The direct position determination (DPD) algorithm usually demonstrates superior effectiveness in target localization scenarios compared to conventional two-step methods. However, as the target-radar distance increases, the conventional DPD algorithm faces challenges maintaining height measurement accuracy. To address this issue, this article proposes a weighted fusion direct position determination algorithm for a 3-D target in a distributed 1-D radar system. The algorithm first employs spatial smoothing techniques to construct the covariance matrix. To address the limitations of the multiple signal classification algorithm, we propose a weighted subspace processing technique that enhances the fidelity of the noise subspace estimation. This approach achieves a closer approximation to the true noise subspace through adaptive weighting while effectively incorporating signal subspace characteristics, thereby substantially improving the algorithm's noise robustness. Furthermore, we innovatively integrate vertical dilution of precision into the cost function design to achieve optimized weighted fusion. To tackle the high computational complexity of 3-D search, we propose an efficient solution, which substantially reduces computational burden while maintaining localization accuracy. The simulation results demonstrate that the proposed algorithm improves localization accuracy, particularly in height estimation.

A substantial body of work has focused on two-dimensional (2-D) self-localization of linear arrays using wireless sensor networks (WSN). However, tackling the higher-dimensional three-dimensional (3-D) case remains an open research question. This paper explores the use of one-dimensional (1-D) angle-of-arrival (AOA) measurements, also referred to as space angles (SA), to achieve 3-D self-localization of a linear array in the presence of anchor position errors. Unlike traditional 3-D source localization using SAs, 3-D self-localization requires simultaneous estimation of the array's position and orientation (direction vector). First, we acquire a coarse solution to the weighted least-squares (WLS) problem via semidefinite relaxation (SDR), and then we enhance it through perturbation analysis. We then address the maximum likelihood (ML) estimation problem using block majorization-minimization (block-MM), which guarantees convergence to the Karush-Kuhn-Tucker (KKT) point. In addition, we propose an improved and tighter quadratic upper bound for a Rayleigh-quotient-like function introduced in our previous work. Simulations confirm that the proposed algorithm reaches the Cram & eacute;r-Rao lower bound (CRLB) performance in low-noise regimes, with the block-MM-based ML method exhibiting minimal bias.

Direct position determination (DPD) outperforms traditional two-step methods in accuracy. However, its practical application is hindered by two primary challenges: (1) the necessity to transmit raw data to the fusion center, which imposes significant demands on bandwidth and hardware resources, and (2) the absence of a closed-form solution for DPD, necessitating exhaustive search techniques and resulting in high computational complexity. To overcome these challenges, we introduce a subspace reconstruction-based DPD approach designed for an uncrewed aerial vehicle (UAV) with a mounted array. This method requires a data transmission amount equivalent to that of two-step localization, specifically angle-of-arrival (AOA) localization while achieving performance comparable to the existing DPD method based on subspace data fusion (SDF). Furthermore, we introduce a numerically convergent solution based on majorization-minimization (MM) that guarantees convergence to a stationary point, thereby significantly reducing computational complexity and grid quantization errors (GQE) by eliminating the exhaustive search process. We validate the proposed method through computer simulations and real-world measurements conducted with a rotary-wing UAV, demonstrating its effectiveness and advantages.

Array self-position determination methods based on multiple emitter data can avoid significant deviations of vehicle satellite navigation in harsh environments. However, existing array self-position determination methods show decrease in performance under multipath environments. To deal with this problem, we propose an array self-position determination method based on orthogonal grid matching with the spatial differencing method. Specifically, the direction of arrival (DOA) of direct path and multipath signals are respectively estimated by array spatial differencing method. The matching accuracy is enhanced by utilizing the prior information of direct path signal. After calculating correlation coefficients of different sources, estimated angles with high correlation are then classified into the same set. Then, the noise subspace of each angle set is reconstructed and the position is estimated by grid matching with the orthogonal property between the noise subspaces and the characteristic steering vectors. The matching results of redundant angle sets are removed as non-matching items, thus averting positioning deviations. The simulation results demonstrate that the computational complexity of the proposed method is comparable to that of the signal subspace fitting (SSF). Moreover, in terms of positioning precision, the proposed method outperforms multiple signal classification with enhanced spatial smoothing (ESSMUSIC), initial signal fitting (ISF), and SSF.

