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Aerospace and Electronic Systems, IEEE Transactions on

Issue 2 • Date April 2007

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  • Table of Contents - April 2007, Vol 43 No 2

    Publication Year: 2007 , Page(s): c1
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  • IEEE Aerospace and Electronic Systems Society

    Publication Year: 2007 , Page(s): c2
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  • Our policy on handling complaints of plagiarism [From the Editors]

    Publication Year: 2007 , Page(s): 417
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  • Optimal and self-tuning information fusion Kalman multi-step predictor

    Publication Year: 2007 , Page(s): 418 - 427
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1271 KB) |  | HTML iconHTML  

    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. View full abstract»

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  • Reiterative median cascaded canceler for robust adaptive array processing

    Publication Year: 2007 , Page(s): 428 - 442
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3693 KB) |  | HTML iconHTML  

    A new robust adaptive processor based on reiterative application of the median cascaded canceler (MCC) is presented and called the reiterative median cascaded canceler (RMCC). It is shown that the RMCC processor is a robust replacement for the sample matrix inversion (SMI) adaptive processor and for its equivalent implementations. The MCC, though a robust adaptive processor, has a convergence rate that is dependent on the rank of the input interference-plus-noise covariance matrix for a given number of adaptive degrees of freedom (DOF), N. In contrast, the RMCC, using identical training data as the MCC, exhibits the highly desirable combination of: 1) convergence-robustness to outliers/targets in adaptive weight training data, like the MCC, and 2) fast convergence performance that is independent of the input interference-plus-noise covariance matrix, unlike the MCC. For a number of representative examples, the RMCC is shown to converge using ~ 2.8N samples for any interference rank value as compared with ~ 2N samples for the SMI algorithm. However, the SMI algorithm requires considerably more samples to converge in the presence of outliers/targets, whereas the RMCC does not. Both simulated data as well as measured airborne radar data from the multichannel airborne radar measurements (MCARM) space-time adaptive processing (STAP) database are used to illustrate performance improvements over SMI methods. View full abstract»

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  • Radio interferometer for geosynchronous-satellite direction finding

    Publication Year: 2007 , Page(s): 443 - 449
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1032 KB) |  | HTML iconHTML  

    A radio interferometer capable of azimuth-elevation direction finding for geosynchronous satellites has been developed. The interferometer has two small-diameter antennas and four movable planar mirrors. The movable mirrors reflect the microwaves from the satellite and guide them to the fixed receiving antennas. Two of the movable mirrors are mounted on a rotary arm, so that the baseline of the interferometer can rotate in the horizontal plane. This configuration enables the interferometer to eliminate phase-ambiguity problems and cable-phase errors, resulting in a direction finding accuracy of better than 0.01 deg in the Ku band. View full abstract»

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  • A mean track approach applied to the multidimensional assignment problem

    Publication Year: 2007 , Page(s): 450 - 471
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2803 KB) |  | HTML iconHTML  

    The primary contribution of this paper is the introduction of a new preprocessing method to eliminate unlikely observation-to-track pairs with medium size target-measurement spacings before filter gating is carried out. The proposed method follows a track-oriented approach, with the different track hypotheses being contained in target trees. A mean track is computed for each target tree to capture as much variability as possible from the track hypotheses which are in the tree. Each newly received measurement is first gated with the mean track; only if the outcome of the test is positive, the measurement is gated with all the track hypotheses in the tree. The experimental results support the theoretical claims in the paper and show that a significant reduction in the number of observation-to-tracking pairing tests is achieved. View full abstract»

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  • Noise-correlating radar based on retrodirective antennas

    Publication Year: 2007 , Page(s): 472 - 479
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1130 KB) |  | HTML iconHTML  

    A new retrodirective antenna-based search radar system has been introduced. The suggested system uses a noise correlation technique to detect the presence and the direction of the target. Simulation and analytical results show an order of magnitude improvement in acquisition time of the radar when compared with a phased array antenna-based radar system with the same specifications, except transmit power. To the best knowledge of the authors, no radar of a comparable acquisition time has been designed to this date. Power versus acquisition time tradeoff has been compared with a phased array radar for evaluating performance of the system. The radar is self-tracking due to retrodirectivity of the antenna array, and is much easier to implement, as it does not require any phase shifters etc. View full abstract»

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  • Detection of satellite attitude sensor faults using the UKF

    Publication Year: 2007 , Page(s): 480 - 491
    Cited by:  Papers (13)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1507 KB) |  | HTML iconHTML  

