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This paper investigates the problem of a divisible nonlinear load distribution on homogeneous complete b-ary tree networks. Classic models of nonlinear computational loads omit several steps in processing the load and yield only an approximate distribution for fractional loads. This paper considers a new model of nonlinear computational loads that includes all load processing steps and yields a practical solution to distribute fractional loads. Two algorithms to distribute a divisible nonlinear load on homogeneous complete b-ary tree networks are proposed. Closed-form expressions for the parallel processing time and speed-up for complete b-ary trees are also derived. This paper demonstrates that the asymptotic speed-up of the proposed algorithms is the number of processors in a multicomputer system. The proposed algorithms improved the classic algorithm in terms of speed-up. [ABSTRACT FROM AUTHOR]

This paper presents an efficient method for estimating the probability of conflict between air traffic within a block of airspace. Autonomous sense-and-avoid is an essential safety feature to enable unmanned air systems to operate alongside other (manned or unmanned) air traffic. The ability to estimate the probability of conflict between traffic is an essential part of sense-and-avoid. Such probabilities are typically very low. Evaluating low probabilities using naive direct Monte Carlo generates a significant computational load. This paper applies a technique called subset simulation. The small failure probabilities are computed as a product of larger conditional failure probabilities, reducing the computational load while improving the accuracy of the probability estimates. The reduction in the number of samples required can be one or more orders of magnitude. The utility of the approach is demonstrated by modeling a series of conflicting and potentially conflicting scenarios based on the standard Rules of the Air. [ABSTRACT FROM AUTHOR]

Automatic target recognition (ATR) is an important part for many computer vision applications. Despite the extensive research which has been carried out in this area for many years, there is no ATR system which performs well on all applications. Recently, different object recognition frameworks have been proposed which yield a high performance in baseline databases. However, our experiments showed that they can fail in real-world scenarios, when dealing with a limited number of data samples. In this paper, we propose a new ATR system, based on deep convolutional neural network (DCNN), to detect the targets in forward looking infrared (FLIR) scenes and recognize their classes. In our proposed ATR framework, a fully convolutional network is trained to map the input FLIR imagery data to a fixed stride correspondingly-sized target score map. The potential targets are identified by applying a threshold on the target score map. Finally, the corresponding regions centered at these target points are fed to a DCNN to classify them into different target types while at the same time rejecting the false alarms. The proposed architecture achieves a significantly better performance in comparison with that of the state-of-the-art methods on two large FLIR image databases. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of reliability and makespan optimization of hardware task graphs in reconfigurable platforms by applying fault tolerance (FT) techniques to the running tasks based on the exploration of the Pareto set of solutions. In the presented solution, in contrast to the existing approaches in the literature, task graph scheduling, tasks parallelism, reconfiguration delay, and FT requirements are taken into account altogether. This paper first presents a model for hardware task graphs, task prefetch and scheduling, reconfigurable computer, and a fault model for reliability. Then, a mathematical model of an integer nonlinear multi-objective optimization problem is presented for improving the FT of hardware task graphs, scheduled in partially reconfigurable platforms. Experimental results show the positive impacts of choosing the FT techniques selected by the proposed solution, which is named Pareto-based. Thus, in comparison to nonfault-tolerant designs or other state-of-the-art FT approaches, without increasing makespan, about 850% mean time to failure (MTTF) improvement is achieved and, without degrading reliability, makespan is improved by 25%. In addition, experiments in fault-varying environments have demonstrated that the presented approach outperforms the existing state-of-the-art adaptive FT techniques in terms of both MTTF and makespan. [ABSTRACT FROM PUBLISHER]

Provisioning of Internet of Things/machine-to-machine (M2M) services over satellite has been experiencing a continuous growth in the last few years, which is expected to further increase in the near future so as to meet the demands of users and enterprises. The design of a suitable network architecture is, hence, of paramount importance to properly take into account the requirements imposed by the technology available nowadays and to properly consider the interaction of the so-defined physical layer with transport and application layers. In this light, this paper analyzes the use case of TCP-based M2M services operating over DVB-RCS2 satellite links, where a contention resolution diversity slotted ALOHA access scheme is applied. The main goal of this paper is to provide a thorough understanding of the interactions of TCP and random access schemes, recognized as key elements to enable efficient M2M services over satellite. In this regard, this paper also develops a novel TCP throughput model, which has been validated through extensive simulation campaigns, proving the value of the proposed theoretical framework and its applicability to study the performance of M2M services in more general satellite scenarios. [ABSTRACT FROM AUTHOR]