The research aims to propose a basic parameter estimation method for high-speed vertical take-off and landing (HSVTOL) aircraft, balancing rotor and fixed-wing mode requirements. Flight profiles and performance indicators are defined based on mission phases, and maximum take-off weight is estimated using the fuel fraction method. A pre-estimation model for a turboshaft–turbofan variable cycle engine (TSFVCE) was established, and the conversion between thrust and power was conducted. Constraints related to different performance requirements were analyzed, and the relationship between the rotor and the wing was established, resulting in the generation of constraint diagrams for the selection of basic parameters. This method allows for the rapid and effective estimation of basic parameters, including maximum take-off weight, rotor disk loading, and wing loading. Two tiltrotor aircraft were analyzed using this method. The estimated results closely matched actual values, with errors within a reasonable range. These findings demonstrate the method’s reliability and provide a reference for HSVTOL conceptual design and engine power matching.

In this paper, we propose a majorization-minimization (MM) based refinement strategy tailored for two-dimensional (2-D) unconditional maximum likelihood (UML) direction-of-arrival (DOA) estimation of a single source. We introduce two surrogate functions (linear and quadratic) for 2-D UML DOA estimation with the aim of successively reducing the objective function's value. The proposed MM method guarantees convergence to the objective function's stationary point. Furthermore, we employ the backtracking squared iterative method (SQUAREM) to accelerate the convergence speed of the proposed MM method. Numerical experiments further validate the efficiency of our proposed MM method.

Control redundancy is a considerable challenge in tiltrotor aircraft, making an effective control allocation scheme critical for ensuring safe and smooth transitional flights. This study focuses on medium-to-large tiltrotor aircraft with fly-by-wire flight control systems and introduces an equal control sensitivity (ECS) allocation method based on ganging control (GC). This method aims to quantify and standardize the control allocation design process and accommodate various complex optimization objectives, including minimizing the control surface deflection angle, yaw-to-roll control coupling, and transient control loads on the nacelle tilt axis and rotor hub. The results show that the ECS allocation method specifically mitigates control coupling effects and transient peak responses in the nacelle tilt-axis and rotor hub moments while maintaining equal control sensitivity within the conversion corridor. In addition, the ECS allocation method has a significant advantage in reducing the variation range and dispersion of gain scheduling in feedback loops as it enables a smooth transition from helicopter mode to airplane mode using fixed control gains, while demonstrating good disturbance rejection capabilities. The ECS allocation method simplifies the workload of the feedback loop control gain design.

Self-position determination with array sensing multiple emitter signals is a feasible alternative when the signal of the global navigation satellite system becomes weak due to obstruction or interference. This article proposes a joint self-position and yaw angle tracking based on signal steering vector expansion. To be more specific, the angles are updated with the orthogonal property of the noise subspace and the first-order Taylor expansion. In order to refine the yaw angle, the estimator with local search is proposed and the estimated error is suppressed by minimizing the derivative of the error variance. After the angle estimation, the steering vectors are reconstructed and the position iterative formula is derived from its first-order Taylor expansion. Both simulated and practical experiments indicate that the proposed algorithm has lower complexity and better tracking performance than multiple signal classification with weighted moving average (WMA), signal subspace fitting (SSF) with WMA, and weighted SSF (WSSF) with WMA.

This paper focuses on the problem of using one-dimensional (1-D) angle-of-arrival (AOA) measurements, also referred to as space angles (SA), from linear arrays to achieve three-dimensional (3-D) rigid body localization (RBL). We address the constrained weighted least squares (CWLS) and maximum likelihood estimation (MLE) problems for SA RBL. We first establish a boundary condition for SA RBL, which determines the minimum number of sensors and anchors for SA RBL. Subsequently, we employ the semidefinite relaxation (SDR) technique to relax the feasible set of the CWLS problem, converting it into a convex semidefinite program (SDP) to achieve a coarse suboptimal solution. Since the SDR technique is limited to bilinear structures, it cannot tackle the MLE problem for SA RBL (given the inverse cosine term in the MLE objective). To handle the MLE problem for SA RBL and to obtain an exact solution for the CWLS problem, we propose a novel, generic majorization-minimization (MM) framework capable of finding a stationary point solution under the special orthogonal group constraint. We establish surrogate functions for the MLE and CWLS objectives and, as a by-product, derive upper bounds for the inverse cosine, squared inverse cosine, and nonhomogeneous Rayleigh quotient functions. Simulation results demonstrate that using the coarse solution obtained from SDR as the initial point, the proposed MM-based CWLS and MLE algorithms can achieve Cram & eacute;r-Rao lower bound (CRLB) performance under low noise conditions, with the MLE algorithm exhibiting the lowest bias.

