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Fault-tolerant attitude tracking control for rigid-flexible coupled spacecraft is studied in this paper. Taking actuator faults, measurement errors of attitude and angular velocity, unmeasured modal displacements, model uncertainties, and external disturbances into consideration, an adaptive fault-tolerant attitude tracking control is designed. Then based on the Sequential Lyapunov Method, the stability of the closed-loop system is analyzed showing that, if the attitude and angular velocity measurement errors are bounded, then successively less conservative bounds on the attitude tracking error and the angular velocity error can be obtained.

This letter shows the first acquisition, Doppler tracking, and positioning results with Starlinks low Earth orbit (LEO satellite signals. A generalized likelihood ratio (GLR -based test is proposed to acquire Starlinks downlink signals. A Kalman filter (KF -based algorithm for tracking the Doppler frequency from the unknown Starlink signals is developed. Experimental results show Doppler tracking of six Starlink satellites, achieving a horizontal positioning error of 10 m.

The plasma sheath will be generated on the surface of the reentry object due to the effect of aerodynamic thermoionization, causing multi-component signals with Doppler frequency components in the radar echo. This situation will further result in the appearance of false targets that seriously affect the detection of reentry targets by radar, causing positioning errors and even tracking losses. In this paper, the echo signal model of the plasma-sheath-covered reentry target is established, and the multi-domain characteristics of the echo signal in terms of time, time-frequency and range are analyzed, revealing the influence mechanism of plasma sheath on radar detection of reentry target. Through range-period analysis, the influence of intra-pulse Doppler frequency caused by plasma sheath is determined, as well as inter-pulse Doppler frequency on target range measurement. Theoretical analyses conducted in this study are verified by simulation results, laying theoretical foundation for effective detection and reliable tracking of plasma-sheath-covered reentry targets.

Development and performance of an endpoint-constrained (EPC) extended Kalman filter (EKF) was published by Haug and Ford, where the EPC EKF was shown to outperform similar non-EPC EKFs against highly maneuverable anti-ship cruise missile (ASCM) trajectories. The purpose of this paper is to resolve two issues identified during the original study. The first issue is that EKFs may not offer superior performance when the target's motion is highly nonlinear. The second issue is that the EPC EKF performs poorly against the high diver trajectory, where the target does not satisfy the EPC until the dive begins. This paper presents a new filter which uses a modified version of the EPC and a multiple model algorithm to achieve superior performance against a larger set of ASCM trajectories than the EPC EKF.

This article presents a fuzzy adaptive tracking control algorithm for hypersonic flight vehicles with input quantization and faults. Different from the usual stabilization results, we present a method to achieve asymptotic tracking with predefined-time performance. An error transformation is established and a modified Lyapunov function (MLF) is constructed. The predefined-time asymptotic tracking control goal can be achieved by simply guaranteeing the boundness of the proposed MLF which is related to the transformed tracking errors. The proposed control strategy has three advantages. First, the predefined-time tracking performance can be ensured even when the initial tracking conditions are completely unknown. Second, no compensation is required for the fuzzy reconstruction errors to achieve accurate tracking control such that the control design is significantly simplified. Third, the derived controller is fault-tolerant, and since the system is controlled by quantized signals, the communication burden can be considerably reduced. Theoretical proof and numerical simulation are presented to demonstrate the effectiveness of the proposed control algorithm.

Accurate georegistration is required in airborne surveillance of ground targets since even a small bias in sensor registration may result in a large error in the converted target geolocation. Thus, this article addresses the problem of estimating and correcting the sensor biases in target geolocation. The effect of sensor biases on target geolocation is modeled and decoupled from the true target geolocation. By assuming that the biases are small, the effect of biases is separately addressed by a linear term in the converted target geolocation. Based on this model, two computationally efficient methods are proposed to estimate the biases. The converted target geolocation can then be corrected based on the bias estimates. Simulation results demonstrate the effectiveness of the proposed methods in estimating sensor biases and correcting target geolocations as well as their high computational efficiency.