    A novel fault detection (FD) method for nonlinear systems using the residuals generated by the unscented Kalman filter (UKF) is proposed. The errors of the UKF are derived and sufficient conditions for the convergence of the UKF are presented. As the local approach is a powerful statistical technique for detecting changes in the mean of a Gaussian process, it is used to devise a hypothesis test to detect faults from residuals obtained from the UKF. Further, it is demonstrated that the selection of a sample number is important in improving the performance of the local approach. To illustrate the implementation and performance of the proposed technique, it is applied to detect sensor faults in the measurement of satellite attitude. View full abstract»

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  • Efficient fault tolerant estimation using the IMM methodology

    Publication Year: 2007 , Page(s): 492 - 508
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1458 KB) |  | HTML iconHTML  

    Space systems are characterized by a low-intensity process noise resulting from uncertain forces and moments. In many cases, their scalar measurement channels can be assumed to be independent, with one-dimensional internal dynamics. The nominal operation of these systems can be severely damaged by faults in the sensors. A natural method that can be used to yield fault tolerant estimates of such systems is the interacting multiple model (IMM) filtering algorithm, which is known to provide very accurate results. However, having been derived for a general class of systems with switching parameters, the IMM filter does not utilize the independence of the measurement errors in different channels, nor does it exploit the fact that the process noise is of low intensity. Thus, the implementation of the IMM in this case is computationally expensive. A new estimation technique is proposed herein, that explicitly utilizes the aforementioned properties. In the resulting estimation scheme separate measurement channels are handled separately, thus reducing the computational complexity. It is shown that, whereas the IMM complexity is exponential in the number of fault-prone measurements, the complexity of the proposed technique is polynomial. A simulation study involving spacecraft attitude estimation is carried out. This study shows that the proposed technique closely approximates the full-blown IMM algorithm, while requiring only a modest fraction of the computational cost. View full abstract»

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  • Pop-up threat models for persistent area denial

    Publication Year: 2007 , Page(s): 509 - 521
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1280 KB) |  | HTML iconHTML  

    Pop-up threats usually appear or disappear randomly in a battle field. If the next pop-up threat locations could be predicted it would assist a search or attack team, such as in a persistent area denial (PAD) mission, in getting a team of unmanned air vehicles (UAVs) to the threats sooner. We present a Markov model for predicting pop-up ground threats in military operations. We first introduce a general Markov chain of order n to capture the dependence of the appearance of pop-up threats at previous locations of the pop-up threats over time. We then present an adaptive approach to estimate the stationary transition probabilities of the nth order Markov models. To choose the order of the Markov chain model for a specific application, we suggest using hypothesis tests from statistical inference on historical data of pop-up threat locations. Anticipating intelligent responses from an adversary, which might change its pop-up threat deployment strategy upon observing UAV movements, we present adaptive Markov chain models using a moving horizon approach to estimate possibly abrupt changes in transition probabilities. We combine predicted and actual pop-up target locations to develop efficient cooperative strategies for networked UAVs. A theoretical analysis and simulation results are presented to evaluate the Markov model used for predicting pop-up threats. These results demonstrate the effectiveness of cooperative strategies using the combined information of threats and predicted threats in improving overall mission performance. View full abstract»

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  • Costas array generation and search methodology

    Publication Year: 2007 , Page(s): 522 - 538
    Cited by:  Papers (8)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1147 KB) |  | HTML iconHTML  

    Costas arrays are permutation matrices that provide sequencing schemes for frequency hop in FSK waveforms. Such frequency-shift keying (FSK) waveforms can be designed to have nearly ideal ambiguity function properties in both the time and frequency directions: the Costas property permits at most one coincident tone in autocorrelations in both time and frequency. Costas arrays are found by number-theoretic generators and their extensions, and by exhaustive search methods. Two new extensions of number-theoretic methods are introduced here that find two new Costas arrays. All Costas arrays for orders 24, 25, and 26 are disclosed here, including previously unknown examples. View full abstract»

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  • PCRLB-based multisensor array management for multitarget tracking

    Publication Year: 2007 , Page(s): 539 - 555
    Cited by:  Papers (22)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2267 KB) |  | HTML iconHTML  

