密林环境空地协同GNSS/UWB快速高精度定位技术

针对密林中卫星信号遮挡难以实现快速高精度定位的问题,提出了一种密林环境空地协同全球卫星导航系统/超宽带(GNSS/UWB)高精度定位方法。该方法综合考虑无人机平台在空旷环境快速运动与超宽带的强穿透性测量等特征,通过无人机携带GNSS/UWB集成化载荷,以移动单基站模拟多基站,配合密林中UWB标签组网测距,完成密林中UWB标签定位。对基站布设方案展开研究,为密林环境下空地协同GNSS/UWB快速高精度定位技术应用提供基站布设指导和依据,并且利用密林环境下实测测距实验+仿真定位实验对提出的方法进行验证。结果表明,所述方法可有效实现密林环境下的快速高精度定位,精度达到分米级,并且通过优选基站布设网型、范围及高度可有效提高定位精度,为密林环境下快速高精度定位方法提供了理论支撑。     

Dynamic Cramer-Rao bound for target tracking in clutter

Recently, there have been several new results for an old topic, the Cramer-Rao lower bound (CRLB). Specifically, it has been shown that for a wide class of parameter estimation problems (e.g. for objects with deterministic dynamics) the matrix CRLB, with both measurement origin uncertainty (i.e., in the presence of false alarms or random clutter) and measurement noise, is simply that without measurement origin uncertainty times a scalar information reduction factor (IRF). Conversely, there has arisen a neat expression for the CRLB for state estimation of a stochastic dynamic nonlinear system (i.e., objects with a stochastic motion); but this is only valid without measurement origin uncertainty. The present paper can be considered a marriage of the two topics: the clever Riccati-like form from the latter is preserved, but it includes the IRF from the former. The effects of plant and observation dynamics on the CRLB are explored. Further, the CRLB is compared via simulation to two common target tracking algorithms, the probabilistic data association filter (PDAF) and the multiframe (N-D) assignment algorithm.     

面向城市飞行安全的无人机离散型多路径规划方法

为了提高无人机（UAV）在城市环境中运行的安全性,且能生成多条备选路径,提出一种离散型城市环境下基于无人机飞行安全的多路径规划方法。根据定义的城市环境模型、无人机的飞行规则和安全性原则,建立无人机飞行安全性分析模型和离散型多路径规划问题的数学模型。为提高算法的收敛速度和解的优质性,以及使算法能够同时输出多条路径,针对蚁群（ACO）算法的运行机制,设计聚类算子,提出改进聚类蚁群（CIACO）算法。实验结果表明,所提方法能够快速的收敛输出多条风险值较低的飞行路径。     

Highly reliable relative navigation for multi-UAV formation flight in urban environments

Formation flight of multiple Unmanned Aerial Vehicles (UAVs) is expected to bring significant benefits to a wide range of applications. Accurate and reliable relative position information is a prerequisite to safely maintain a fairly close distance between UAVs and to achieve inner-system collision avoidance. However, Global Navigation Satellite System (GNSS) measurements are vulnerable to erroneous signals in urban canyons, which could potentially lead to catastrophic consequences. Accordingly, on the basis of performing relative positioning with double differenced pseudoranges, this paper develops an integrity monitoring framework to improve navigation integrity (a measure of reliability) in urban environments. On the one hand, this framework includes a fault detection and exclusion scheme to protect against measurement faults. To accommodate urban scenarios, spatial dependence in the faults are taken into consideration by this scheme. On the other hand, relative protection level is rigorously derived to describe the probabilistic error bound of the navigation output. This indicator can be used to evaluate collision risk and to warn collision danger in real time. The proposed algorithms are validated by both simulations and flight experiments. Simulation results quantitatively reveal the sensitivity of navigation performance to receiver configurations and environmental conditions. And experimental results suggest high efficiency and effectiveness of the new integrity monitoring framework.     

Robust space-time adaptive processing for airborne radar in nonhomogeneous clutter environments

Space-time adaptive processing (STAP) holds tremendous potential for the new generation airborne surveillance radar, in which the phased array antennas and pulse Doppler processing mode are adopted. A new STAP approach using the multiple-beam and multiple Doppler channels is presented here for airborne phased array radar. The approach with space-time multiple-beam (STMB) architecture is robust to array errors and has very low system degrees of freedom (DOFs). Hence, it has low sample support requirement and it is very suitable for the practical planar phased array radar under nonhomogeneous clutter environments. Meanwhile, a new nonhomogeneous detector (NHD) based on the correlation dimension (CD) is also proposed here, which is used as an effective method to screen tracing data prior to detection processing. It can further improve the performance of the STAP approach in the severely nonhomogeneous clutter environments. Therefore, a scheme that incorporates the correlation dimension nonhomogeneity detector (CD-NHD) with the STMB is recommended, which we term CD-NHD-STMB. The experimental simulation results indicate that: 1) the STMB processor is robust to array element error and has high performance under nonhomogeneous clutter environments; 2) the CD-NHD is also effective on the nonhomogeneous clutter. As a result, the CD-NHD-STMB scheme is robust to array element error and nonhomogeneous clutter, and therefore available for airborne phased array radar applications.     

