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Enhancing operation efficiency of flight deck has become a hotspot because it has an important impact on the fighting capacity of the carrier–aircraft system. To improve the operation efficiency, aircraft need taxi to the destination on deck with the optimal trajectory. In this paper, a general method is proposed to solve the trajectory optimization problem for aircraft taxiing on flight deck considering that the existing methods can only deal with the problem in some specific cases. Firstly, the ground motion model of aircraft, the collision detection strategy and the constraints are included in the mathematical model. Then the principles of the chicken swarm optimization algorithm and the generality of the proposed method are explained. In the trajectory optimization algorithm, several strategies, i.e. generation of collocation points, transformation of control variable, and setting of segmented fitness function, are developed to meet the terminal constraints easier and make the search efficient. Three groups of experiments with different environments are conducted. Aircraft with different initial states can reach the targets with the minimum taxiing time, and the taxiing trajectories meet all the constraints. The reason why the general trajectory optimization method is validated in all kinds of situations is also explained.

Reentry guidance has been researched as a popular topic because it is critical for a successful flight. In view that the existing guidance methods do not take into account the accumulated navigation error of Inertial Navigation System (INS) in the blackout zone, in this paper, an adaptive reentry guidance method is proposed to obtain the optimal reentry trajectory quickly with the target of minimum aerodynamic heating rate. The terminal error in position and attitude can be also reduced with the proposed method. In this method, the whole reentry guidance task is divided into two phases, i.e., the trajectory updating phase and the trajectory planning phase. In the first phase, the idea of model predictive control (MPC) is used, and the receding optimization procedure ensures the optimal trajectory in the next few seconds. In the trajectory planning phase, after the vehicle has flown out of the blackout zone, the optimal reentry trajectory is obtained by online planning to adapt to the navigation information. An effective swarm intelligence algorithm, i.e. pigeon inspired optimization (PIO) algorithm, is applied to obtain the optimal reentry trajectory in both of the two phases. Compared to the trajectory updating method, the proposed method can reduce the terminal error by about 30% considering both the position and attitude, especially, the terminal error of height has almost been eliminated. Besides, the PIO algorithm performs better than the particle swarm optimization (PSO) algorithm both in the trajectory updating phase and the trajectory planning phases.

Flame chemiluminescence tomography (FCT) plays an important role in combustion monitoring and diagnostics due to the easy implementation and non-intrusion. However, on account of the high data throughput and the inefficiency of the conventional iteration methods, the 3D reconstructions in FCT are typically conducted off-line and time-consuming. In this work, we present a 3D rapid FCT reconstruction system based on convolutional neural networks (CNN) model for practical combustion measurement, which has the ability to reconstruct 3D flame distribution rapidly after training process. First, the numerical simulation has been performed by creating three cases of phantoms which are designed to mimic the 3D conical flame. Next, after the evaluation of loss function and training time, the optimal CNN architecture has been determined and certificated quantitatively. Finally, a real time FCT system consisting of 12 color CCD cameras is realized and multispectral separation algorithm is adopted to extract CH* and C2* components. Certificated by practical measurements testing, the proposed CNN model is able to reconstruct 3D flame structure from real time captured projections with credible accuracy and structure similarity. Furthermore, compared with conventional iteration reconstruction method, the proposed CNN model shows better performance on obviously improving reconstruction speed and it is expected to achieve 3D rapid monitoring of flames.

Synthetic aperture particle image velocimetry (SAPIV) provides a non-invasive means of revealing the physics of complex flows using a compact camera array to resolve the 3D flow field with high temporal and spatial resolution. Intensity-threshold-based methods of reconstructing the flow field are unsatisfactory in nonuniform illuminated fluid flows. This article investigates the characteristics of the focused particles in re-projected image stacks, and presents a convolutional neural network (CNN)-based particle field reconstruction method. The CNN architecture determines the likelihood of each area containing focused particles in the re-projected 3D image stacks. The structural similarity between the images projected by the reconstructed particle field and the images captured from the cameras is then computed, allowing in-focus particles to be extracted. The feasibility of our method is investigated through synthetic simulations and experiments. The results show that the proposed technique achieves remarkable performance, paving the way for non-uniformly illuminated particle field applications in 3D velocity measurements.