Multidetection (MD) systems are characterized by multiple observation modes (OMs), and hence, simultaneously produce multiple measurements for each target. The key challenge in exploiting MD systems for multitarget tracking (MTT), compared to single-detection (SD) systems, is the significant amount of extra computational burden involved in order to solve the resulting multidimensional assignment problem among measurements, targets, and OMs. This article presents a novel computationally efficient MTT framework for MD systems, wherein the multitarget state is modeled as a random finite set (RFS), and a bank of OM-dependent MTT RFS filters with SD model are employed to recursively provide OM-dependent posteriors. The latter, which contain both real and false targets, are then suitably fused so as to enhance consensus on the true targets while weakening trust on the existence of the false ones. In this way, the computational complexity is significantly reduced compared to existing MTT algorithms with the MD model. Two representative RFS filters, i.e., unlabeled probability hypothesis density (PHD) and labeled multi-Bernoulli (LMB), are considered in the proposed framework and the computational complexity of the resulting MD MTT algorithms is analyzed. Performance of the proposed approach is assessed by simulation experiments in both over-the-horizon-radar (OTHR) and single-frequency-network passive radar (SFN-PR) MTT applications.

This article is devoted to the problem of multitarget tracking in the maritime environment using Bernoulli track-before-detect (TBD) filtering. Detection and tracking of weak targets in sea clutter using high-resolution airborne radar is notoriously challenging compared to classical target tracking problems. Furthermore, the measured amplitude in range–azimuth cells that are in the vicinity of closely spaced (interacting) targets represents a superposition of individual target contributions, demanding a special joint treatment. This article proposes a novel solution for multitarget tracking using Bernoulli TBD for maritime radar and covers cases when targets are both far apart and closely spaced. Comparisons with existing TBD algorithms are used to verify the efficacy of the proposed Bernoulli TBD algorithm with realistic sea clutter simulations used to create challenging scenarios with variable target strengths.

Leveraging the benefits of the multi-objective Particle Swarm Optimisation (PSO) technique, we introduce a new concept of Adaptive Strictly Negative Imaginary (SNI) Controllers. The proposed adaptive control systems are specifically designed to minimize a certain performance index, representing the objective of our control design, which is to obtain a stable,robust, and responsive 3D tracking of the AR.Drone drone in the face of wind gusts. We compare the performance of our proposed adaptive controllers with respect to the performance of PSO-PID control systems and the traditional Ziegler-Nichols PID controllers, not only in simulated flights but also real flight tests.We also present a stability analysis of the closed-loop control system using the SNI system theory. Refereed/Peer-reviewed

This article presents an innovative framework of real-time vision-based pose tracking for asteroid using the contour information of the asteroid image. At the first-time instant, the tracking is initialized by the distance-based template matching and contour-based pose optimization. Subsequently, at each time instant, with the prediction of the extended Kalman filter (EKF) as initial guess, the pose of the asteroid is obtained in real time by geometrically fitting the contour of the projected asteroid CAD model over the contour of the asteroid image with M-estimation. The variance of the pose is calculated based on 1-order approximation inference, which enables EKF to generate the final pose estimation and predict the pose at the next time instant. Sufficient experiments validate the accuracy and the efficiency of the proposed method.

Sensor registration is an important problem that must be considered when attempting to perform any kind of data fusion in multimodal, multisensor target tracking. In this multiple target tracking (MTT) application, any inaccuracies in the registration can lead to false tracks being created, and tracks of true targets being stopped prematurely. This article introduces a method for simultaneously tracking multiple targets in a surveillance region and estimating appropriate sensor registration parameters so that sensor fusion can be performed accurately. The proposed method is based around particle belief propagation (BP), a recent but highly efficient framework for tracking multiple targets. The proposed method also uses a hierarchical model which allows for multiple processes to be linked and interact with one another. We present a comprehensive set of simulations and results using differing, asynchronous sensor setups, and compare with a random finite set (RFS) approach, namely the sequential Monte Carlo (SMC)-probability hypothesis density (PHD) filter. The results show the proposed method is 17% more accurate than the RFS approach on average.