This paper presents a Poisson multi-Bernoulli mixture (PMBM) conjugate prior for multiple extended object filtering. A Poisson point process is used to describe the existence of yet undetected targets, while a multi-Bernoulli mixture describes the distribution of the targets that have been detected. The prediction and update equations are presented for the standard transition density and measurement likelihood. Both the prediction and the update preserve the PMBM form of the density, and in this sense, the PMBM density is a conjugate prior. However, the unknown data associations lead to an intractably large number of terms in the PMBM density, and approximations are necessary for tractability. A gamma Gaussian inverse Wishart implementation is presented, along with methods to handle the data association problem. A simulation study shows that the extended target PMBM filter performs well in comparison to the extended target $\delta$ -generalized labelled multi-Bernoulli and LMB filters. An experiment with Lidar data illustrates the benefit of tracking both detected and undetected targets. [ABSTRACT FROM AUTHOR]

This paper describes a formal language for describing intent of quadrotor aircraft. The aim of this language is to support the prediction of quadrotor trajectories modeling guidance instructions in a compact and univocal manner, enabling to efficiently share trajectory information by several actors. A trajectory computation engine, translating from sentences of this language into a common trajectory is also described. Its use is demonstrated for a complex flight and in real-flight operations. [ABSTRACT FROM AUTHOR]

A general expression for binary integration for Swerling target fluctuations is developed in terms of the regularized incomplete beta function, which is accurately and efficiently calculated by built-in functions in software packages. From the general expression, optimal thresholds for binary integration are revisited. [ABSTRACT FROM AUTHOR]

This paper proposes an optimal control framework for the climb and descent economy modes of a flight management system (FMS) yielding a solution that can be implemented in real-time flights below the drag divergence Mach number. The problem is formulated as the optimization of a functional that trades off the fuel- and time-related costs of a flight as a function of a (crew-supplied) parameter called the cost index. The work builds on previous research of the authors for the cruise phase and extends it to the climb and descent phases of flight. More specifically, for both climb and descent, it is found that suboptimal solutions can be obtained as the positive real roots of a fifth-degree polynomial lying inside the flight envelope, which can be found using fast-converging algorithms such as Newton's method. The main contributions of this work are threefold. First, the proposed method gives physical insight because there is an analytical expression for each coefficient of the polynomial. Second, this approach eliminates the need to have a performance database in the system, thus making its implementation faster in real-time. Third, the solution exhibits the same behavior of airborne FMS units as a function of the cost index, which is justified in this paper based on Bellman's principle of optimality. This justification is an important theoretical contribution of the paper. A validation of the approximate solution is obtained using the shooting method to compute the optimal trajectories and compare them against the proposed suboptimal solution. Simulation results show that, for an Airbus A320 model and for a Gulfstream-IV aircraft model, the relative error of the suboptimal trajectories when compared to the optimal trajectories is small for climb and descent trajectories, respectively. [ABSTRACT FROM AUTHOR]

This paper deals with the swath acquisition planning problem for multisatellite Earth observation missions. Given a set of satellites and a mission time frame, the problem we solve consists of selecting a set of acquisitions from the satellites in order to cover a given region of interest during the requested time frame, optimizing a certain objective function.We show that the planning problem can be modeled as a set covering problem, using basic tools of mathematical programming. The formulation of the model requires the solution of a complex computational geometry problem, and therefore the use of heuristics and metaheuristics applies. In this paper, we discuss the efficiency of the constructive phase of a greedy randomized adaptive search procedure algorithm. Computational results comparing the heuristic algorithms with the exact approach are presented. [ABSTRACT FROM PUBLISHER]

In this paper, multiple sensor measurement update is studied for a random matrix model. Four different updates are presented and evaluated: three updates based on parametric approximations of the extended target state probability density function and one update based on a Rao–Blackwellized (RB) particle approximation of the state density. An extensive simulation study shows that the RB particle approach shows best performance, at the price of higher computational cost, compared to parametric approximations. [ABSTRACT FROM PUBLISHER]

This paper deals with a new modeling method and a powerful planning approach for deep space probes. The key technique is based on the concept of extended state model. Furthermore, extended state model planning (ESMP) algorithm is designed to be fully compliant with the model, and flaw selection strategies are proposed to avoid redundant work. Finally, we discuss the time complexity of ESMP, and computational results comparing ESMP with the algorithm in Europa are presented. [ABSTRACT FROM AUTHOR]

This paper presents a model-driven multiobjective avionics system optimization method developed over six years at its final destination, the application in real-world integrated modular avionics systems. It documents the methods, the application scenario, and the results of large-scale optimization studies carried out for the Airbus A350 peripheral acquisition system. The results predict that a significantly lighter system could be possible. [ABSTRACT FROM AUTHOR]

This paper introduces a reduced state signal model in which interfering signals are suppressed with the help of beamforming. Based on our model, a particle filtering algorithm integrated with beamformer for satellite navigation signal tracking is proposed to increase the computational efficiency and the robustness to the signal model mismatch from unknown interference. Through extensive simulations and flop complexity analysis, we verify the superiority of the proposed algorithm against existing Bayesian filtering algorithms without beamforming. [ABSTRACT FROM AUTHOR]