Pilot modeling techniques, have played a crucial role in manned aviation and have also, as a consequence, generated major advances in human control behavior research. This paper presents a review of control models for human pilot behavior. The purposes of the models are to analyze the characteristics of the pilot-aircraft system, and to provide valuable guidance in the flight control system design. Existing human pilot models are classified into three types. The first category of models, based on control theory, can only be used to describe the control functions of human pilot. The typical models include McRuer quasi-linear models and optimal control models. Then the principle of human pilot control behavior was revealed from the physiological perspective, the Hess structural model being the most common one. Hosman's descriptive model and biodynamic model are also summarized. The paper also proposes that the development of artificial intelligence technology has enabled the study of nonlinear characteristics of pilot behavior in manual control. Models based on intelligence techniques are briefly introduced, e.g., fuzzy control models, neural network models. Finally, the paper outlines several typical and future applications of the techniques in the pilot modeling field. However, as the presented discussion is limited to a small area of pilot modeling, many other challenges remain open for further research and development.

In this paper, nonlinear flight control problem for Air-breathing Hypersonic Vehicles (AHVs) is addressed. Designing a high quality flight controller for AHVs is highly challenging due to model uncertainties caused by inherent structural/aerodynamic couplings, high nonlinearities within the large envelope. A nonlinear flight control approach named Sensor-Based Backstepping (SBB) is employed to design a highly robust controller for AHVs, and ensure that the controller does not suffer from large parameter variations and model uncertainties. The SBB approach is essentially an incremental type of approximate dynamic inversion (ADI) control scheme developed based on singular perturbation theory and multi-loop backstepping design methodology. The SBB uses angular accelerometers to capture dynamic changes in time derivatives of body angular rates, and requires nearly no accurate model information in its implementation. In this way, unlike model based adaptive nonlinear control laws such as adaptive nonlinear dynamic inversion (ANDI), dependency on accurate full model information and the associated identification workload is removed. Several key issues associated with inherent characteristics of AHVs before the SBB based AHV controller is developed. To validate the SBB flight controller, numerical simulations are performed on a nonlinear AHV model with sudden model parameter uncertainties introduced, and the controller is compared with the linear controller augmented Linear Quadratic Regulator (LQR) and the real-time model based ANDI controller. The results have demonstrated the superior tracking performance of the SBB approach, and highlighted its strong robustness to model uncertainties.

Safety and efficiency are crucial for landing a team of aircraft in order to improve the combat capability of aircraft carrier. One way to enhance the safety and efficiency level of the landing mission is to optimize the landing sequence. It can shorten the landing time consumption and improve the capacity of deck as well as flight safety. In this paper, a modeling and sequencing approach for landing a team of aircraft is proposed. Firstly, the sequencing problem for landing a team of aircraft (SPLTA) is described by introducing each procedure of the landing mission. The traffic in the terminal area and the strategy for failed-to-land aircraft (FLA) are paid more attention because they are closely related to the SPLTA. Then the state change of a single aircraft in flight and exchanges of aircraft in the terminal area are taken into account. The overall SPLTA is formulated into an optimization problem with a cost function subjected to realistic constraints. To solve the SPLTA, a dynamic sequencing algorithm using ant colony method (DSAAC) is proposed to enable a team of aircraft to land with an optimal sequence. In the experiments, the SPLTA is solved using a “least fuel first service” (LFFS) principle based method, the ant colony optimization algorithm (ACO) based method, the static sequencing algorithm of ant colony (SSAAC) and the DSAAC. It shows that the DSAAC performs better than other three methods in minimizing the cost function and the landing time consumption. Furthermore, DSAAC guarantees a higher level of flight safety and yields an effective response to dynamic circumstance. The DSAAC approach provides an intelligent tool for overall air traffic management on aircraft carrier.

The flow field velocity is an important parameter for completely characterizing the topologies of unsteady coherent flow structures. Synthetic aperture (SA)-based particle image velocimetry (SAPIV) has been used for three-dimensional flow measurements, owing to its wide range of acceptable tracer particle intensities and ability to view partially occluded fields. However, SAPIV typically suffers from poor reconstruction quality for nonuniformly illuminated particle volumes. In this paper, we propose a hybrid remapping particle field reconstruction method for SAPIV in a nonuniformly illuminated fluid flow. Both additive and minimum line-of-sight remapping are used to reconstruct the in-focus particles from the refocused image stacks. The structural similarity between the images projected by the reconstructed particle field and the images captured by the cameras are used to determine the reconstruction quality. This method was verified by both synthetic simulation and an experimental implementation. The performance of the proposed technique was compared with existing methods. The proposed method has the best reconstruction quality and computational speed among the considered methods.