This article contrives a fault-tolerant quantized control for flexible air-breathing hypersonic vehicles (FAHVs) with appointed-time tracking performances. At first, to online identify the lumped effect of actuator faults, flexible modes, parameter uncertainties as well as external disturbances, a hysteresis quantizer based neural estimator (HQNE) using finite precision state information is proposed, enabling a reduced communication load and computational time with a competitive estimation capability. Utilizing the estimation of HQNE, fault-tolerant quantized control laws equipped with auxiliary systems are established for FAHVs to realize a stable reference tracking result using discrete-time control signals, where auxiliary systems are employed to automatically monitor the impact of input saturation for command regulation. Furthermore, an appointed-time prescribed performance control based on a hyperbolic cosecant function is developed to make the tracking errors of velocity and altitude reach to the pregiven residual sets with a prescribed time in the absence of exact initial system states. The presented controller achieves a preassigned-time tracking performance for FAHVs under actuator faults and input saturation maintaining a decreased communication burden. The ultimately uniformly bounded stability of closed-loop system is proved through Lyapunov stability analysis, while numerical simulations are designed to verify the effectiveness of presented controller.

Past decades technological breakthroughs have made software-defined radio (SDR) a viable alternative to analog architectures for many applications. However, projectile tracking is traditionally performed using dedicated architectures, not taking advantage of SDR reconfigurability. This article aims at developing a projectile tracking ground station using low cost commercial SDR. The principles, technical constraints, and experimental setup are detailed, and measurement results are presented for projectiles fired at Mach 0.8.

This article presents a new approach, named extension-deformation approach (EDA), to extended object tracking (EOT). It models the extension of an object as being deformed from a simple reference extension by moving several control points. Then, a complex object extension can be characterized by these control points given the reference extension. This greatly simplifies the problem of shape description. Thus, modeling and estimation of the extension reduce to those of the control points, which are treated as state components of the extension. A noiseless measurement is a point on the deformed extension that corresponds to a source on the reference extension. Once the source is given, the corresponding measurement model is linear in the state, making state estimation simple and direct. However, the source is unknown and hard to estimate accurately. To handle this problem, a multiple-source approach to kinematic state and extension estimation is proposed. Overall the proposed approach is simple and it can effectively track an object with an extension that can be nonstar-convex. Simulation results are given to illustrate the effectiveness of the proposed approach.

In a sensor network compensation of the correlated information caused by previous communication is of utmost interest for distributed estimation. In this article, we investigate an information decorrelation approach that can be applied when using interacting multiple model filters in the sensor nodes for a family of jump Markov linear systems. Implementation issues that might arise while applying the decorrelation approach are addressed in detail. The investigated approach is compared with alternatives on simple distributed single maneuvering target tracking examples.

International audience; In this paper, we propose a novel zoning-based tracking technique that combines the sensors' mobility with a WiFi-based observation model in the belief functions framework to track the sensors in real time. The next possible destinations of the sensors are predicted, leading to a mobility model. The belief functions framework is used to propagate the previous step evidence till the current one. The mobility of the sensors, along with information from the network, are used to obtain an accurate estimation of their position. The contributions of this paper are two-fold. Firstly, it proposes new mobility models based on the transition between zones and hidden Markov models, to generate evidence on the zones of the sensors without the use of inertial measurement units. Secondly, it explores the fusion of evidence generated by the mobility models on one hand, and the observation model on the other hand. The efficiency of the proposed method is demonstrated through experiments conducted on real data in two experimental scenarios.

Tracking space noncooperative targets, including disabled and mobile spacecrafts, remains a challenging problem. This article develops two reinforcement-learning-based parameter-self-tuning controllers for the following two different tracking cases: case A, tracking a disabled target, and case B, tracking a mobile target. An adaptive controller consisting of five model uncertainties is adopted for case A, and a modified PD controller is derived for case B. The actor–critic framework is employed to reduce the initial control accelerations for case A and to improve the terminal tracking accuracy for case B. Relations between control parameters and tracking errors are found through the fuzzy inference system. Finally, the reinforcement learning is used to select suitable control parameters for achieving desired purposes. Numerical experimental results validate the effectiveness of the proposed algorithms on reducing initial control accelerations for case A and improving the terminal tracking accuracy for case B.