    In this paper we consider the general problem of managing an array of sensors in order to track multiple targets in the presence of clutter. There are three complicating factors. The first is that because of physical limitations (e.g., communication bandwidth) only a small subset of the available sensors can be utilized at any one time. The second complication is that the associations of measurements to targets/clutter are unknown. The third complication is that the total number of targets in the surveillance region is unknown and possibly time varying. It are these second and third factors that extend previous work [ Tharmarasa, R., Kirubarajan, T., and Hernandez, M. L. Large-scale optimal sensor array management for multitarget tracking. IEEE Transactions on Systems, Man, and Cybernetics, to be published.]. Hence sensors must be utilized in an efficient manner to alleviate association ambiguities and to allow accurate estimation of the states of a varying number of targets. We pose the problem as a bi-criterion optimization with the two objectives of (1) controlling the posterior Cramer-Rao lower bound ((PCRLB) which provides a measure of the optimal achievable accuracy of target state estimation), and (2) maximizing the probability of detecting new targets. Only recently have expressions for multitarget PCRLBs been determined [Hue, C, Le Cadre, J.-P., and Perez, P]. Performance analysis of two sequential Monte Carlo methods and posterior Cramer-Rao bounds for multitarget tracking. In Proceedings of the 5th International Conference on Information Fusion, vol. 1, Annapolis, MD, July 2002, 464-473.], and the necessary simulation techniques are computationally expensive. However, in this paper we show the existence of a multitarget information reduction matrix (IRM) which can be calculated off-line in most cases. Additionally, we propose some approximations that further reduce the computational load. We present solution methodologies that, in simulations, are shown t- o determine efficient utilization strategies for the available sensor resources, with some sensors selected to track existing targets and others given the primary task of surveillance in order to identify new threats. View full abstract»

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  • Novel data association schemes for the probability hypothesis density filter

    Publication Year: 2007 , Page(s): 556 - 570
    Cited by:  Papers (29)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2058 KB) |  | HTML iconHTML  

    The probability hypothesis density (PHD) filter is a practical alternative to the optimal Bayesian multi-target Alter based on finite set statistics. It propagates the PHD function, a first-order moment of the full multi-target posterior density. The peaks of the PHD function give estimates of target states. However, the PHD filter keeps no record of target identities and hence does not produce track-valued estimates of individual targets. We propose two different schemes according to which PHD filter can provide track-valued estimates of individual targets. Both schemes use the probabilistic data-association functionality albeit in different ways. In the first scheme, the outputs of the PHD filter are partitioned into tracks by performing track-to-estimate association. The second scheme uses the PHD filter as a clutter filter to eliminate some of the clutter from the measurement set before it is subjected to existing data association techniques. In both schemes, the PHD filter effectively reduces the size of the data that would be subject to data association. We consider the use of multiple hypothesis tracking (MHT) for the purpose of data association. The performance of the proposed schemes are discussed and compared with that of MHT. View full abstract»

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  • Robust SVA method for every sampling rate condition

    Publication Year: 2007 , Page(s): 571 - 580
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2328 KB) |  | HTML iconHTML  

    Linear apodization, or data weighting, is the traditional procedure to improve sidelobe levels in a finite sampled signal at the expense of resolution. New apodization methods, such as spatially variant apodization (SVA), apply nonlinear filtering to the signal in order to completely remove sidelobes without any loss of resolution. However, the results are strongly influenced by signal sampling rate. Some variations which improve results have been previously published, but sidelobe cancellation gets worse since sampling frequency is not settled at Nyquist (or a multiple). This paper presents a new and efficient technique based on SVA that drastically reduces sidelobe levels for every sampling rate condition. The algorithm is, essentially, a parameter optimization of a variant filter for each pixel of the image. A one-dimensional case and a two-dimensional generalization are presented, as well as some applications to target detection capability in a synthetic aperture radar (SAR) system. View full abstract»

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  • Simultaneous tracking and classification: a modularized scheme

    Publication Year: 2007 , Page(s): 581 - 599
    Cited by:  Papers (7)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1726 KB) |  | HTML iconHTML  

    The high computational complexity of existing joint tracking and classification (JTC) algorithms hampers their application. After presenting a new description of the JTC problem--simultaneous tracking and classification (STC) instead of JTC, we derive two STC algorithms in both exact and approximate forms by applying Hayes' rule to the target state probability density function (pdf) and target class probability mass function (pmf) simultaneously under the assumption that the kinematic and attribute measurement processes are conditional independent. The mutual information exchange between tracker and classifier of the proposed STC algorithms is introduced by defining the simultaneous pdf-pmf of target state and class, the dependence of kinematic measurement on target class, the dependence of attribute measurement on target state and target model, class-dependent kinematic model sets, and class-dependent flight envelopes, etc. The proposed STC algorithms have four distinctive features. First, they have a modularized structure, i.e., they explicitly integrate a multiple-model filter and a Bayesian classifier. Second, the approximate versions, which follow easily from the proposed STC algorithms thanks to their modularized structure, have a closed form with a lower computational complexity and are more suitable for real-time applications. Third, the proposed exact STC algorithms are derived without the hidden approximation made in some existing multiple-model based JTC algorithms. Fourth, one of the proposed STC algorithms has the potential to further reduce the computational load since it has no redundant motion models. Simulation results suggest that the proposed STC algorithms provide a hopeful solution to a class of STC problems. View full abstract»