Direct position determination (DPD) (a.k.a. direct localization) offers enhanced precision over traditional two-step approaches. This technique, however, involves considerable communication overhead for transmitting raw data. Low-bit direct localization methods have recently been introduced to address this issue. In this letter, we present a gridless, one-bit maximum likelihood (ML) approach for the direct localization of an orthogonal frequency division multiplexing (OFDM) signal source. A recent majorization-minimization (MM) algorithm introduced a surrogate function for the log-likelihood function, which lacks a closed-form optimal solution and requires exhaustive searches at each iteration. Our method improves upon this algorithm by developing a refined surrogate function that yields a closed-form optimal solution, thereby eliminating the need for exhaustive searches. Accordingly, the proposed MM approach can eliminate grid quantization errors (GQE) by eliminating the search process. Simulation results validate the proposed method's efficacy in mitigating GQE and its efficiency in scenarios with densely populated search grids.

Targeting the problem of high pilot workload caused by trajectory planning of low altitude penetration, this paper proposed a trajectory planning method considering the pilot workload. Firstly, by decomposing the three-dimensional trajectory into horizontal and vertical directions, the trajectory tree method based on the dynamic programming technique is used for horizontal trajectory planning, and the comprehensive slope-curvature limitation method is used for altitude trajectory planning. Then, a method was proposed to quantify the control inputs and pilot workload based on trajectory tracking simulation. The pilot workload cost, together with flight altitude cost and route length cost, was added to the local planning of a reference trajectory. To solve the problem of dimension explosion in trajectory tree search, a segmented optimal route tree algorithm is proposed. Finally, the simulation results show that the proposed method can effectively consider the pilot workload in trajectory planning, which is helpful to improve the flight ability of the planning trajectory and reduce the pilot workload at the same time.

The purpose of this paper is to solve the problem of the helicopter load factor limit exceeding caused by inappropriate pilot control in vertical maneuvering flight. Through the collective stick control power restriction, an load factor limit protection method based on a load factor predictive algorithm is proposed. First, the load factor prediction is realized by the change rate of the collective stick position given by the actuator and the control derivative of the collective stick position to vertical acceleration, and then the vertical control power is restricted according to the predicted load factor, so as to achieve the purpose of helicopter load factor protection.

针对多旋翼无人机弹药发射过程的扰动响应问题,建立了弹药发射过程无人机飞行动力学模型.在考虑了基本增稳增控飞行控制的条件下,分析了弹药安装位置、质量、反冲量大小和倾斜发射角等参数对发射过程无人机位置、高度和姿态扰动响应的影响规律.分析表明:对于弹药水平发射,纵向安装位置对无人机水平位置扰动幅值具有重要影响,适当后置弹药安装位置可减小无人机位置扰动幅值,在其他参数一定的条件下,存在无人机位置扰动最小的弹药纵向安装位置.对于弹药倾斜发射,给出了无人机水平位置扰动幅值关于弹药安装位置和倾斜发射角的等值图,可为无人机和弹药的匹配选型问题提供参考.

Advanced CFD tools are nowadays used routinely for analysis and design of rotorcraft with the research community shifting towards simulations of rotorcraft during maneuvering flight. One of the impediments of this effort is the lack of data for validation, evaluation and thorough assessment of CFD methods when it comes to rotors with time-varying control inputs. This paper presents a first effort to validate CFD tools for step-inputs in rotor control angles against previously un-published experimental data. The experimental study was carried out at the Nanjing University of Aeronautics and Astronautics in China and consists of time varying control inputs for a hovering rotor. The agreement between the simulation and experimental study is good and quantify the overshoot in loads for this dynamic case. The results examine the lag in loads response to and the dynamic response of the wake with recommendations on spatial and temporal resolution to capture these effects.