In some target tracking scenarios, tracking performance can be improved by the incorporation of a constraint imposed by a preset trajectory. When the complete information about the trajectory is known, the constraint can be formulated in an explicit way. However, in practical applications, only partial information about the trajectory may be available. This article deals with the modeling of implicit constraints, imposed by a circular trajectory, along with the constrained state estimation. First, constraint models for two kinds of representative implicit constraints, the destination constraint and the trajectory shape constraint, are proposed for various cases with different conditions of prior information. In the cases with destination constraints, the destination information and the trajectory shape information are assumed to be known a priori . For each case, two forms of constraint models are derived. One is a direct form model where the constraint relationships are used to eliminate the unknowns in the final model, whereas the other is a state augmentation form model, where the unknowns are augmented into the state vector being estimated along with the target state. In the cases with trajectory shape constraints, only the trajectory shape information is available. For each case, the state augmentation form models are formulated. Second, constrained state estimation methods are proposed by utilizing pseudomeasurement constructed based on the constraint models. Furthermore, an extension to the maneuvering target tracking by integrating the proposed constraint filters into an interacting multiple model estimator is also presented. Numerical simulations in two scenarios are provided to show the effectiveness of the proposed constraint models and state estimation methods.

For an airborne sensor, there is a pressing need to be able to detect/track submerged submarines, shipwrecks, sea mines, unexploded explosive ordnance, and buried drums during maritime surveillance. Traditional usage is the magnetic anomaly detection (MAD), where the small changes in the earth's magnetic field caused by the ferrous components of the targets are measured. The primary means of long-range detection and classification of targets are with passive and active acoustic sensors, and MAD is used for accurate final localization. MAD could also be used for land-based targets but this is not common. Knowing the relationship between the magnetic signature and the kinematic parameters, the tracking problem can be formulated under a Bayesian framework. In this article, multiple nonlinear filters are used for a real single surface-target tracking problem in maritime surveillance using an airborne total-field sensor. The posterior Cramer–Rao lower bound for MAD is derived. Given the total-field measurements, these filters can estimate the kinematic states as well as the permanent moments and induced moments effectively. Results demonstrate the effectiveness of the proposed nonlinear filters as well as the impact of using MAD as part of airborne surveillance.

This article presents a generalized multifrequency carrier tracking architecture applicable to multifrequency global navigation satellite system receivers operating in frequency-selective fading environments. This architecture unifies a conventional single-frequency-independent tracking (ST) mode, which only operates on measurements from a single carrier, a multifrequency joint tracking (JT) mode, which linearly combines measurements from multiple carriers, and a multifrequency optimal tracking (OT) mode, which incorporates signal intensity in multicarrier combinations via an aggregate Kalman filter. The analytical closed-form expressions of the tracking error variance for the ST, JT, and OT modes are derived for theoretical analysis and comparison. Their tracking performances are evaluated and quantified using a simulated triple-frequency GPS signal with varying combinations of signal strengths. To have a fair assessment in real-world scenarios, real data collected during strong ionospheric, tropospheric scintillations, and multipath reflections and scattering are also used for performance evaluations in ST, JT, and OT modes.

The purpose of this article is threefold. We first show that the second-order post-Newtonian (p-N) equations for the Earth exterior Schwarzschild field introduced in this article are the equations that, unlike those of the first p-N order, allow the space-based pointing, acquisition, and tracking laser systems to substantially reduce the size of the p-N corrections to the Newtonian location from the systems of middle size targets in orbit about the Earth. To achieve this goal the trackers must be endowed with very narrow laser beams and atomic clocks. Second, we show that these corrections can be used to speed up the trackers’ shooting systems in any scenario where this kind of beam may be involved. In fact, since the first step in any procedure aimed to achieve this goal is to accurately determine the ranging and shooting directions to the targets from the lines of sight, the computation of the second p-N relative orbits of the targets with respect to the trackers must be carried out in real time. Then, the solution of these equations for the target of interest can be uploaded in the closed-loop control system on board the tracker involved, in order to initiate the control process of the respective ranging and shooting direction, as well as to reinitiate it, as soon as the target ceases to be hidden for the tracker due to the Earth. Third, we to show that these equations will be very useful for autonomous trackers to carry out two demanding tasks, namely 1) to steadily maintain intersatellite optical links and 2) to perform laser ablation of middle size LEO debris objects at operative distances.

