Showing papers in "IEEE Transactions on Aerospace and Electronic Systems in 2018"

Deep convolutional autoencoder for radar-based classification of similar aided and unaided human activities

Abstract: Radar-based activity recognition is a problem that has been of great interest due to applications such as border control and security, pedestrian identification for automotive safety, and remote health monitoring. This paper seeks to show the efficacy of micro-Doppler analysis to distinguish even those gaits whose micro-Doppler signatures are not visually distinguishable. Moreover, a three-layer, deep convolutional autoencoder (CAE) is proposed, which utilizes unsupervised pretraining to initialize the weights in the subsequent convolutional layers. This architecture is shown to be more effective than other deep learning architectures, such as convolutional neural networks and autoencoders, as well as conventional classifiers employing predefined features, such as support vector machines (SVM), random forest, and extreme gradient boosting. Results show the performance of the proposed deep CAE yields a correct classification rate of 94.2% for micro-Doppler signatures of 12 different human activities measured indoors using a 4 GHz continuous wave radar—17.3% improvement over SVM.

Fall Detection Using Deep Learning in Range-Doppler Radars

Abstract: In this paper, we propose an approach that uses deep learning to detect a human fall. The proposed approach automatically captures the intricate properties of the radar returns. In order to minimize false alarms, we fuse information from both the time-frequency and range domains. Experimental data is used to demonstrate the superiority of the deep learning based approach in comparison with the principal component analysis method and those methods incorporating predefined physically interpreted features.

A New Adaptive Extended Kalman Filter for Cooperative Localization

Abstract: To solve the problem of unknown noise covariance matrices inherent in the cooperative localization of autonomous underwater vehicles, a new adaptive extended Kalman filter is proposed. The predicted error covariance matrix and measurement noise covariance matrix are adaptively estimated based on an online expectation-maximization approach. Experimental results illustrate that, under the circumstances that are detailed in the paper, the proposed algorithm has better localization accuracy than existing state-of-the-art algorithms.

Adaptive Nonsingular Fixed-Time Attitude Stabilization of Uncertain Spacecraft

Abstract: This paper addresses the problem of adaptive fixed-time attitude stabilization for uncertain rigid spacecraft with inertia uncertainties, external disturbances, actuator saturations, and faults. A nonsingular fixed-time sliding mode surface is constructed so that the settling time of the established surface is independent of the system initial states. By employing an exponential function in the controller design, an adaptive fixed-time control scheme is proposed to shorten the time during which the system states reach the sliding mode surface. With the proposed control method, the information on the bound of the lumped uncertainty is not needed in prior but estimated by the designed update laws. The fixed-time convergence of both the attitude and angular velocity is established, and comparative simulations are presented to illustrate the effectiveness of the proposed control scheme.

Fast Genetic Algorithm Path Planner for Fixed-Wing Military UAV Using GPU

Abstract: Military unmanned aerial vehicles (UAVs) are employed in highly dynamic environments and must often adjust their trajectories based on the evolving situation. To operate autonomously and safely, a UAV must be equipped with a path planning module capable of quickly recalculating a feasible and quasi-optimal path in flight while in the event a new obstacle or threat has been detected or simply if the destination point is changed during the mission. To allow for a fast path planning, this paper proposes a parallel implementation of the genetic algorithm on graphics processing unit (GPU). The trajectories are built as series of line segments connected by circular arcs resulting in smooth paths suitable for fixed-wing UAVs. The fitness function we defined takes into account the dynamic constraints of the UAVs and aims to minimize fuel consumption and average flying altitude in order to improve range and avoid detection by enemy radars. This fitness function is also implemented on the GPU and different parallelization strategies were developed and tested for each step of the fitness evaluation. By exploiting the massively parallel architecture of GPUs, the execution time of the proposed path planner was reduced by a factor of 290x compared to a sequential execution on CPU. The path planning module developed was tested using 18 scenarios on six realistic three-dimensional terrains with multiple no-fly zones. We found that the proposed GPU-based path planner was able to find quasi-optimal solutions in a timely fashion allowing in-flight planning.