No AccessTechnical CommentsComment on “Analysis of Helicopter Handling Quality in Turbulence with Recursive von Kármán Mode”Peter J. ShermanPeter J. Sherman https://orcid.org/0000-0002-8985-7195Iowa State University, Ames, Iowa 50011*Associate Professor, Department of Aerospace Engineering and the Department of Statistics; .Search for more papers by this authorPublished Online:21 Feb 2021https://doi.org/10.2514/1.C036265SectionsRead Now ToolsAdd to favoritesDownload citationTrack citations ShareShare onFacebookTwitterLinked InRedditEmail About References [1] Ji H., Chen R. and Li P., “Analysis of Helicopter Handling Quality in Turbulence with Recursive von Kármán Model,” Journal of Aircraft, Vol. 54, No. 5, 2017, pp. 1631–1639. https://doi.org/10.2514/1.C034189 LinkGoogle Scholar[2] Brown R. G. and Hwang P. Y. C., Introduction to Random Signals and Applied Kalman Filtering, 4th ed., Wiley, Hoboken, NJ, 2012, p. 118. Google Scholar[3] Schmidt D. K., Modern Flight Dynamics, McGraw–Hill, New York, 2012. Google Scholar[4] Nelson R. C., Flight Stability and Automatic Control, 2nd ed., McGraw–Hill, New York, 1998. Google Scholar[5] Etkin B. and Read L. D., Dynamics of Flight Stability and Control, 3rd ed., Wiley, Hoboken, NJ, 1996. Google Scholar[6] Etkin B., “Turbulent Wind and Its Effect on Flight,” Journal of Aircraft, Vol. 18 No. 5, 1981, pp. 327–345. https://doi.org/10.2514/3.57498 LinkGoogle Scholar[7] Kailath T., Linear Systems, Prentice–Hall, Upper Saddle River, NJ, 1980. Google Scholar[8] Papoulis A., Probability, Random Variables, and Stochastic Processes, McGraw–Hill, New York, 1965. Google Scholar[9] MATLAB, Ver. R2018a, https://www.mathworks.com/help/aeroblks/drydenwindturbulencemodelcontinuous.html [retrieved 9 Feb. 2021]. Google Scholar[10] Handbook: Flying Qualities of Piloted Aircraft. U.S. Dept. of Defense MIL-HBDK 1797, Dec. 1997. Google Scholar[11] Beal R. T., “Digital Simulation of Atmospheric Turbulence for Dryden and vonKármán Models,” Journal of Guidance, Control, and Dynamics, Vol. 16, No. 1, 1993, pp. 132–138. https://doi.org/10.2514/3.11437 LinkGoogle Scholar[12] Campbell C. W., “Monte Carlo Simulation Using Rational Approximations to vonKármán Spectra,” AIAA Journal, Vol. 24, No. 1, 1986, pp. 62–66. https://doi.org/10.2514/3.9223 LinkGoogle Scholar[13] Gradshteyn I. S. and Ryzhik I. M., Table of Integrals, Series, and Products, Corrected and Enlarged Edition, Academic Press, New York, 1980. Google Scholar Previous article Next article FiguresReferencesRelatedDetails What's Popular Volume 58, Number 2March 2021 CrossmarkInformationCopyright © 2021 by the authors. Published by the American Institute of Aeronautics and Astronautics, Inc., with permission. All requests for copying and permission to reprint should be submitted to CCC at www.copyright.com; employ the eISSN 1533-3868 to initiate your request. See also AIAA Rights and Permissions www.aiaa.org/randp. TopicsAerodynamicsAeronautical EngineeringAeronauticsAerospace SciencesAircraft Operations and TechnologyAircraftsFlight DynamicsFlight TrainingFlying QualitiesHelicoptersRotorcraftsTurbulenceTurbulence Models KeywordsTurbulenceFrequency Response FunctionsHandling QualitiesHelicoptersFlight VehicleGaussian White NoiseMATLABDirac Delta FunctionFlight DynamicsProbability Density FunctionsPDF Received30 October 2020Accepted3 December 2020Published online21 February 2021