Estimation for discrete-time stochastic systems with parameters varying in a continuous space is considered in this paper. Justified by an analysis of model approximation, a novel approach, called hybrid grid multiple model (HGMM), is proposed for state estimation. The model set used by HGMM is a combination of a fixed coarse grid and an adaptive fine grid to cover the mode space with a relatively small number of models. Next, two fundamental problems of the HGMM approach?model-set sequence-conditioned estimation and design of adaptive fine models?are addressed. Then, based on two model-set designs by moment matching, HGMM estimation algorithms are presented. Finally, performance of the developed HGMM estimation algorithms is evaluated on benchmark tracking scenarios, and simulation results demonstrate their superiority to the state-of-the-art MM estimation algorithms in terms of accuracy and computational complexity. [ABSTRACT FROM AUTHOR]

Asynchronous multisensor systems have been widely equipped on various host platforms to meet the requirements of modern navigation campaigns. However, the existing asynchronous fusion algorithms are not efficient in many challenged environments, due to the limitations of communication condition, out-of-sequence observation, and computation affordance. To solve these problems, this paper proposes a batch state asynchronous fusion algorithm with feedback for distributed architecture systems. It is optimal in the sense of minimum mean-squared estimation and its fusion solution is rigorously derived by fully considering the correlations of sequential/cross channels and one-step prediction information. The final fusion solution is straightforward expressed with the combinations of multiple local Kalman filtering estimates outputs during one fusion period. Moreover, no additional interpolation, extrapolation or synchronization procedures are required. Furthermore, to alleviate the computational burden of our proposed algorithm, its approximation form is also given by replacing the weighting matrix by the weighting scalar. Simulations are carried out to illustrate the efficiency and the performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper studies the clearance of flight control law for a hypersonic gliding vehicle (HGV) and proposes two linear clearance criteria based on structural singular value (μ) theory. The first criterion is the multiinput-multioutput (MIMO) stability margin, which is defined independently of an exclusive region depicted in Nichols diagrams. Through introducing a measure matrix, the classic phase/magnitude margin can be expanded into an MIMO system, thereby becoming the robust stability criterion of the MIMO system with pure complex uncertainties. The second criterion is setup based on an analysis of the robustness, i.e., the stability under pure real parametric uncertainties. The study establishes a physical linear fractional representation (LFR) model of the HGV and then reduces it to an equivalent model of a lower order. Then a global optimization strategy is proposed to compute μ with real parametric uncertainties. The computational burden can be significantly reduced by model reduction and global optimization. The study evaluates an HGV as an example to demonstrate the validity and efficiency of the proposed methods. [ABSTRACT FROM PUBLISHER]

This paper presents a strategy for optimal estimation of parameters for towed cable systems using moving horizon estimation (MHE). The main contributions of the work include a novel formulation of MHE using a dead-band that explicitly rejects measurement noise, real-time implementation results, and the investigation of time-varying stochastic disturbances as well as unknown yet constant disturbances. Further analysis is conducted on the observability and sensitivity of key parameters to determine which parameters can be estimated by the proposed approach using real-time streaming data from experiments. In addition to the real-time results, an offline multiobjective optimization is conducted to reveal the tradeoff between the computational burden and estimation variation. A Pareto frontier is used to demonstrate a series of optimal experimental configurations obtained by adjusting the model complexity (number of cable links and horizon steps). Finally, a multivariate parameter estimation is performed to explore the applicability of the proposed approach in simultaneously estimating multiple parameters. [ABSTRACT FROM AUTHOR]

This paper presents a systematical framework to solve the multiple unmanned aerial vehicles (multi-UAV) cooperative task assignment problem. Based on a combinatorial optimization model, it is solved by a digraph-based method and a novel meta-heuristic optimization method, named modified two-part wolf pack search (MTWPS) algorithm. When the number of UAVs/targets is large, in order to reduce the simulation time, we also present a new solution framework based on an easy-computing objective function. Additionally, the parameter and time-sensitive uncertainties are considered in the extended task assignment problem. For the problem with parameter uncertainty, it is formulated by a robust optimization method and solved by a novel combined algorithm, including the classical interior point method and our MTWPS algorithm. For the problem with time-sensitive uncertainty, it is solved by a practical online hierarchical planning algorithm. Finally, numerical simulations and physical experiments demonstrate that the proposed methods can provide a flyable solution for the UAVs and achieve outstanding performance in comparison with other algorithms. [ABSTRACT FROM AUTHOR]