Poisson Multi-Bernoulli Mixture Filter: Direct Derivation and Implementation

Abstract: We provide a derivation of the Poisson multi-Bernoulli mixture (PMBM) filter for multitarget tracking with the standard point target measurements without using probability generating functionals or functional derivatives. We also establish the connection with the $\delta$ -generalized labeled multi-Bernoulli ( $\delta$ -GLMB) filter, showing that a $\delta$ -GLMB density represents a multi-Bernoulli mixture with labeled targets so it can be seen as a special case of PMBM. In addition, we propose an implementation for linear/Gaussian dynamic and measurement models and how to efficiently obtain typical estimators in the literature from the PMBM. The PMBM filter is shown to outperform other filters in the literature in a challenging scenario.

Robust Fault-Tolerant Tracking Control for Spacecraft Proximity Operations Using Time-Varying Sliding Mode

Abstract: The capture of a free-floating tumbling object using an autonomous vehicle is a key technology for many future orbital missions. Spacecraft proximity operations will play an important role in guaranteeing the success of such missions. In this paper, we technically propose a tracking control scheme for proximity operations between a target and a pursuer spacecraft that ensures accurate relative position tracking as well as attitude synchronization. Specifically, an integrated six degrees of freedom dynamics model is first established to describe the relative motion of the pursuer with respect to the target. Then, a robust fault-tolerant controller is derived by combining the sliding mode control and the adaptive technique. The designed controller is proved to be not only robust against unexpected disturbances and adaptive to unknown and uncertain mass/inertia properties of the pursuer, but also able to accommodate a large class of actuator faults. In particular, by incorporating a novel time-varying forcing function into the sliding dynamics, the proposed control algorithm is able to guarantee the finite-time convergence of the translational and rotational tracking errors, and the convergence time as an explicit parameter can be assigned a priori by the designers. Furthermore, a theoretical analysis is also presented to assess the fault tolerance ability of the designed controller. Finally, numerous examples are carried out to evaluate the effectiveness and demonstrate the benefits of the overall control approach.

FDA-MIMO Radar Range–Angle Estimation: CRLB, MSE, and Resolution Analysis

Abstract: Multiple-input multiple-output (MIMO) radar enjoys the advantage of increased degrees-of-freedom and spatial diversity gain, but it cannot effectively resolves the targets closely spaced in the same angle cell (but different range cells). Frequency diverse array (FDA)-MIMO radar can handle this problem by exploiting its range-dependent beampattern. FDA-MIMO radar was, thus, suggested for range–angle estimation of targets. Nevertheless, it is necessary to provide theoretical performance analysis for such a relatively new radar technique. Since multiple signal classification (MUSIC) algorithm is widely adopted in most of the FDA-MIMO literature, this paper derives the Cramer–Rao lower bound and mean square error expressions in MUSIC-based range–angle estimation algorithms for a general FDA-MIMO radar. Furthermore, the corresponding range and angle resolution thresholds in target detection and localization are also derived. Numerical results verify that the FDA-MIMO indeed outperforms conventional MIMO radar in both range–angle estimation and resolution threshold performance.

Continuous Finite-Time Attitude Control for Rigid Spacecraft Based on Angular Velocity Observer

Abstract: This paper addresses the problem of designing an angular velocity observer and an output feedback attitude controller with finite-time convergence and disturbances for spacecraft. First, two new concepts of finite-time stability are proposed and defined as the local fast-finite-time stability and the fast-finite-time uniformly ultimately boundness, which can be seen as the extensions of the traditional fast-finite-time stability. Then, based on these two concepts of stability, a fast-finite-time observer is designed to estimate the unknown angular velocity. Next, based on the estimation of the angular velocity, a nonsingular and continuous attitude control algorithm is proposed to achieve the finite-time stability or finite-time boundness. With consideration of the observation errors and disturbances in the closed-loop system, a rigorous analysis of the proposed strategy is provided through Lyapunov approach. It shows that the observation errors and the spacecraft attitude will converge to a region of zero in finite time. Numerical simulation studies are presented to illustrate the effectiveness of the proposed observer-based attitude control scheme.

Sparsity-Driven Micro-Doppler Feature Extraction for Dynamic Hand Gesture Recognition

Abstract: In this paper, a sparsity-driven method of micro-Doppler analysis is proposed for dynamic hand gesture recognition with radar sensors. First, sparse representations of the echoes reflected from dynamic hand gestures are achieved through the Gaussian-windowed Fourier dictionary. Second, the micro-Doppler features of dynamic hand gestures are extracted using the orthogonal matching pursuit algorithm. Finally, the nearest neighbor classifier is combined with the modified Hausdorff distance to recognize dynamic hand gestures based on the sparse micro-Doppler features. Experiments with real radar data show that the recognition accuracy produced by the proposed method exceeds 96% under moderate noise, and the proposed method outperforms the approaches based on principal component analysis and deep convolutional neural network with small training dataset.