No AccessReplysReply by the Authors to P. J. ShermanHonglei Ji, Renliang Chen and Pan LiHonglei JiChongqing University, 400044 Chongqing, People’s Republic of China*Lecturer, College of Aerospace Engineering; (Corresponding Author).Search for more papers by this author, Renliang ChenNanjing University of Aeronautics and Astronautics, 210016 Nanjing, People’s Republic of China†Professor, National Laboratory of Science and Technology on Rotorcraft Aeromechanics, College of Aerospace Engineering, Jiangsu; (Corresponding Author).Search for more papers by this author and Pan LiNanjing University of Aeronautics and Astronautics, 210016 Nanjing, People’s Republic of China‡Associate Professor, National Laboratory of Science and Technology on Rotorcraft Aeromechanics, College of Aerospace Engineering, Jiangsu; .Search for more papers by this authorPublished Online:16 Feb 2021https://doi.org/10.2514/1.C036264SectionsRead Now ToolsAdd to favoritesDownload citationTrack citations ShareShare onFacebookTwitterLinked InRedditEmail About References [1] Sherman P. J., “Comment on ‘Analysis of Helicopter Handling Quality in Turbulence with Recursive von Kármán Model’,” Journal of Aircraft, Vol. XX, No. XX, 2020, pp. XXX–XXX. Google Scholar[2] Ji H., Chen R. and Li P., “Analysis of Helicopter Handling Quality in Turbulence with Recursive von Kármán Model,” Journal of Aircraft, Vol. 54, No. 5, 2017, pp. 1631–1639. https://doi.org/10.2514/1.C034189 LinkGoogle Scholar[3] McFarland R. E. and Duisenberg K., “Simulation of Rotor Blade Element Turbulence,” NASA TM-108862, 1995. Google Scholar[4] Ji H., Chen R. and Li P., “Distributed Atmospheric Turbulence Model for Helicopter Flight Simulation and Handling Quality Analysis,” Journal of Aircraft, Vol. 54, No. 1, 2017, pp. 190–198. https://doi.org/10.2514/1.C033667 LinkGoogle Scholar[5] Ji H., Chen R. and Li P., “Distributed Turbulence Model with Rigorous Spatial Cross-Correlation for Simulation of Helicopter Flight in Atmospheric Turbulence,” Journal of the American Helicopter Society, Vol. 64, No. 4, 2019, Paper 042011. https://doi.org/10.4050/JAHS.64.042011 CrossrefGoogle Scholar[6] Ji H., Chen R. and Li P., “Real-Time Simulation Model for Helicopter Flight Task Analysis in Turbulent Atmospheric Environment,” Aerospace Science and Technology, Vol. 92, Sept. 2019, pp. 289–299. https://doi.org/10.1016/j.ast.2019.05.066 CrossrefGoogle Scholar[7] Gaonkar G. H., “A Perspective on Modelling Rotorcraft in Turbulence,” Probabilistic Engineering Mechanics, Vol. 3, No. 1, 1988, pp. 36–42. https://doi.org/10.1016/0266-8920(88)90006-9 CrossrefGoogle Scholar[8] Brown R. G. and Hwang P. Y. C., Introduction to Random Signals and Applied Kalman Filtering, 4th ed., Wiley, Hoboken, NJ, 2012, Chaps. 1, 2. Google Scholar[9] Hess R. A. and Siwakosit W., “Assessment of Flight Simulator Fidelity in Multiaxis Tasks Including Visual Cue Quality,” Journal of Aircraft, Vol. 38, No. 4, 2001, pp. 607–614. https://doi.org/10.2514/2.2836 LinkGoogle Scholar[10] Lusardi J. A., Tischler M. B., Blanken C. L. and Labows S. J., “Empirically Derived Helicopter Response Model and Control System Requirements for Flight in Turbulence,” Journal of the American Helicopter Society, Vol. 49, No. 3, 2004, pp. 340–349. https://doi.org/10.4050/JAHS.49.340 CrossrefGoogle Scholar Previous article Next article FiguresReferencesRelatedDetails What's Popular Volume 58, Number 2March 2021 CrossmarkInformationCopyright © 2020 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved. All requests for copying and permission to reprint should be submitted to CCC at www.copyright.com; employ the eISSN 1533-3868 to initiate your request. See also AIAA Rights and Permissions www.aiaa.org/randp. TopicsAerodynamicsAeronautical EngineeringAeronauticsAircrewAviationCivil AviationCommercial AviationComputing, Information, and CommunicationSignal ProcessingTurbulenceTurbulence Models KeywordsPower Spectral DensityWhite NoiseTurbulence ModelsPilotAcknowledgmentsThe authors acknowledge the financial support of the National Natural Science Foundation of China (grant 11902052), the Chongqing Science and Technology Bureau (grant cstc2019jscx-msxmX0043), and the National Laboratory of Science and Technology on Rotorcraft Aeromechanics (grant JZX7Y201911SY004001).PDF Received30 October 2020Accepted3 December 2020Published online16 February 2021