Real-time computing systems are increasingly used in aerospace and avionic industries. In the face of power challenge, performance requirements and demands for higher flexibility, hardware designers are directed toward reconfigurable computing using field programmable gate arrays (FPGAs) that offer high computation rates per watt and adaptability to the application constraints. However, considering reconfigurable computing in the avionic design process leads to several challenges for system developers. Indeed, such technology should be validated along the verification & validation cycle starting with simulation tools, passing through the test benches and finishing with the integration phase. For each step, the FPGA can play an essential role to achieve better performances, more adaptive systems, and cost-effective solutions. In this paper, we present a seamless FPGA-centric design process for avionic equipments. Along this process, we redefine the role of the FPGA circuits to cover the simulation, the test, and the integration steps. First, reconfigurable logics are used in the frame of heterogeneous CPU/FPGA computing in order to obtain high speed-up for real-time avionic simulation. The proposed environment supports dynamic execution model enabling reconfiguration during runtime to avoid the timing constraint violation. Second, the FPGA is used as a key solution to offer versatile test benches and to converge toward unified test and simulation tools. We have designed several commercial input output intellectual property systems with dynamic runtime reconfiguration capabilities, in order to mitigate component obsolescence and to provide increased flexibility and decreased design time. Third, at the integration phase, we use the conventional tools to make profit from reconfigurable technology in embedded avionic applications in order to deliver high computation rates and to adapt their functioning mode to provide reliability, fault tolerance, deterministic timing guarantees, and energy efficiency. [ABSTRACT FROM AUTHOR]

With the ever-increasing demand in the analysis and understanding of aerial images in order to remotely recognize targets, this paper introduces a robust system for the detection and localization of cars in images captured by air vehicles and satellites. The system adopts a sliding-window approach. It compromises a window-evaluation and a window-classification subsystems. The performance of the proposed framework was evaluated on the Vaihingen dataset. Results demonstrate its superiority to the state of the art. [ABSTRACT FROM AUTHOR]

Radar tracking of aircraft targets at low elevation angles can be complicated by clutter from wind turbines, which we define to be clutter. In this paper, we study detection in a staring pulse-Doppler radar. By exploiting the second-order correlation structure of this wind-turbine clutter we derive a target detector that uses a sequence of adaptive coherence scores. The detector uses a multipulse coherence statistic consisting of an incoherent geometric average of coherently computed adaptive coherence scores. The detector compares favorably to a multipulse coherence detector that uses no modeling or estimation of the clutter. [ABSTRACT FROM PUBLISHER]

Earth-orbiting satellites experience a partial or full eclipse when they pass through the night side of the Earth. These eclipse events can have significant impacts on the satellite due to the dramatic change of the solar radiation incident. It is therefore essential to propose a high-precision eclipse prediction algorithm or even a space-borne algorithm applied to the satellite to predict the eclipse events autonomously. This paper proposes two eclipse prediction algorithms based on an atypical eclipse condition. The first is the numerical variable-step algorithm whose computation efficiency is superior to the previous fixed-step algorithm due to adopting multiscale step sizes. The other is the autonomous space-borne algorithm, which, based on the analytical solution of the eclipse condition, predicts the penumbra and umbra. Both algorithms have been implemented to the Sun-synchronous orbit and the prediction results are compared with that of the Satellite Tool Kit software. It is indicated that the numerical variable-step algorithm is of high prediction accuracy, whereas the autonomous space-borne algorithm is more suitable for the on-board processor to predict the eclipse due to its reduced computational demands. [ABSTRACT FROM AUTHOR]

This paper presents a complete nonlinear parameter-dependent mathematical model, as well as a procedure for computing the quasi-linear parameter-varying (q-LPV) model of a class of spin-stabilized canard-controlled guided projectiles. The proposed projectile concept possesses a so-called dual-spin configuration: the forward section contains the necessary control and guidance software and hardware, whereas the aft roll-decoupled and rapidly spinning section contains the payload. Wind-axes instead of body-axes variables, as is the case in the existing literature, are preferred for the modeling of the highly coupled pitch/yaw airframe nonlinear dynamics, since they are better suited to control synthesis. A q-LPV model, approximating these nonlinear dynamics around the system equilibrium manifold and capturing their dependence on diverse varying flight parameters, is analytically obtained. In addition, a detailed stability analysis specific to this kind of weapons is performed throughout their large flight envelope using the aforementioned q-LPV model. Furthermore, a study is conducted in order to quantify the influence of reducing the dimension of the flight parameter vector on the exactness of the q-LPV model. Finally, the critical influence on the pitch/yaw-dynamics of the nose-embedded sensor position, and of uncertainty on the various static and dynamic aerodynamic coefficients as well as the aerodynamic angles, is shown. [ABSTRACT FROM AUTHOR]

The goal of this paper is to advance mathematical and computational methodology for orbiter-to-orbiter radio tomography of small solar system bodies. In this study, an advanced full waveform forward model is coupled with a total variation-based inversion technique.We use a satellite formation model in which a single unit receives a signal that is transmitted by one or more transponder satellites. Numerical results for a two-dimensional domain are presented. [ABSTRACT FROM AUTHOR]

In the paper, a novel auxiliary truncated particle filtering for bearings-only maneuvering target tracking (ATPF-BOT) is proposed. In the proposed algorithm, a modified prior probability density function (PDF) is derived to solve the modeling uncertainty problem, which can simultaneously incorporate current measurement information and target characteristic information. Meanwhile, the proposal distribution is jointly designed by using the prior PDF and the modified prior PDF. Moreover, the proposal distribution is approximately calculated based on adaptive least square method so as to apply the ATPF algorithm for bearings-only maneuvering target tracking, and a practical algorithm is also developed. The experiment results show that the proposed algorithm is computationally efficient and successfully implemented in bearings-only target tracking systems. [ABSTRACT FROM AUTHOR]