Maritime Moving Target Indication Using Passive GNSS-Based Bistatic Radar

Abstract: This paper is a first introduction to the concept of using global navigation satellite systems (GNSS) as illuminators of opportunity in a passive bistatic real-time radar system for maritime target indication applications. An overview of the system concept and the signal processing algorithms for moving target indication is provided. To verify the feasibility of the system implementation as well as test the developed signal processing algorithms, an experimental test bed was developed and the appropriate experimental campaign with the new Galileo satellites and a ferry as the target was carried out. The results confirm the system concept and its potential for multistatic operation, with the ferry being detected simultaneously by two satellites.

GNSS Signal Authentication Via Power and Distortion Monitoring

Abstract: We propose a simple low-cost technique that enables civil global positioning system receivers and other civil global navigation satellite system (GNSS) receivers to reliably detect carry-off spoofing and jamming. The technique, which we call the power-distortion detector, classifies received signals as interference-free, multipath-afflicted, spoofed, or jammed according to observations of received power and correlation function distortion. It does not depend on external hardware or a network connection and can be readily implemented on many receivers via a firmware update. Crucially, the detector can with high probability distinguish low-power spoofing from ordinary multipath. In testing against more than 25 high-quality empirical datasets yielding more than 900,000 separate detection tests, the detector correctly alarms on all malicious spoofing or jamming attacks while maintaining a $ 0.6% single-channel false alarm rate.

Spectrum Allocation for Noncooperative Radar Coexistence

Abstract: Access to the electromagnetic spectrum is an ever-growing challenge for radar. Future radar will be required to mitigate RF interference from other RF sources, relocate to new frequency bands while maintaining quality of service, and share frequency bands with other RF systems. The spectrum sensing, multioptimization (SS-MO) technique was recently investigated as a possible solution to these challenges. Prior results have indicated significant improvement in the signal-to-interference plus noise ratio at the cost of a high computational complexity. However, the optimization computational cost must be manageable in real time to address the dynamically changing spectral environment. In this paper, a bioinspired filtering technique is investigated to reduce the computational complexity of SS-MO. The proposed technique is analogous to the processing of the thalamus in the human brain in that the number of samples input to SS-MO is significantly decreased, thus, resulting in a reduction in computational complexity. The performance and computational complexity of SS-MO and the proposed technique are investigated. Both techniques are used to process a variety of measured spectral data. The results indicate a significant decrease in computational complexity for the proposed approach while maintaining performance of the SS-MO technique.

Multi-UAV Task Assignment With Parameter and Time-Sensitive Uncertainties Using Modified Two-Part Wolf Pack Search Algorithm

Abstract: 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.

Minimum-Fuel Low-Thrust Transfers for Spacecraft: A Convex Approach

Abstract: This paper presents a convex approach to the numerical solution of the minimum-fuel low-thrust orbit transfer problem. The main contribution is the transformation of the original nonlinear optimal control problem into a sequence of convex optimization problems. First, the control is decoupled from the states through a change of variables. Then, by introducing a lossless convexification technique, the control constraints are convexified, and the original problem is relaxed into a sequence of second-order cone programming problems. The resulting subproblems can be solved in real time by efficient interior-point methods. Finally, the effectiveness of the proposed methodology is demonstrated through numerical simulations of the three-dimensional minimum-fuel Earth-to-Mars low-thrust transfer problem.

An INS Monitor to Detect GNSS Spoofers Capable of Tracking Vehicle Position

Abstract: In this paper, we describe and evaluate a new monitor that uses inertial navigation system (INS) measurements to detect spoofing attacks on global navigation satellite system (GNSS) receivers. Spoofing detection is accomplished by monitoring the Kalman filter innovations in a tightly coupled INS/GNSS mechanization. Monitor performance is evaluated against worst case spoofing attacks, including spoofers capable of tracking vehicle position. There are two main contributions of this paper. The first is a mathematical framework to quantify postmonitor spoofing integrity risk. The second is an analytical expression of the worst case sequence of spoofed GNSS signals. We then apply these to an example spoofing attack on a Boeing 747 on final approach. The results show that GNSS spoofing is easily detected, with high integrity, unless the spoofer's position-tracking devices have unrealistic, near-perfect accuracy, and no delays.