Due to the existence of dense subarray, nested array (NA) is susceptible to mutual coupling, which seriously degrades the parameter estimation performance. To deal with this problem, we propose an improved scheme to obtain direction of arrival (DOA) and mutual coupling estimation in this paper. Specifically, a new sparse subarray can be formed by moving specific sensors of the nested array, which enables well-performed estimation free from severe mutual coupling effect. Subsequently, the contaminated steering vector of the whole non-uniform sparse array is constructed and a quadratic optimization problem is established to simultaneously eliminate DOA estimation ambiguity and estimate mutual coupling coefficients (MCCs). Numerical simulations demonstrate the superiority of the proposed scheme in terms of estimation accuracy and computation complexity.

以某汽轮机级为研究对象,采用三维非定常数值模拟的方法,研究了动叶顶部汽封泄漏涡的涡动特性和泄漏涡影响下汽封腔室内的压力脉动规律.结果 表明:叶顶汽封腔室内存在周向螺旋状涡与壁角涡组成的多尺度涡系,涡系的位置和影响范围会随时间经历复杂的变化.叶顶汽封腔室内的压力脉动频率中兼具叶轮旋转的周期性频率和溃散为小尺度涡结构后的高频率.间隙内的多尺度涡结构和动静叶间交替变化的压力场在汽封间隙内的传播是引起汽封腔室内周向压力不均的主要原因.

Optimal rotation of an unmanned aerial vehicle (UAV) mounted with a cross array for enhanced two-dimensional (2D) direction estimation is discussed, and a novel method based on Cramer–Rao bound (CRB) minimisation is proposed. CRB serves as the lower bound for direction-of-arrival (DOA) estimation, which directly determines the localisation performance of jamming sources. Based on the fact that CRB differs while UAV's rotation angle changes, a theoretical closed-form solution for an optimal rotation in the 2D direction estimation is derived, and then the authors can acquire capability enhancement of DOA estimation after the optimal rotation. Rigorous analysis and multiple simulations demonstrate the feasibility and effectiveness of the proposed scheme.

The aerodynamic environment during the flight of a helicopter is complex because of the severe aerodynamic interaction between various components. To fully consider the effect of aerodynamic interaction in the initial stages of helicopter design and to eliminate or reduce its adverse effects, a comprehensive design optimization method for the aerodynamic layout of a helicopter that is capable of reducing the adverse effects of aerodynamic interaction is developed in this paper. To satisfy the requirements for precision and efficiency in the calculation model, an aerodynamic interaction analysis model of various helicopter components was established based on a viscous vortex particle and the unsteady panel hybrid method. To simultaneously consider the influences of the position and shape of the aerodynamic components on the aerodynamic interaction during the optimization process, parameter modeling of the helicopter's shape was performed based on the class function/shape function transformation (CST) method. A Kriging surrogate model of the objective function was further developed and combined with a hybrid sequential quadratic algorithm and genetic algorithm optimization strategy to establish a comprehensive optimization flow for the aerodynamic layout of a helicopter that reduces the adverse effects of aerodynamic interaction. Verification was carried out based on a fuselage shape derived from UH-60 helicopter. The optimization results showed that the use of the comprehensive optimization method for the aerodynamic layout of a helicopter can effectively reduce the adverse effects of aerodynamic interaction. Based on the optimization objectives, the efficiency of hovering increased by 4.7%, the hovering ceiling increased by 3.48%, the speed stability derivative increased by 264.7%, and the angle of attack stability derivative decreased by 26.4%.