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  • Simple adaptive control for aircraft control surface failures

    Publication Year: 2007 , Page(s): 600 - 611
    Cited by:  Papers (2)  |  Patents (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (876 KB) |  | HTML iconHTML  

    This work extends the so-called simple adaptive control approach to direct model reference adaptive control of multi-input multi-output systems to include loss of control effectiveness failures. It is proven that all signals are bounded for loss of control effectiveness failures during a bounded input disturbance. A state space approach is introduced for computing the feedforward compensator that is required by the stability result. The adaptive algorithm is applied to a three input model of the linearized lateral dynamics of the F/A-18 aircraft. Simulation results are obtained with single, double, and triple control effectiveness failures of 88% during the occurrence of a lateral gust. These results show that the adaptive controller exhibits improved model following as compared with a fixed gain eigenstructure assignment controller. View full abstract»

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  • Bayesian tracking of two possibly unresolved maneuvering targets

    Publication Year: 2007 , Page(s): 612 - 627
    Cited by:  Papers (6)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (602 KB) |  | HTML iconHTML  

    The paper studies the problem of maintaining tracks of two targets that may maneuver in and out formation flight, whereas the sensor and measurement extraction chain produces false and possibly unresolved or missing measurements. If the possibility of unresolved measurements is not modelled then it is quite likely that either the two tracks coalesce or that one of the two tracks diverges on false measurements. In literature a robust measurement resolution model has been incorporated within an interacting multiple model/multiple hypothesis tracking (IMM/MHT) track maintenance setting. A straightforward incorporation of the same model within an IMM and probabilistic data association (PDA)-like hypothesis merging approach suffers from track coalescence. In order to improve this situation, the paper develops a track-coalescence avoiding hypotheses merging version for the two target problem considered. Through Monte Carlo simulations, the novel filters are compared with applying hypotheses merging approaches that ignore the possibility of unresolved measurements or track-coalescence. View full abstract»

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  • Loran data modulation: extensions and examples

    Publication Year: 2007 , Page(s): 628 - 644
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4967 KB) |  | HTML iconHTML  

    Loran has provided navigation service since 1958. Though not originally designed with data broadcast capabilities, Loran's versatility has enabled data to be broadcast with great benefits. Research in the last two decades has resulted in a tremendous increase in the data capacity of Loran thereby increasing its utility. Currently, a modernized Loran is being evaluated for its capability to backup GPS and data modulation is an integral part of this Loran design. This paper details some recent Loran modulation designs and ideas. View full abstract»

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  • Performance limits of sensor-scheduling strategies in electronic support

    Publication Year: 2007 , Page(s): 645 - 650
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (393 KB) |  | HTML iconHTML  

    The case is considered in which a frequency-agile receiver (FAR) for electronic support (ES) attempts to intercept radar emissions over a wide search bandwidth. It was recently shown [1,2] that a random strategy exists in which the expected intercept time can be made arbitrarily close to linear as a function of the scan period of the radar. Can a deterministic strategy be devised in which a similar linear relationship exists for the maximum intercept time? By applying the celebrated arithmetic results of van der Waerden [3] and Szemeredi [4], we show that no such strategy is possible. View full abstract»

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  • Adaptive cancellation method for geometry-induced nonstationary bistatic clutter environments

    Publication Year: 2007 , Page(s): 651 - 672
    Cited by:  Papers (14)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4424 KB) |  | HTML iconHTML  

    This paper describes and characterizes a new bistatic space-time adaptive processing (STAP) clutter mitigation method. The approach involves estimating and compensating aspects of the spatially varying bistatic clutter response in both angle and Doppler prior to adaptive clutter suppression. An important feature of the proposed method is its ability to extract requisite implementation information from the data itself, rather than rely on ancillary - and possibly erroneous or missing - system measurements. We justify the essence of the proposed method by showing its ability to align the dominant clutter subspaces of each range realization relative to a suitably chosen reference point as a means of homogenizing the space-time data set. Moreover, we numerically characterize performance using synthetic bistatic clutter data. For the examples considered herein, the proposed bistatic STAP method leads to maximum performance improvements between 17.25 dB and 20.75 dB relative to traditional STAP application, with average improvements of 6 dB to 10 dB. View full abstract»