Spectrum Sharing Radar: Coexistence via Xampling

Abstract: We present a Xampling-based technology enabling interference-free operation of radar and communication systems over a common spectrum. Our system uses a recently developed cognitive radio (CRo) to sense the spectrum at low sampling and processing rates. The Xampling-based cognitive radar (CRr) then transmits and receives in the available disjoint narrow bands. Our main contribution is the unification and adaptation of two previous ideas—CRo and CRr—to address spectrum sharing. Hardware implementation shows robust performance at SNRs up to –5 dB.

Consensus-Based Two-Stage Salvo Attack Guidance

Abstract: The problem of salvo attack of multiple missiles without time-to-go estimation has been addressed by a two-stage guidance scheme in this correspondence. In the first stage, a simple decentralized control law is designed to provide desired initial conditions (target-missile relative range and velocity lead angle) for the latter stage. As for the second stage, all missiles are governed by classical pure proportional navigation guidance law. Numerical simulations under two-dimensional (2-D) and 3-D cases demonstrate the effectiveness of the proposed formulation.

Multiple Ornstein–Uhlenbeck Processes for Maritime Traffic Graph Representation

Abstract: We propose an unsupervised procedure to automatically extract a graph-based model of commercial maritime traffic routes from historical Automatic Identification System (AIS) data. In the proposed representation, the main elements of maritime traffic patterns, such as maneuvering regions and sea-lanes, are represented, respectively, with graph vertices and edges. Vessel motion dynamics are defined by multiple Ornstein–Uhlenbeck processes with different long-run mean parameters, which in our approach can be estimated with a change detection procedure based on Page's test, aimed to reveal the spatial points representative of velocity changes. A density-based clustering algorithm is then applied to aggregate the detected changes into groups of similar elements and reject outliers. To validate the proposed graph-based representation of the maritime traffic, two performance criteria are tested against a real-world trajectory dataset collected off the Iberian Coast and the English Channel. Results show the effectiveness of the proposed approach, which is suitable to be integrated at any level of a JDL system.

Receive-Beam Resource Allocation for Multiple Target Tracking With Distributed MIMO Radars

Abstract: A distributed multiple-input multiple-output (MIMO) radar system is capable of tracking multiple targets under the “defocused transmit-focused receive” (DTFR) operating mode, in which each transmitter forms a completely defocused beam to illuminate the whole surveillance region and each receiver adopts a focused beam to attain a higher resolution. However, when operating in this mode, there exists a resource optimization problem on the allocation of receive-beams. To address this problem, a receive-beam resource allocation (RBRA) strategy is proposed in this paper. The key mechanism is to implement the optimal allocation between receive-beams and targets based on the feedback information in the tracking recursion cycle, with the objective of improving the worst tracking accuracy with multiple targets. Since the posterior Cramer–Rao lower bound (PCRLB) provides a lower bound on the accuracy of target state estimates, it is derived and adopted as an optimization criterion. It is shown that the optimal RBRA is a multidimensional nonconvex assignment problem that is NP-hard. We propose an efficient convex relaxation optimization to solve it. Numerical results demonstrate the superior performance of the proposed strategy in terms of the worst case tracking root mean-square errors.

Adaptive Finite-Time Attitude Tracking Control for Spacecraft With Disturbances

Abstract: In this paper, the adaptive finite-time attitude tracking control problem for rigid spacecraft with upper bounds unknown external disturbances is studied by using a novel combining control scheme of adaptive control technique and adding a power integrator (AAPI) technique. An adaptive finite-time control law with boundary layer is first established under the proposed combined control scheme, which can attenuate the influences of disturbances and contain the advantages of AAPI technique. Then, another adaptive finite-time control law without boundary layer is further given, which can simply the control design. It is proved that both the two control laws can ensure the tracking errors converge to the desired regions in finite-time. Moreover, since the established two control laws are both designed with continuous control architecture, the chattering problem is eliminated, which are more adopt to practical engineering applications. An example is included to show the effectiveness of the proposed methods.