This paper uses a Kalman filter with retrospective cost?based input estimation (KF/RCIE) to track maneuvering targets with unknown acceleration. Unlike conventional tracking methods that model the acceleration as a random process, KF/RCIE views the unknown acceleration as a deterministic unknown signal. Retrospective cost optimization is then used to estimate the unknown acceleration. Numerical examples and laboratory experiments illustrate the effectiveness of this approach with comparison to conventional tracking methods. [ABSTRACT FROM AUTHOR]

In this paper, a fuzzy weighted active contour model for synthetic aperture radar image segmentation is proposed. A feature-based approach comprising weighted active contours and level set segmentation is used to improve segmentation results. A fuzzy inference engine is used to assign weights to pixels of the level set function based on local entropy and local variance. Experiment results show that the proposed scheme improves the segmentation efficiency and accuracy as compared with the existing region-scalable fitting scheme. [ABSTRACT FROM AUTHOR]

In this paper we present an approximate multi-Bernoulli filter and an approximate hybrid multi-Bernoulli cardinalized probability hypothesis density filter for superpositional sensors. The approximate-filter equations are derived by assuming that the predicted and posterior multitarget states have the same form and propagating the probability hypothesis density function for each independent component of the multitarget state. We examine the performance of the filters in a simulated acoustic sensor network and a radio frequency tomography application. [ABSTRACT FROM PUBLISHER]

A novel aircraft path?following guidance algorithm based on model predictive control is proposed in this paper. The algorithm tracks a precomputed trajectory and produces reference commands for a low-level attitude controller. To solve the associated nonlinear optimization problem, an iterative scheme is proposed, using as a feasible hotstart the guidance solution provided by a well-behaved L1 navigation law. Simulations show the effectiveness of the algorithm, even in the presence of disturbances such as wind. [ABSTRACT FROM PUBLISHER]

This paper introduces an original method for sensors localization in WSNs. Based on radio-location fingerprinting and machine learning, the method consists of defining a model whose inputs and outputs are, respectively, the received signal strength indicators and the sensors locations. To define this model, several kernel-based machine-learning techniques are investigated, such as the ridge regression, support vector regression, and vector-output regularized least squares. The performance of the method is illustrated using both simulated and real data. [ABSTRACT FROM AUTHOR]

Detection of cars has a high variety of civil and military applications, e.g., transportation control, traffic monitoring, and surveillance. It forms an important aspect in the deployment of autonomous unmanned aerial systems in rescue or surveillance missions. In this paper, we present a two-stage algorithm for detecting automobiles in aerial digital images. In the first stage, a feature-based detection is performed, based on local histogram of oriented gradients and support vector machine classification. Next, a generative statistical model is used to generate a ranking for each patch. The ranking can be used as a measure of confidence or a threshold to eliminate those patches that are least likely to be an automobile. We analyze the results obtained from three different types of data sets. In various experiments, we present the performance improvement of this approach compared to a discriminative-only approach; the false alarm rate is reduced by a factor of 7 with only a 10% drop in the recall rate. [ABSTRACT FROM AUTHOR]

This paper considers the problem of decentralized localization in wireless sensor networks (WSNs). The problem is set by combining both radio-fingerprints and accelerometer information. It is then resolved in a decentralized manner where the surveillance area is divided into several zones equipped with calculators. The proposed algorithm consists of computing first estimates using received signal strength indicator (RSSI)-fingerprints, then correcting them using accelerometer information leading to more accurate estimates. Computations are performed using the interval analysis and the Kalman filter in a simulated environment. [ABSTRACT FROM AUTHOR]

Aerospace radar operation requires robust target detection in the presence of strong ground clutter returns. Space-time adaptive processing (STAP) is a leading approach to cull weak target signals from a strong clutter background. A scene comprised of heterogeneous clutter degrades typical STAP implementation by corrupting training data, thereby leading to covariance estimation error, corresponding adaptive clutter filter mismatch, and loss of detection performance. In the knowledge-aided parametric covariance estimation (KAPE) method, the processor estimates the parameters of a validated clutter covariance model to tailor the clutter filter response to rapid changes in heterogeneous clutter environments. Computational burden and array manifold estimation, given antenna complex gain errors, are two primary concerns when implementing KAPE. This paper develops and characterizes an enhanced KAPE (E-KAPE) approach. Specifically, we develop a computationally efficient implementation via application of Gram-Schmidt orthonormalization to the modeled clutter manifold and discuss an iterative approach to estimate complex gain errors from channel-to-channel, leading to substantial improvement in model-based clutter cancellation potential. For the specific simulation results shown, we find a 9-dB improvement in signal-to-interference-plus-noise ratio loss for our proposed approach relative to the original KAPE method. Furthermore, for these cases, we show that our E-KAPE method outperforms conventional STAP algorithms in the presence of homogeneous and heterogeneous clutter. [ABSTRACT FROM AUTHOR]