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  • Efficient wideband signal parameter estimation using a radon-ambiguity transform slice

    Publication Year: 2007 , Page(s): 673 - 688
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4182 KB) |  | HTML iconHTML  

    A novel efficient technique based on a single slice Radon-ambiguity transform (RAT) for time-delay and time-scale estimation is proposed. The proposed approach combines the narrowband cross-ambiguity function (NBCAF), the wideband cross-ambiguity function (WBCAF), and a single slice RAT to estimate multiple target parameters in noisy environments. The square modulus of Gaussian-enveloped linear frequency modulated (GLFM) signals has high-energy centrality in the ambiguity plane. Its peaks in the NBCAF fall along nearly straight lines whose slopes depend on the Doppler rates of the moving targets. These lines could be effectively detected by computing the entire Radon transform of the NBCAF for all possible angles; however, it is a computationally intensive procedure. It is shown that without calculating the entire RAT, it is possible to estimate target parameters using only a single slice of the RAT, i.e., using an appropriate projection of the NBCAF. It is demonstrated that the proposed method can successfully separate overlapping targets efficiently. The efficiency is achieved due to fast Fourier transform (FFT)-bascd processing, use of a single slice of RAT, and the use of only one-dimensional (1-D) searches. View full abstract»

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  • Rao-blackwellised particle filtering in random set multitarget tracking

    Publication Year: 2007 , Page(s): 689 - 705
    Cited by:  Papers (25)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1063 KB) |  | HTML iconHTML  

    This article introduces a Rao-Blackwellised particle filtering (RBPF) approach in the finite set statistics (FISST) multitarget tracking framework. The RBPF approach is proposed in such a case, where each sensor is assumed to produce a sequence of detection reports each containing either one single-target measurement, or a "no detection" report. The tests cover two different measurement models: a linear-Gaussian measurement model, and a nonlinear model linearised in the extended Kalman filter (EKF) scheme. In the tests, Rao-Blackwellisation resulted in a significant reduction of the errors of the FISST estimators when compared with a previously proposed direct particle implementation. In addition, the RBPF approach was shown to be applicable in nonlinear bearings-only multitarget tracking. View full abstract»

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  • Accommodating controller malfunctions through fault tolerant control architecture

    Publication Year: 2007 , Page(s): 706 - 722
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3287 KB) |  | HTML iconHTML  

    In previous work we have proposed a supervised globalized dual heuristic programming (GDHP) controller as a solution to the fault tolerant control (FTC) problem of nonlinear plants subject to abrupt and incipient faults capable of drastically modifying the system dynamics to maintain stability and performance. The neural network (NN) based adaptive critic controller presented the best choice for the flexibility and power necessary to accomplish the task, however no success guarantees can be made for the online training of neural weights for the unrestricted fault recovery problem. Built on the existing framework, we propose a novel supervisory system capable of detecting controller malfunctions before the stability of the plant is compromised. Furthermore, due to its ability to discern between controller malfunctions and faults within the plant, the proposed supervisor acts in a specific fashion in the event of a controller malfunction to provide new avenues with a greater probability of convergence using information from a dynamic model bank. The classification and distinction of controller malfunctions from the faults in the plant itself is achieved through an advanced decision logic based on three independent quality indexes. Proof-of-the-concept simulations over a nonlinear plant demonstrate the validity of the approach. View full abstract»

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  • Optimal motion planning for dual-spacecraft interferometry

    Publication Year: 2007 , Page(s): 723 - 737
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2192 KB) |  | HTML iconHTML  

    We address the question of design and optimal control of a class of dual-spacecraft interferometric imaging formations. The first main contribution is that we combine two ideas introduced separately in the literature and propose a maneuver that offers improved imaging performance. We then formulate an optimal control problem to minimize fuel consumption and maximize image quality by minimizing the relative speed, which is proportional to the signal-to-noise ratio (SNR) of the reconstructed image. We show that the necessary conditions are also sufficient and that the resulting optimal control is unique. Finally, we apply a continuation method to solve for the unique optimal trajectory. View full abstract»

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Aims & Scope

IEEE Transactions on Aerospace and Electronic Systems focuses on the equipment, procedures, and techniques applicable to the organization, installation, and operation of functional systems designed to meet the high performance requirements of earth and space systems.

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Meet Our Editors

Editor-in-Chief
Lance Kaplan
Army Research Laboratory