Synthetic aperture particle image velocimetry (SAPIV) is a flow field diagnostic technique that provides instantaneous velocimetry information non-intrusively. In SAPIV, particle scattering images are captured from different cameras with camera array configuration. To acquire refocusing images, images are remapped and accumulated in pre-designed remapping planes. During the refocused images, particles that lie in the remapped plane are aligned and appear sharp, whereas particles off this surface are blurred due to parallax between the cameras. During the remapping process, captured images are back-projected to different remapped planes of different depth z within the volume. The projected images from different cameras, which are called remapped images, are merged to generate refocused images at different depth z. We developed a remap method based on the weight coefficient to improve the quality of the reconstructed velocity field. The images captured from the cameras are remapped into different remapped planes by use of homography matrix. The corresponding pixels of the remapped images in the same remapped plane are first added and averaged. The corresponding pixels of the remapped images in the same remapped plane are multiplied and the obtained intensity values act as the weight coefficients of the intensity in the added refocused image stacks. The unfocused speckles can be restrained to a great degree, and the focused particles are retained in the added refocused image stacks. A 16-camera array and a vortex ring field at two adjacent frames are simulated to evaluate the performance of our proposed method. In the simulation, a vortex ring can be clearly seen. An experimental system consisting of 16 cameras was also used to show the capability of our improved remap method. The results show that the proposed method can effectively restrain the unfocused speckles and reconstruct the velocity field in the flow field.

Particle image velocimetry (PIV) is a necessary flow field diagnostic technique that provides instantaneous velocimetry information non-intrusively. Three-dimensional (3D) PIV methods can supply the full understanding of a 3D structure, the complete stress tensor, and the vorticity vector in the complex flows. In synthetic aperture particle image velocimetry (SAPIV), the flow field can be measured with large particle intensities from the same direction by different cameras. During SAPIV particle reconstruction, particles are commonly reconstructed by manually setting a threshold to filter out unfocused particles in the refocused images. In this paper, the particle intensity distribution in refocused images is analyzed, and a SAPIV particle field reconstruction method based on an adaptive threshold is presented. By using the adaptive threshold to filter the 3D measurement volume integrally, the three-dimensional location information of the focused particles can be reconstructed. The cross correlations between images captured from cameras and images projected by the reconstructed particle field are calculated for different threshold values. The optimal threshold is determined by cubic curve fitting and is defined as the threshold value that causes the correlation coefficient to reach its maximum. The numerical simulation of a 16-camera array and a particle field at two adjacent time events quantitatively evaluates the performance of the proposed method. An experimental system consisting of a camera array of 16 cameras was used to reconstruct the four adjacent frames in a vortex flow field. The results show that the proposed reconstruction method can effectively reconstruct the 3D particle fields.

Trajectory optimization of hypersonic vehicles has become a hotspot because of its advantages in flight speed and range. This task is challenging because of the strong nonlinear couplings a...

Electromagnetic catapult launch tends to be equipped on future carrier deck gradually. This paper aims to establish the mathematical representation of the system of electromagnetic aircraft launch considering aspects of carrier, aircraft and electromagnetic catapult. The proposed catapult system is evaluated by conducted simulation considering disturbance of deck wind and ground effect during stroke process and disturbance of wind ahead of carrier bow during flight process. The modeling approach of the system of electromagnetic aircraft launch is of significance for safety and efficiency of aircraft-carrier system.

Carrier landing task is accomplished by human pilot according to guidance signal. During lateral lineup flight, appropriate guidance signal generation is of great significance in improving carrier landing precision and reducing pilot workload. This paper proposes a method of generating the guidance signal for lateral lineup flight by taking carrier deck motion compensation and appropriate signal input moment into account. The deck motion compensation employs online auto regressive (AR) model and the signal input moment considers preventing signal mutation. Based on the analysis of pilot-aircraft system for lateral lineup flight simulation, the signal generator parameters of prediction horizon and guidance signal input moment are selected. The simulation results demonstrate the effectiveness of the suggested method in the improvement of carrier landing precision and the reduction of human pilot's workload.

This paper develops an intelligent pilot-aircraft system based on an analysis of the developmental trend of the intelligent flight control system. We establish an intelligent pilot-aircraft integrated system and analyze the characteristics from three levels (perception level, decision-making level and execution level). According to respective advantages for pilot and aircraft in specific circumstances, the control strategies can be divided into “Pilot Master/Aircraft Slave” and “Aircraft Master/Pilot Slave”. And the Pilot Aircraft Collaboration mechanism is used to allocate the appropriate authority. Multiple behavioral characteristics of the human pilot are classified and comprehensive human pilot evaluation is discussed. The authors propose an intelligent pilot-aircraft integrated system based on the idea of a pilot-aircraft system, which provides a theoretical reference for the design of an intelligent flight control system.

