tracking performance     

Multiframe detector/tracker: optimal performance

We develop the optimal Bayes multiframe detector/tracker for rigid extended targets that move randomly in clutter. The performance of this optimal algorithm provides a bound on the performance of any other suboptimal detector/tracker. We determine by Monte Carlo simulations the optimal performance under a variety of scenarios including spatially correlated Gaussian clutter and non-Gaussian (K and Weibull) clutter. We show that, for similar tracking performance, the optimal Bayes tracker can achieve peak signal-to-noise ratio gains possibly larger than 10 dB over the commonly used combination of a spatial matched filter (spatial correlator) and a linearized Kalman-Bucy tracker. Simulations using real clutter data with a simulated target suggest similar performance gains when the clutter model parameters are unknown and estimated from the measurements     

PMHT: problems and some solutions

The probabilistic multihypothesis tracker (PMHT) is a target tracking algorithm of considerable theoretical elegance. In practice, its performance turns out to be at best similar to that of the probabilistic data association filter (PDAF); and since the implementation of the PDAF is less intense numerically the PMHT has been having a hard time finding acceptance. The PMHT's problems of nonadaptivity, narcissism, and over-hospitality to clutter are elicited in this work. The PMHT's main selling-point is its flexible and easily modifiable model, which we use to develop the "homothetic" PMHT; maneuver-based PMHTs, including those with separate and joint homothetic measurement models; a modified PMHT whose measurement/target association model is more similar to that of the PDAF; and PMHTs with eccentric and/or estimated measurement models. Ideally, "bottom line" would be a version of the PMHT with clear advantages over existing trackers. If the goal is of an accurate (in terms of mean square error (MSE)) track, then there are a number of versions for which this is available.     

Uniform versus nonuniform sampling when tracking in clutter

Many target tracking subsystems have the ability to schedule their own data rates; essentially they can "order" new information whenever they need it, and the cost is in terms of the sensor resource. But among the unmanaged schemes, uniform sampling, in which a new measurement is requested periodically and regularly, is the most commonly-used sampling scheme; deliberately nonuniform schemes are seldom given serious consideration. In this paper, however, we show that such schemes may have been discarded prematurely: a nonuniform sampling can have its benefits. Specifically, the nonuniform and uniform sampling schemes are compared for two kind of trackers: the probabilistic data association filter (PDAF), which updates its track based on a single scan of information at a time; and N-D assignment (an optimization-based implementation of the multi-hypothesis tracker (MHT)), in which the sliding window involves many scans of observations. We find that given the ground rule of maintenance of the same overall scan rate (i.e., the same sensor effort) uniform sampling is always optimal for the single-scan tracker in the sense of track life. However, nonuniform sampling can outperform uniform sampling if a more sophisticated multi-scan tracker is used, particularly when 1) the target has a high process noise, and/or 2) the false alarm density is high, and/or 3) the probability of detection is high.     

Target tracking using infrared measurements and laser illumination

A missile target tracker is designed using a filter/correlator (with adaptive target shape identification) based on forward-looking infrared (FLIR) sensor measurements to track the center-of-intensity of the hardbody/plume combination, and another filter using Doppler and/or speckle information in the return from a low-power laser illuminator to estimate the offset between the intensity centroid and the hardbody center-of-mass. The Doppler information is shown to yield smaller bias and error variance from the tracker than the speckle information. Performance of trackers based on just Doppler or both Doppler and speckle information from the laser return is portrayed as a function of important parameters in the tracking environment     

航空发动机控制系统性能恢复设计:H_∞/PR

对于航空发动机这样一类状态及输出矩阵存在摄动的系统,提出了一种直接根据性能指标作为恢复的目标的性能恢复理论设计方法。首先进行具有稳定及性能鲁棒性的状态反馈的H∞控制设计,然后通过设计一个状态观测器使得上述状态反馈控制的特性得以恢复。     

Precession–nutation correction for star tracker attitude measurement of STECE satellite

Space Technology Experiment and Climate Exploration(STECE) is a small satellite mission of China for space technology experiment and climate exploration. A new test star tracker and one ASTRO 10 star tracker have been loaded on the STECE satellite to test the new star tracker's measurement performance. However,there is no autonomous precession–nutation correction function for the test star tracker,which causes an apparent periodic deflection in the inter-boresight angle between the two star trackers with respect to each other of up to ±500 arcsec,so the precession and nutation effect needs to be considered while assessing the test star tracker. This paper researches on the precession–nutation correction for the test star tracker's attitude measurement and presents a precession–nutation correction method based on attitude quaternion data. The periodic deflection of the inter-boresight angle between the two star trackers has been greatly eliminated after the precession and nutation of the test star tracker's attitude data have been corrected by the proposed method and the validity of the proposed algorithm has been demonstrated. The in-flight accuracy of the test star tracker has been assessed like attitude noise and low-frequency errors after the precession–nutation correction.     