Tracked targets often exhibit common behaviors due to influences from the surrounding environment, such as wind or obstacles, which are usually modeled as noise. Here, these influences are modeled using sparse Gaussian processes that are learned online together with the state inference using an extended Kalman filter. The method can also be applied to time-varying influences and identify simple dynamic systems. The method is evaluated with promising results in a simulation and a real-world application. Funding Agencies|Vinnova Industry Excellence Center LINK-SICVinnova; European Union Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie GrantEuropean Union (EU) [642153]; Research Council of Norway through the Centres of Excellence funding scheme [223254-NTNU-AMOS]; CENIIT program at Linkoping University [17:12]; FRAM Centre, Tromso, Norway, through the Project "Ground-based radar measurements of sea-ice, icebergs, and growlers"

As radar signals propagate above the ocean surface to determine the trajectory of a target, the signals that are reflected directly from the target arrive at the receiver along with indirect signals reflected from the ocean surface. These unwanted signals must be properly filtered; otherwise, their interference may mislead the signal receiver and significantly degrade the tracking performance of the radar. To this end, we propose a low-elevation target tracking mechanism considering the specular and diffuse reflection effects of multipath propagation over the ocean surface simultaneously. The proposed mechanism consists of a state-space model and a particle filtering algorithm and promises considerable improvements in the capacity and accuracy of the radar tracking systems. The efficiency and accuracy of the developed target tracking method are tested and compared with an unscented Kalman filtering method in two- and three-dimensional space using a series of simulation experiments.

This article considers the general problem of tracking a noncooperative target in the presence of specular multipath. We revisit a novel posterior Cramer–Rao bound (PCRB) methodology for this problem [1] and develop a robust interacting multiple model (IMM) extended Kalman filter (EKF) implementation. Both the PCRB and the IMM-EKF augment the target state to include the multipath parameters. The approaches are demonstrated via a simulated scenario, in which an airborne radar tracks a low-altitude airborne target. It is shown that the PCRB increases rapidly subsequent to the multipath effect occurring, indicating that tracking performance is significantly degraded at such times. Furthermore, it is shown that the IMM-EKF offers near-optimal performance, with target state estimation errors consistently within 15% of the PCRB. The IMM-EKF also consistently provides credible estimates. It is concluded that the PCRB methodology and the IMM-EKF tracker provide powerful tools for performance assessment and target state estimation in challenging multipath scenarios.

The problem of an optimal attitude control is studied for a tethered system under a constant thrust in noncoplanar orbital transfer. The contributions of this article mainly include two aspects: first, a noncoplanar orbital transfer scheme and second, a closed-loop optimal control method. Based on a constant thrust, a noncoplanar orbital transfer scheme between two sun-synchronous orbits is proposed, enabling convenient engineering implementation. Compared with existing closed-loop control methods, the hierarchical finite-time attitude tracking control method is presented in the last cycle of the closed-loop optimal control to reduce the error at the final moment. A numerical case of a tethered system in noncoplanar orbital transfer is studied to demonstrate the effectiveness of the proposed control scheme.

The fast and robust detection of far targets is one of the key techniques in infrared searching and tracking applications. Using multiorder directional derivatives, an effective and concise detection method performed on a single IR image is proposed in this article. First, multiorder directional derivatives of an image are derived from the facet model. According to the derivative characteristics of small targets, enhancement filters are designed to maximize the target components. Convolved with these filters, the small targets are enhanced and the backgrounds are suppressed in each derivative subband. The final result is obtained by fusing all these filtered derivative subbands. The experimental results demonstrate that the proposed method consistently achieves a robust and effective performance on all datasets. In addition, the low computational complexity makes the proposed method suitable for real time detection purpose.