Safety and efficiency of carrier flight deck are two important factors that have great effects on the comprehensive performance of carrier. This paper studies on path planning algorithms for carrier aircraft on the deck in consideration of safety and efficiency requirements. Firstly, a kinematic model of the carrier aircraft, obstacle avoidance models and boundary constraints are proposed. Subsequently, search zone for path planning is designed based on geometry. Then, the optimal path is computed according to Dijkstra's Algorithm and the details are described by steps. Finally, an example is simulation to verify the rationality and the effectiveness of the path planning algorithms.

Tomographic particle image velocimetry (Tomo-PIV) is a new developed technique for three-component threedimensional (3C-3D) velocity measurement of the flow field based on the optical tomographic reconstruction method, and has been received extensive attention of the related industries. Three-dimensional light source illuminating the tracer particles of flow field is a critical application for tomographic particle image velocimetry. Three-dimensional light source not only determines the size of measurement volume and the range of the scope of application, but also has a great influence on the image quality. In this work, we propose a rectangular light amplification system using powell lens, prisms and two reflectors. The system can be optimized if given the system parameters based on the theoretical model. The rectangular light amplification system will be verified experimentally by measuring the cross section size of the illuminated light source. A 60mm×25mm cross section of rectangular three-dimensional light source can be obtained by using the rectangular light amplification system. The experiments demonstrate the the feasibility the proposed system.

Image deblurring is a fundamental problem in image processing. Conventional methods often deal with the degraded image as a whole while ignoring that an image contains two different components: cartoon and texture. Recently, total variation (TV) based image decomposition methods are introduced into image deblurring problem. However, these methods often suffer from the well-known stair-casing effects of TV. In this paper, a new cartoon -texture based sparsity regularization method is proposed for non-blind image deblurring. Based on image decomposition, it respectively regularizes the cartoon with a combined term including framelet-domain-based sparse prior and a quadratic regularization and the texture with the sparsity of discrete cosine transform domain. Then an adaptive alternative split Bregman iteration is proposed to solve the new multi-term sparsity regularization model. Experimental results demonstrate that our method can recover both cartoon and texture of images simultaneously, and therefore can improve the visual effect, the PSNR and the SSIM of the deblurred image efficiently than TV and the undecomposed methods.

Flame tomography of chemiluminescence is a necessary combustion diagnostic technique that provides instantaneous 3D information on flame structure and excited species concentrations. During combustion diagnostics, imaging overexposure always causes missing information, which obviously decreases the accuracy in further reconstructions. In order to compensate imaging overexposure, a hybrid algorithm combining weight correction and Tikhonov's regularization is proposed in this paper. The intensity of the overexposure region can be estimated via the accumulation of weight coefficients. Meanwhile, Tikhonov's regularization is utilized to ameliorate the quality of reconstruction. The numerical simulation quantitatively evaluates the performance of the hybrid algorithm. Additionally, an experiment system consisting of 12 cameras was established to reconstruct the 3D combustion structure of axisymmetric flame with different exposure time settings. This work further investigates dynamic nonaxisymmetric propane diffusion flame. The obtained results show that the hybrid algorithm can effectively reveal the flame structure less influenced by imaging overexposure and achieve better results.

PurposeAirdrop operation has become an important transportation mode due to its mobility and rapidity and mission planning is one of the critical steps in the preparation of an airdrop operation. The purpose of this paper is to propose an efficient mission planning method for airdrop operation using multiple vehicles.Design/methodology/approachA hierarchical mission planning method is proposed. According to the objectives of the action, the mission planning is divided into three planning levels to form the hierarchical structure and the constraints are distributed among them. By doing so, the proposed approach converts the original mission planning problem to a constrained optimization problem, which is solvable using existing mathematical methods.FindingsOn the basis of analysis, the mathematic models of three planning levels are established. Each level has its own optimization objective, taking part of constraints into account. The integrated mission scheme had been obtained step by step.Practical implicationsThis paper systematically tackles the complicated multiple vehicles airdrop mission planning problem, and it provides a platform for optimizing the outcomes. The mathematical models established in this paper could apply in a variety of more complex mission scenarios.Originality/valueThis paper fulfils an urgent need to study how the advantages of airdrop operation can be maximized through planning airdrop mission schemes carefully.

This paper proposes a method for predicting aircraft flying qualities according to Cooper-Harper rating scale, where neural networks model is used to describe the nonlinear characteristics of pilot control based on experimental data. The relationship between the Cooper-Harper pilot rating and characteristics of a closed-loop aircraft-pilot system is investigated according to the parameters of pilot control error of closed-loop systems and pilot model phase lag. A large number of simulation results have been used to derive a numerical method that will effectively predict the aircraft flying qualities, and the new method has been validated independently using five different aircraft configurations.