An Efficient Recursive Multiframe Track-Before-Detect Algorithm

Abstract: Multiframe track-before-detect (MF-TBD) usually uses sliding-window-based batch processing, where a number $N$ of the latest data frames are jointly processed at each measurement time. The sliding window mechanism compromises the operating efficiency of MF-TBD by increasing both computational costs and memory requirements, thus, heavily restricting its application in practical problems. In this paper, an improved recursive implementation for MF-TBD is proposed. Unlike the sliding-window-based implementation, the proposed method calculates the merit function, a measure of the possibility that a state is target originated, of the current batch based on an approximated recursive relationship between the merit functions of consecutive batches. As a result, instead having to process the whole batch, at any given time only the latest frame needs to be processed. The recursive relationship is first derived for any arbitrary merit function, and then explored further with several typical merit functions that are used in MF-TBD. Both the theoretical analysis and simulation results demonstrate that the proposed method can achieve almost $N$ times reduction in computational complexity and memory requirements with negligible performance loss.

Optimal Collaborative Mapping of Terrestrial Transmitters: Receiver Placement and Performance Characterization

Abstract: Mapping multiple unknown terrestrial signals of opportunity (SOP) transmitters via multiple collaborating receivers is considered. The receivers are assumed to have knowledge about their own states, make pseudorange observations on multiple unknown SOPs, and fuse these pseudoranges through a central estimator. Two problems are considered. The first problem assumes multiple receivers with random initial states to pre-exist in the environment. The question of where to optimally place an additional receiver so to maximize the quality of the estimate of the SOPs’ states is addressed. A novel, computationally efficient optimization criterion that is based on area-maximization is proposed. It is shown that the proposed optimization criterion yields a convex program, the solution of which is comparable to two classical criteria: minimization of the geometric dilution of precision (GDOP) and maximization of the determinant of the inverse of the GDOP matrix. The second problem addresses the optimal mapping performance as a function of time and number of receivers in the environment. It is demonstrated that such optimal performance assessment could be generated off-line without knowledge of the receivers’ trajectories or the receivers’ estimates of the SOP. Experimental results are presented demonstrating collaborative mapping of an unknown terrestrial SOP emanating from a cellular tower for various receiver trajectories versus the optimal mapping performance.

Optimal Cooperative Guidance Law for Salvo Attack: An MPC-Based Consensus Perspective

Abstract: In this paper, a model-predictive-control (MPC)-based cooperative guidance law is presented to perform a salvo attack against a stationary target, which guarantees that multiple missiles hit the target simultaneously. Inspired by the concept of consensus in the multiagent systems, the impact time coordination is reformulated as a consensus on the ranges and range rates of the missiles, leading to a solution without explicitly exploiting the time-to-go or its estimate. By the virtue of an MPC framework, constraints on the normal acceleration and field-of-view are also addressed, and the finite-horizon optimality is guaranteed. Three numerical simulation cases are provided to demonstrate the effectiveness of the proposed approach.

Look Angle Constrained Impact Angle Control Guidance Law for Homing Missiles With Bearings-Only Measurements

Abstract: This paper presents an impact angle control guidance law that confines the missile look angle during homing in order to not exceed a seeker's field-of-view limit. A sliding surface variable whose regulation guarantees the interception of a stationary target at the desired impact angle is designed, and the guidance law is derived to make the surface variable go to the sliding mode. Using a magnitude-limited sigmoid function in the surface variable, the proposed law prohibits the look angle from exceeding the specified limit during the entire homing. This capability to confine the missile look angle is valuable when a seeker's field-of-view is restricted, since imposing the terminal impact angle constraint demands the missile to fly a curved trajectory. Furthermore, the proposed law can be implemented under bearings-only measurements because the command does not involve any information of the relative range and line-of-sight rate. Numerical simulations are conducted to demonstrate the validity of the proposed law. The result shows that the proposed guidance law accomplishes the impact angle constraint without violating the prescribed look angle limit although it only uses the information of bearing angles.

Cooperative Missile Guidance for Active Defense of Air Vehicles

Abstract: In air combat, an active countermeasure against an attacking missile homing into a Target aircraft entails the launch of a defending missile. The Target is protected by the Defender, which aims to intercept the Attacker before the latter reaches the Target aircraft. A differential game is presented where the Target–Defender team strives to maximize the terminal separation between the Target and the Attacker at the time instant where the Attacker is intercepted by the Defender, whereas the Attacker strives to minimize the said separation. This paper discusses the case where the Defender is endowed with a positive capture radius. Optimal strategies for the three agents are derived and simulation examples illustrate the effectiveness of the proposed approach.