In serial wireless sensor networks, nodes send their compact information (decisions) to a fusion center via multihop. Each node performs data fusion and transmits the result. We propose using the Plackett copula for resolving the correlation in heterogeneous serial networks. The proposed scheme improves detection performance with low computation burden. [ABSTRACT FROM AUTHOR]

Using the dynamic inversion philosophy, a nonlinear partial integrated guidance and control approach is presented in this paper for formation flying. It is based on the evolving philosophy of integrated guidance and control. However, it also retains the advantages of the conventional guidance then control philosophy by retaining the timescale separation between translational and rotational dynamics explicitly. Simulation studies demonstrate that the proposed technique is effective in bringing the vehicles into formation quickly and maintaining the formation. [ABSTRACT FROM AUTHOR]

This paper describes the hardware-in-the-loop (HIL) simulation methodology used for the verification of functional requirements of e-st @r-I CubeSat. The satellite's behavior is investigated via HIL simulation, and the results obtained are consistent with the expected values in any operative conditions. It is proven that HIL simulation is a valuable means for supporting the verification process of small satellites and may help reduce the time and cost of the development phase and increase mission reliability. [ABSTRACT FROM AUTHOR]

Radar observations of human activities have a variety of applications in security, defense, and rescue operations. Range-Doppler signatures of human motions are a useful tool for retrieving information on observed activities but require an understanding of the scattering processes involved to enable interpretation. This paper presents a study of human Doppler signatures using simulations, in particular focusing on the impact of the polarization to enable an understanding of any advantages in the use of polarimetric radar. The simulation model utilized is based on an approximate scattering approach combined with a 12-cylinder description of the human body. A comparison with single polarization co-pol measurements is used to show that the model provides reasonable first-order predictions of human signatures. Further simulations for polarimetric signatures illustrate the differing contributions of individual body parts to micro-Doppler returns and suggest that multi-polarization measurements can be useful in future micro-Doppler radar systems for human observation. [ABSTRACT FROM AUTHOR]

The stellar refraction navigation is a type of autonomous celestial navigation method for satellites that uses high-accuracy star sensors to obtain the stellar refraction. The apparent height and the stellar refraction angle are two kinds of measurements for the satellite stellar refraction navigation system. A better navigation performance can be achieved by the stellar refraction angle rather than the refraction apparent height. However, the measurement model of the stellar refraction angle is an implicit function, which needs an implicit filter method. A new implicit unscented Kalman filter (UKF) method is proposed and applied to the satellite stellar refraction navigation system in this paper. The main difference between the classical UKF and the implicit UKF lies in the measurement update. First, since the implicit measurement model can be rearranged into an equation whose one side is zero, the vector of zeros is regarded as the equivalent measurement, and the difference between the predicted measurement and its true value is utilized for updating the predicted state and its covariance matrix. Second, the measurement noise and its covariance matrix are transformed based on the unscented transformation principle. In addition, the implicit UKF is compared with the implicit extended Kalman filter (IEKF), the iterative IEKF, and the UKF. Simulations show that the proposed implicit UKF method can obtain about 19.0% and 17.5% improvements in three-dimensional root mean square position estimation compared to the IEKF and the iterative IEKF, respectively. Although the navigation performance of the UKF is close to the implicit UKFs, the computational time of the implicit UKF is much shorter than the UKFs. In addition, the sensitivities of the four different Kalman filters to the star sensor's faults and the atmosphere perturbation are compared, respectively. [ABSTRACT FROM PUBLISHER]

In this paper, a novel predictive smooth variable structure filter (PSVSF) is presented for attitude synchronization during satellite formation flying (SFF). The strategy is proposed based on variable structure control concept and applies to any nonlinear system with modeling errors. Because the modeling errors in the PSVSF are not restricted to Gaussian noise only, the technique can deal with various kinds of uncertainties, parameter variations, or noises. It also has low computational burden; therefore, it is more general than other nonlinear estimation methods. Sufficient conditions to analyze the stability and characteristics of the PSVSF are presented. Simulations are performed to demonstrate the accuracy, robustness, and stability of the proposed PSVSF for the attitude synchronization estimation of the SFF system. [ABSTRACT FROM PUBLISHER]