In modern fly-by-wire control system, pilot induced oscillations (PIOs) have long been an aviation safety problem. The violation of actuator rate saturation is known to be a leading cause of PIO and has long been linked with performance and stability degradation of an aircraft. This paper proposes a phase compensator designed to reduce the adverse effects of rate saturation of the control surfaces. Without changing the structure of original control scheme in the design process, this proposed phase compensator reduces the effective time delay by minimizing the error of derivatives between the commanded input signal and the actual input signal, as well as minimizing the error of altitudes between these two signals. Simulations on the pilot-aircraft closed-loop system model with rate limiter are used to check the properties of the designed compensator. The pitch angle and roll angle rate commands, which are easy to induce PIO to a pilot, are chosen as references. Results from the simulation evaluation demonstrate that this compensator has good capability of decreasing the occurrence possibility of PIO, including the severity and duration of PIO, introduced by control surface rate limiting.

The species concentrations of flame chemiluminescence play important role in combustion diagnostics, such as CH* and C2* of hydrocarbon flame, which can provide specific characteristics in combustion control and monitoring. In order to realize both CH* and C2* chemiluminescence intensity detection in propane-air diffusion flame simultaneously, we present three-dimensional dynamic flame detecting method for species concentration determination. Firstly, quantitative flame chemiluminescence multispectral separation technique based on color cameras coupled with double-channel bandpass filters is adopted for dual channel signal division. Next, flame chemiluminescence tomography combining with multi-directional simultaneous capturing is proposed for real time three dimensional observations and detection in flame. Moreover, the proposed technique can quantitatively provide comparison of species intensity between CH* and C2* for further analysis. Considering its credible detecting accuracy and simple requirements, it is believed the proposed technique can be widely used in combustion diagnostics.

The landing safety of aircraft is of great significance for the normal operation of an aircraft carrier. This paper presents an optimal method for landing routes of aircraft. Owing to the fuzziness of environmental information and human judgment, a decision-making method is introduced to reduce the workload of landing console operators (LCO). Specifically, a route option method for aircraft landing is proposed based on Fuzzy Multi-attribute Group Decision Making (FMAGDM). Firstly, the route option problem is described. Then the essential elements of route option are described. A group decision-making method is proposed, and the most reasonable landing route in current environment is obtained considering the knowledge composition and the weight of each decision maker. Finally, simulations under different environments are conducted. The results indicate that the decision-making method is able to adapt to the environment changes, and determine the most reasonable route for each validation case.

We propose a two-stage model of Data Envelopment Analysis (DEA) to assess the innovation efficiency of the innovative effort for Chinese industries. The first stage is to measure the technology efficiency and the second is to measure the economy efficiency. The integral stage is the combination of the two stages and to evaluate integral efficiency for the whole innovative process. For different evaluating purposes, two kinds of DEA models, namely CCR and BCC, are used to measure the innovation efficiency and analyze evolution of innovation efficiency of Chinese Industries during the recent years, respectively. The results indicate that most of Chinese industries have a relative high efficiency in the first stage, however, and a low efficiency in the second stage. Mediocre efficiency occurs for most of Chinese industries in the integral stage. This reveals there are some serious inconsistencies between technology capability and economic performance in most Chinese industries and their capability of transforming technology efficiency to economy is relatively poor. Research results further show that there is still much room for Chinese industries to improve their innovation efficiency. The findings reveal that the heavy investment in R&D alone can neither bring high S&T output, nor competitive advantages.

Launching safety and efficiency are important indexes to measure the fighting capacity of carrier. The study on path planning for taxi of carrier aircraft launching under actual deck environment is of great significance. In actual deck scheduling, manual command is applied to taxi of carrier aircraft, which has negative effects on the safety of staff and carrier aircraft launching. In consideration of both the safety and efficiency of carrier aircraft launching, the key elements of the problem are abstracted based on the analysis of deck environment, carrier aircraft maneuver performance and task requirements. According to the problem description, the mathematical model is established including various constraints. The carrier aircraft and the obstacles are reasonably simplified as circle and polygons respectively. What’s more, the proposed collision detection model reduces the calculations. Aimed at the features of model, the theory of model predictive control (MPC) is applied to the path search. Then a dynamic weight heuristic function is designed and a dynamic multistep optimization algorithm is proposed. Taking the Nimitz-class aircraft carrier as an example, the paths from parking place to catapult are planned, which indicate the rationality of the model and the effectiveness of the algorithm by comparing the planning results under different simulation environments. The main contribution of research is the establishment of obstacle avoidance and path planning model. In addition, it provides the solution of model and technological foundations for comprehensive command and real-time decision-making of the carrier aircraft.