Optimizing Communication and Computation for Multi-UAV Information Gathering Applications

Abstract: Typical mobile agent networks, such as multi-unmanned aerial vehicle (UAV) systems, are constrained by limited resources: energy, computing power, memory and communication bandwidth. In particular, limited energy affects system performance directly, such as system lifetime. Moreover, it has been demonstrated experimentally in the wireless sensor network literature that the total energy consumption is often dominated by the communication cost, i.e., the computational and the sensing energy are small compared to the communication energy consumption. For this reason, the lifetime of the network can be extended significantly by minimizing the communication distance as well as the amount of communication data, at the expense of increasing computational cost. In this paper, we aim at attaining an optimal tradeoff between the communication and the computational energy. Specifically, we propose a mixed-integer optimization formulation for a multihop hierarchical clustering-based self-organizing UAV network incorporating data aggregation, to obtain an energy-efficient information routing scheme. The proposed framework is tested on two applications, namely target tracking and area mapping. Based on simulation results, our method can significantly save energy compared to a baseline strategy, where there is no data aggregation and clustering scheme.

Disturbance-Observer-Based Robust Relative Pose Control for Spacecraft Rendezvous and Proximity Operations Under Input Saturation

Abstract: This paper investigates the robust relative pose control for spacecraft rendezvous and proximity operations subject to input saturation, kinematic couplings, parametric uncertainties, and unknown external disturbances. Relative rotational and relative translational nonlinear system models are first derived, and relative attitude and relative position controllers are then proposed, respectively. The kinematic couplings, parametric uncertainties, and unknown external disturbances in dynamical models are treated as compound disturbances, and nonlinear disturbance observers are developed and incorporated into the relative pose control design, which can avoid the assumption on the bounded derivatives of compound disturbances. Meanwhile, input saturation effect of the control torques and forces is compensated by synthesizing the outputs of the auxiliary systems into the controllers. Based on the proposed disturbance observers and auxiliary systems, saturated attitude synchronization and position tracking controllers are developed to reject the unknown compound disturbances and ensure the convergence of the relative pose and velocities. The stability of the closed-loop system is rigorously proved in the Lyapunov framework; relative pose and velocities ultimately converge to the small neighborhoods of the origin in spite of input saturation and model uncertainties. Simulation experiments validate the performance of the proposed robust saturated control strategy.

Maritime Moving Target Long Time Integration for GNSS-Based Passive Bistatic Radar

Abstract: This paper addresses the exploitation of Global Navigation Satellite Systems (GNSS) as transmitters of opportunity in passive bistatic radar systems for maritime surveillance. The main limitation of this technology is the restricted power budget provided by navigation satellites, which makes it necessary to define innovative moving target detection techniques specifically tailored for the system under consideration. To this aim, this paper puts forward long integration time techniques able to collect the signal energy over long time intervals (tens of seconds), allowing the retrieval of suitable levels of signal-to-disturbance ratios for detection purposes. A local plane based technique is first considered, providing target detection in a plane that represents the section of maritime area covered by the radar antenna. As a suboptimum solution in terms of achievable integration gain, but more efficient from a computational point of view, a second technique is considered working in the conventional bistatic range and Doppler plane (basic plane based). Results against synthetic and experimental datasets show the effectiveness of the proposed techniques.

Optimal Rendezvous Trajectory for Unmanned Aerial-Ground Vehicles

Abstract: Fixed-wing unmanned aerial vehicles (UAVs) can be an essential tool for low-cost aerial surveillance and mapping applications in remote regions. There is, however, a key limitation, which is the fact that low-cost UAVs have limited fuel capacity and, hence, require periodic refueling to accomplish a mission. Moreover, the usual mechanism of commanding the UAV to return to a stationary base station for refueling can result in the fuel wastage and inefficient mission operation time. Alternatively, one strategy could be the use of an unmanned ground vehicle (UGV) as a mobile refueling unit, where the UAV will rendezvous with the UGV for refueling. In order to accurately perform this task in the presence of wind disturbances, we need to determine an optimal trajectory in three-dimensional taking UAV and UGV dynamics and kinematics into account. In this paper, we propose an optimal control formulation to generate a tunable UAV trajectory for rendezvous on a moving UGV that also addresses the possibility of the presence of wind disturbances. By a suitable choice of the value of an aggressiveness index that we introduce in our problem setting, we are able to control the UAV rendezvous behavior. Several numerical results are presented to illustrate the reliability and effectiveness of our approach.