The inverse synthetic aperture radar (ISAR) imaging for nonuniformly rotating target has always been a challenging task due to the time-varying Doppler parameter, especially in the low signal-to-noise ratio (SNR) environment. In this paper, a novel ISAR imaging algorithm for nonuniformly rotating targets in the low SNR environment based on the parameter estimation approach is presented. First, the received signal in this work in a range bin is modeled as a multicomponent cubic phase signal (CPS) after motion compensation. Two approaches, namely coherently integrated modified cubic phase function (CIMCPF) and coherently integrated modified high-order ambiguous function (CIMHAF), are proposed to, respectively, estimate the second-order and the third-order coefficients in the CPS. Thanks to the coherent integrations developed in both CIMCPF and CIMHAF, they demonstrate excellent low SNR performance. Moreover, to efficiently implement the proposed approach, the nonuniform fast Fourier transform (NUFFT) is utilized in this work. Due to the usage of the NUFFT, the computational cost is reduced, and the search procedure also dispenses with the nonuniformly spaced signal. Finally, CIMCPF and CIMHAF are applied to produce ISAR image for a maneuvering target based on the CPS model. Several numerical examples, analyses of the noise tolerance performance for the proposed approaches, and ISAR imaging results demonstrate the superior performance of the proposed method. [ABSTRACT FROM PUBLISHER]

Nonlinear sparse data regression and generation have been a long-term challenge in the field of aerodynamics, flow field reconstruction is an area of specific interest in this article. The high computational costs of computational fluid dynamics (CFD) make large scale CFD data production expensive, which is the reason why cheaper methods are needed. Traditional reduced-order models were promising but they cannot generate a large amount of full domain flow field data (FFD) to execute high-precision flow field reconstructions. Motivated by the problem of existing approaches, and inspired by the success of generative adversarial networks (GANs) in the field of computer vision, we prove a theorem that shows the optimal approximation to a GAN discriminator is a radial basis function neural network when engaged in with nonlinear sparse FFD regression and generation. Based on this theorem, a radial basis function-based GAN (RBF-GAN) and a RBF cluster-based GAN (RBFC-GAN) are proposed for regression and generation purposes. Three different datasets are applied to verify the feasibility of our models. The results show that the performance of RBF-GAN and RBFC-GAN are better than that of GANs and conditional GANs (cGANs) by both the mean square error and the MSPE measurements. In addition, compared with GANs/cGANs, the stability of the RBF-GAN and the RBFC-GAN are improved by 34.62 and 72.31%, respectively. Consequently, our proposed models can be used to generate full domain FFD from limited and sparse datasets to meet the requirements of high-precision flow field reconstructions. [ABSTRACT FROM AUTHOR]

Recent missions reveal that rubble-pile asteroids usually have a wide distribution of bare rocks in various scales. The dynamical evolution of a lander interacting with such rough surfaces is still an open problem. This article investigates the influence of the explicitly refined rocky terrains on the natural landing motion of a cuboid asteroid lander. The methodology for constructing the local terrains with centimeter-sized to meter-sized rocks is developed based on manipulating the original polyhedron shape model. Local rocky terrains refined with different-sized rocks (20 cm, 40 cm, 80c m, and 1 m) are generated. Moreover, hypothetical local terrains in different numbers of 1-m-sized rocks are presented to assess their influence on the landing motion. Numerical simulations are performed to characterize the dynamical behavior of the lander in rocky terrains. The distributions of the first and second touchdown positions, the transfer time, and the locomotion distance are taken as the three key indicators for examining the motion difference in these rocky terrains. The mechanism for the landing motion is analyzed by considering the collision slope. The results are expected to provide guidelines for the lander deployment and the path planning of a rover on rocky asteroids. [ABSTRACT FROM AUTHOR]

It is important to predict the Mach number in a transonic wind tunnel system. According to the aerodynamic mechanism, the Mach number is indirectly calculated from the total pressure (TP) in the stilling chamber and the static pressure (SP) in the test section. The high-dimensional input features and large-scale data are the main difficulties to build the TP and the SP models. The fixed-size LS-SVM is a popular method to build a nonlinear model for a large-scale problem. However, it is difficult to further improve the sparsity in the high-dimensional input space. Based on the multivariate fuzzy Taylor theorem, the feature subsets ensemble (FSE) method is proposed to deal with the high-dimensional problem. The set of direct, exhaustive, independent feature-space subdivisions forms the basis to develop FSEs. In the FSE, submodels are learned using substantially low-dimensional data sets and characterized by low complexity. The TP and the SP are estimated with the FSE-based ensemble fixed-size least squares support vector machines (LS-SVMs). Experiments show that the FSEs speed up both training and testing time that would otherwise be infeasible for individual, bagging, and random subspace (RS). FSE models meet the requirements of the forecasting speed, the accuracy and the generalization of the Mach number prediction. [ABSTRACT FROM AUTHOR]

The interacting multiple model (IMM) estimator outperforms fixed model filters, e.g. the Kalman filter, in scenarios where the targets have periods of disparate behavior. Key to the good performance and low complexity is the mode mixing. Here we propose a systematic approach to mode mixing when the modes have states of different dimensions. The proposed approach is general and encompasses previously suggested solutions. Different mixing approaches are compared, and the proposed methodology is shown to perform very well. [ABSTRACT FROM PUBLISHER]