Theorem for the combination of bistatic radar measurements using least squares

In order to improve position finding performance, least squares method is often used to combine the measurement sets of bistatic radar system. However, which measurement sets can be combined by least squares method and which cannot have received little attention until now. We address this issue based on 2 measurements of bistatic radar system, for example (/spl rho/, /spl theta//sub T/), and present a new theorem, with proof, which shows that the least squares estimator can be obtained by combining any two measurement sets if these measurement pairs are disjoint. We provide an example that satisfies this condition, which shows that the least square combination of measurement sets yield improved performance especially in the vicinity of the transmitter and the receiver.     

Performance limits of track-to-track fusion versus centralized estimation: theory and application [sensor fusion]

Track-to-track fusion is an important part in distributed multisensor-multitarget tracking. The centralized and distributed tracking configurations were studied in (H.Chen et al., Proc. of SPIE Conf. on Signal and Data Processing of Small Targets, vol. 4048, 2000) using simulated air-to-air scenarios, and in (K.C. Chang, et al, IEEE Transact. on Aerospace and Electronic Systems, vol. 33, no. 4, pp. 1271-1276, 1997) with analytical results based on /spl alpha/-/spl beta/ filters. The current work generalizes the results in the latter to the cases with more than 2 sensors. As the number of sensors increases, the performance of the distributed tracker is shown to degrade compared with the centralized estimation even when the optimal track-to-track fusion is used. An approximate track-to-track fusion is presented and compared with the optimal track-to-track fusion with performance curves for various numbers of sensors. These performance curves can be used in designing a fusion system where certain trade-offs need to be considered. Finally, these results are compared with simulation results for a realistic air-to-air encounter scenario.     

PCM/FM aeronautical telemetry in frequency selective multipath interference

The performance of PCM/FM using a delay-line detector in multipath interference is analyzed. In aeronautical telemetry applications, the multipath interference causes a in the signal spectrum which is web modeled by a channel composed of a line-of-sight signal and a strong specular reflection with magnitude /spl Gamma/ and excess delay less than a bit time. Analysis shows that the bit error probability is strongly dependent on the location of the within the spectrum. The bit error probability averaged over the location is also analyzed. For the case of no predetection filtering, the average bit error probability is dependent on the depth of the but not on the width. The loss in performance (relative to the AWGN environment) is bounded by (1-/spl Gamma/)/sup -2/ which is the depth of the . For the case of predetection filtering, the average bit error probability is dependent on both the width and depth of the . The performance loss is bounded by (1-/spl Gamma/)/sup -2/spl Gamma// which is less than that for the case of no predetection filtering.     

面向目标的概率多假设跟踪算法

 概率多假设跟踪(PMHT)算法由于其计算量与目标和量测的个数成线性关系而成为一种重要的数据关联方法,但该算法采用的是一种面向量测的参数模型,容易受到杂波的干扰。针对这个问题,提出一种面向目标的PMHT（TO/PMHT）算法,将量测与目标的距离作为权重,使计算出的后验关联概率能够真实地反映量测和目标的关联可能性。通过多种典型环境的仿真计算表明：TO/PMHT算法和面向量测的PMHT算法相比,跟踪性能有了明显的提高;与多假设跟踪(MHT)算法相比,在保持跟踪性能的同时,极大地提高了计算效率。     

A Kalman Tracker with a Simple Input Estimator

Two Kalman filter based schemes are proposed for tracking maneuvering targets. Both schemes use least squares to estimate a target's acceleration input vector and to update the tracker by this estimate. The first scheme is simpler and by an approximation to its input estimator the computation can be considerably reduced with insignificant performance degradation. The second scheme requires two Kalman filters and hence is more complex. However, since one of its two filters assumes input noise, it may outperform the first scheme when input noise is indeed present. A detector that compares the weighted norm of the estimated input vector to a threshold is used in each scheme. Its function is to guard against false updating of the trackers and to keep the error covariance small during constant velocity tracks. Simulation results for various target profiles are included. They show that in terms of tracking performance, both schemes are comparable. However, because of its computation simplicity, the first scheme is far superior.