The relative pose (position and attitude) measurement of space noncooperative targets is very important for on-orbit servicing activities, such as target tracking, approaching, and capturing. The traditional methods rarely consider the instability of feature extraction and image blurring caused by target tumbling. In this paper, a method based on the maximum outer contour (MOC) recognition is proposed to measure the pose of the target. Different feature extraction algorithms can simultaneously achieve close- and long-range measurement tasks. First, the trailing image is restored by the image enhancement method. Second, the “rough + fine” combination recognition method is used for contour extraction and connected component labeling of the restored image, and the target feature extraction time is reduced to one-third of traditional methods. Furthermore, the elliptical surface on the MOC is fitted by the least squares method (LSM), and the ellipse parameters (i.e., the center position, the long- and short-axes, and the deflection angle) are extracted. The accuracy of the target recognition is improved. Third, for the close-range measurement, based on the detected ellipse parameter, the pose of the noncooperative target is solved by the binocular imaging algorithm of the space circle; for the long-range measurement, the contour centroid of the target is calculated by the detected MOC, and the position of the target is solved by the LSM. Moreover, the effectiveness of the method is verified by the OpenSceneGraph numerical simulation system. Finally, an experimental system consisting of a binocular camera, a UR5 manipulator, and a satellite mockup was built. The experimental results verified the proposed method.

This paper presents a nonlinear tracking controller including the cutoff frequency adaptation algorithm for quadcopters without the use of true plant parameters. This study makes two contributions. First, an adaptation algorithm automatically updates the closed-loop cutoff frequency by updating the feedback gains. Second, a nonlinear control law renders the closed-loop system to guarantee the exponential performance recovery and offset-free properties, while only using the proportional component of tracking errors. The numerical verifications are performed to verify the closed-loop performance improvement from the proposed controller with MATLAB/Simulink.

Feature-aided tracking can often yield improved tracking performance over the standard multiple target tracking (MTT) algorithms. However, in many applications, the feature signal of the targets consists of sparse Fourier-domain signals. It changes quickly and nonlinearly in the time domain, and the feature measurements are corrupted by missed detections and misassociations. In this paper, we develop a feature-aided multiple hypothesis tracker for joint MTT and feature extraction in dense target environments. We use the atomic norm constraint to formulate the sparsity of feature signal and use the $\ell _1$ -norm to formulate the sparsity of the corruption induced by misassociations. Based on the sparse representation, the feature signal are estimated by solving a semidefinite program. With the estimated feature signal, refiltering is performed to estimate the kinematic states of the targets, where the association makes use of both kinematic and feature information. Simulation results are presented to illustrate the performance of the proposed algorithm.

In skywave over-the-horizon radar, the ionosphere often supports multiple propagation paths, and a single target can appear as a family of returns. This paper presents two new tracking approaches for this environment: The first uses measurement fusion, and the second uses group-target track-before-detect algorithm. In contrast to previous studies, we find that the biggest benefit is not improved detection sensitivity but rather associating the whole pattern of multipath returns significantly improves the ability to follow maneuvers.

A labeled multi-Bernoulli (LMB) filter is presented to perform multitarget tracking (MTT) for the glint noise. The measurement noise is modeled as a multivariate Student- $t$ process. The variational Bayesian method is applied in the LMB framework with the augmented state. The predictive likelihood is calculated via minimizing the Kullback–Leibler divergence by the variational lower bound. Simulation results show that our approach is effective in MTT with the glint noise.

Tail-sitters can fly as rotary-wing or fixed-wing aircraft and the transition between the two flight modes is critical. This paper develops a robust nonlinear control method to achieve the trajectory tracking control in flight mode transitions. The coordinate system does not need to be switched neither the controller structures or parameters. Theoretical analysis and simulation studies are conducted to verify the effectiveness of the developed control approach.