The Poisson multi-Bernoulli mixture (PMBM) is a multiobject conjugate prior for the closed-form Bayes random finite set filter. The extended object PMBM filter provides a closed-form solution for multiple extended object filtering with standard models. This article considers computationally lighter alternatives to the extended object PMBM filter by propagating a Poisson multi-Bernoulli (PMB) density through the filtering recursion. A new local hypothesis representation is presented, where each measurement creates a new Bernoulli component. This facilitates the developments of methods for efficiently approximating the PMBM posterior density after the update step as a PMB. Based on the new hypothesis representation, two approximation methods are presented: one is based on the track-oriented multi-Bernoulli (MB) approximation, and the other is based on the variational MB approximation via Kullback–Leibler divergence minimization. The performance of the proposed PMB filters with gamma Gaussian inverse-Wishart implementations are evaluated in a simulation study. [ABSTRACT FROM AUTHOR]

A sequential decision process in which an adaptive radar system repeatedly interacts with a finite-state target channel is studied. The radar is capable of passively sensing the spectrum at regular intervals, which provides side information for the waveform selection process. The radar transmitter uses the sequence of spectrum observations as well as feedback from a collocated receiver to select waveforms which accurately estimate target parameters. It is shown that the waveform selection problem can be effectively addressed using a linear contextual bandit formulation in a manner that is both computationally feasible and sample efficient. Stochastic and adversarial linear contextual bandit models are introduced, allowing the radar to achieve effective performance in broad classes of physical environments. Simulations in a radar-communication coexistence scenario, as well as in an adversarial radar-jammer scenario, demonstrate that the proposed formulation provides a substantial improvement in target detection performance when Thompson sampling and EXP3 algorithms are used to drive the waveform selection process. Further, it is shown that the harmful impacts of pulse-agile behavior on coherently processed radar data can be mitigated by adopting a time-varying constraint on the radar’s waveform catalog. [ABSTRACT FROM AUTHOR]

Theoretical tools are presented to analyze the relationship between the design choices for a class of NASA-inspired fault-tolerant, reconfigurable computer architectures and the tracking performance degradation of a digital flight control system implemented on such a platform while operating in a high-intensity radiated field (HIRF) environment. A HIRF experiment was conducted at the NASA Langley Research Center to validate the theory for a distributed Boeing 747 flight control system subject to HIRF upsets. [ABSTRACT FROM PUBLISHER]

Dynamic coupling is an inherent behavior of the space robot system, which seriously affects the motion accuracy of the system. This article focuses on how to guarantee the base attitude stability of semifloating space robots, from a control perspective—namely, the base attitude decoupling control. The quaternion representation of the system dynamics is derived to avoid the representation singularity of the Euler angles. In consideration of the complicated system dynamics and the limited computing ability of the on-board computer, an efficient decoupling controller is designed based on the time-delay estimation (TDE) and the supertwisting control (STC). Herein, for the proposed TDE-based STC (TDE-STC) scheme, TDE is used to decouple and linearize the nonlinear dynamics, and STC is a second-order sliding mode control algorithm which can compensate for the TDE error and drive the state variables to converge to the equilibrium point robustly in finite time. The model-free decoupling principle of TDE is clearly illustrated by comparison with the classical computed torque control. The global asymptotical stability analysis of the closed-loop system is proven using the Lyapunov theory and the linear matrix inequality theorem. Finally, several comparative simulation studies on a 3-D space robot system with consideration of joint friction, disturbances, and model uncertainties are conducted to verify the effectiveness of the proposed TDE-STC scheme. The corresponding results show that the TDE-STC scheme can achieve a high-accuracy attitude decoupling performance with both the model-free merits and the chatter-free merits. [ABSTRACT FROM AUTHOR]

Crowd management is an essential task to ensure the safety and smoothness of any event. Using novel technologies, including surveillance cameras, drones, and the communication techniques between security agents, the control of the crowd has become easier. Yet, the usage of such techniques is still not effective. This article presents an approach for crowd counting from drones' data. The proposed method exploits the dilated and scaled neural networks for feature extraction and density crowd estimation. A new dataset named ViseDrone2020 is used for training and testing of the proposed method. In order to compare the proposed method, we implemented 10 state-of-the-art methods and trained it on this dataset. The experiments show that the proposed model is more efficient for crowd counting compared with the implemented methods. Also, some of these methods give relatively accurate results in terms of estimated crowd numbers as well as the quality of estimated density maps. The proposed model was further evaluated on nondrone datasets, namely, UCF_QNRF, UCF_CC_50, and shanghaiTech_(A, B), which produced satisfying results for all the datasets. In addition, the proposed method was tested on noisy images where Gaussian noise and salt-and-pepper noise was applied to all the images of the dataset with a noise density of 0.02. The analysis showed that the quality of the density map as well as the quantity of the crowd count estimation is comparatively better than other existing methods without the presence of noise. After the acceptance, the code of the proposed method as well as 10 implemented methods will be available at: [Online]. Available: https://github.com/elharroussomar/Crowd-Conting-on-Drone-Data. [ABSTRACT FROM AUTHOR]