In this paper, we study the problem of mobile target tracking using the fewest number of mobile trackers for two different types of targets. Given the target trajectories and the period of observation, we propose techniques to compute the minimum number of trackers and their trajectories required to track all mobile targets. Two classes of mobile targets are considered in this paper: 1) targets that need tracking for the entire duration of observation and 2) targets that need tracking at least once during the period of observation. We show that even when target trajectories are known in advance, the problem is computationally hard, i.e., NP-complete. We formulate the problem as a network flow problem and propose algorithms for its solution. We evaluate the performance of our algorithms through simulation and study the impact of parameters such as the speed and sensing range of the trackers.

Reference trajectories, for the longitudinal states of a fixed-wing unmanned aerial vehicle subjected to asymmetric control constraints, are distinguished using the characteristics of regions of asymptotic stability calculated as ellipsoidal domains by solving linear matrix inequalities. These domains are used to determine the maximum allowable deviations from the reference, for which stable closed-loop tracking can be ensured. Constraints are imposed on a linear state feedback controller with the saturation modeled as a sector nonlinearity.

This paper considers the fixed-time translational operation control issue for the spacecraft rendezvous task with a free-tumbling target. The translation dynamics model of the rendezvous mission under actuator misalignments is first established in the line-of-sight coordinate frame. Then, a novel time-varying sliding mode surface is constructed and a control scheme with the sliding manifold is proposed to achieve the fixed-time convergence of relative parameters’ tracking errors. It is interesting that the preceding convergence time can be altered explicitly according to the specific rendezvous requirements. Meanwhile, a modified updating strategy is developed to ensure system's fixed-time convergence property in the presence of lumped uncertainties (external disturbance and actuator misalignments). The associated stability proof is constructive and accomplished by the development of a Lyapunov function candidate. Finally, numerical simulations on rendezvous missions are performed to demonstrate the effectiveness and the superiorities of the designed fixed-time control scheme.

This paper considers the problem of attitude tracking control for uncertain rigid spacecraft subject to control input magnitude and rate saturation (MRS). First, a smooth input MRS model is proposed. Then, a robust attitude tracking control scheme is designed based on the backstepping and finite-time disturbance observer techniques. Finally, the effectiveness of the control scheme derived here is illustrated by numerical simulations.

This paper investigates the fixed-time attitude tracking control problem for rigid spacecraft with input quantization and external disturbances. A novel fixed-time disturbance observer is designed to estimate unknown disturbances. By using adding a power integrator technique, a fixed-time controller is constructed, which can guarantee system states converge to a neighborhood of origin in fixed time. The parameter of quantizer can be coarsely chosen. Finally, simulation results are employed to demonstrate the effectiveness of the developed control scheme.

In many cases, target tracking is subjected to a dense, nonuniform, and time-varying clutter background. This will seriously deteriorate the tracking performance under an unknown clutter environment. In this paper, multitarget tracking under the unknown environment is considered. First, the finite mixture distribution is used to fit the unknown clutter distributions and then Gibbs sampling and Bayesian information criterion are adopted to evaluate and estimate the clutter parameters. Besides, the unknown detection profile and clutter rate are also considered. All these issues are solved in the random finite set framework because it is a principled and top-down approach. Finally, several experiments are provided to verify the proposed algorithm.

In the field of target tracking, a tremendous amount of work has been performed on improving the ability of many different algorithms to detect and track a target in the presence of clutter and other interfering targets. However, to date, surprisingly little work has been performed on analyzing whether or not, for a given target in a given clutter/interfering target environment, it is even possible to track the target at all. Our recent work has started to explore this—we have developed a framework to assess “trackability”—that is, we assess if a given target can be statistically separated from the clutter in the given environment. Here, we expand this framework by adding a second target. Assuming that both the targets are individually trackable, we develop a mechanism for establishing how close the two targets can get to one another and still be resolvable as two distinct targets. Note that we answer the question from the perspective of the tracker: a resolution of measurements is a different (and simpler) matter to adjudicate. Generally resolution of measurements implies resolvability of targets, but the converse is not implied.