舵机电位器的反馈位置误差补偿方法研究

舵机因电位器基准电压线性误差以及负载效应非线性误差等因素产生反馈位置误差,易造成导弹在发射、大机动飞行时控制效率降低,甚至影响到控制系统回路的稳定性。在电位器等效电路模型的基础上,对两类误差产生机理展开了深入研究,提出了一种舵机电位器的反馈位置误差补偿方法,实现了舵面偏角的有效修正。实验结果验证了该反馈位置误差补偿方法的正确性和有效性。     

Local node selection for localization in a distributed sensor network

This paper discusses a new localized resource manager for a wireless sensor network of bearings-only sensors. Specifically, each node uses knowledge of the target under surveillance to determine whether it should actively collect measurements and how far to disseminate the data in order for the sensor network to maintain track of the target. At each node, the resource manager requires only knowledge of the relative location to the target for itself and the active nodes from the previous snapshot. The decentralized strategy represents a modification to the global node selection (GNS) method that exploits knowledge of the location of all nodes in the network. Simulations show that despite the lack of global network knowledge, the new localized management method is almost as effective as GNS in terms of balancing the tradeoff between energy usage and localization accuracy.     

Global node selection for localization in a distributed sensor network

This work considers the problem of selecting the best nodes for localizing (in the mean squared (MS) position error sense) a target in a distributed wireless sensor network. Each node consists of an array of sensors that are able to estimate the direction of arrival (DOA) to a target. Different computationally efficient node selection approaches that use global network knowledge are introduced. Performance bounds based on the node/target geometry are derived, and these bounds help to determine the necessary communication reach of the active nodes. The resulting geolocation performance and energy usage, based on communication distance, is evaluated for a decentralized extended Kalman filter (EKF) that is exploiting the different selection approaches.     

机器人自动制孔中绝对定位误差的分析与补偿

由于机器人绝对定位精度相对较低,无法直接满足自动制孔的孔位精度要求。为了提高机器人自动制孔的孔位精度,对机器人绝对定位误差进行了研究。首先,阐述了绝对定位误差的来源和产生过程,并通过理论分析和相关试验,证明了绝对定位误差会对机器人基坐标系的平移分量和姿态变换分量产生不同程度的影响。然后,为了补偿由于基坐标系标定不准确所引起的坐标转换误差,从飞机曲面构造原理角度,提出了一种基于误差Coons曲面函数的补偿方法。制孔试验表明,采用基于误差Coons曲面函数的补偿方法,可以使得坐标转换误差得到有效的补偿。机器人自动制孔的孔位平均位置误差为0.205mm,最大位置误差为0.343mm,满足孔位精度在0.5mm以内的要求,实现了机器人自动化精确制孔。     

Installation error calibration for MEMS angular measurement system in hypersonic wind tunnel

The accuracy of model attitude measurement has an important impact on wind tunnel test results. Microelectromechanical System Inertial Measurement Unit (MEMS IMU) provides a feasible way to measure model attitudes with high accuracy. However, the installation error between MEMS IMU coordinate system and the body coordinate system of test models can make the accuracy of the model attitude measurement decrease. In wind tunnel tests, the installation error depends on the relationship between the IMU and the model mechanism before tests. Therefore, in-field calibration in wind tunnel tests is necessary to reduce installation errors. To improve attitude measurement accuracy, the least squares quaternion calibration method based on MEMS IMU and six-position calibration procedure are proposed. High-precision three-axis turntable tests are performed. The pitch accuracy after calibration is higher than that before calibration in the angle of attack sweeping tests. The Root-Mean-Square Errors (RMSE) in the roll and yaw are within 0.01°, which are smaller than those before calibration. In the roll sweeping tests, RMSE of three attitude angles decrease significantly. In hypersonic wind tunnel tests, the pitch errors before and after calibration are within 0.05° and 0.02° in the angle of attack sweeping tests without wind. In five angle of attack sweeping tests with wind, the deviation between the mean of the pitch and the pitch after the elastic angle correction is within 0.03° and the standard deviation of five tests is within 0.01°. The proposed method is confirmed to enhance the accuracy of attitude measurement effectively, which is convenient for engineering applications.     

含误差的齿面接触分析初始点确定算法

研究含误差的螺旋锥齿轮齿面接触分析中初值的选择与确定问题,基于几何等量关系,计算出离初始啮合点P较近的一点P',计算P'的过程不涉及两个齿面在安装坐标系下的旋转等操作,相比直接计算P所求解的方程简单,然后用迭代法计算出初始啮合点P.计算实例表明新方法对于迭代初值的敏感性下降,对迭代初值要求降低,提高了误差齿面接触分析(ETCA)的计算稳定性与效率,该方法使得ETCA的算法更加的实用、计算中初值的选取更加简单.      

美国展开未来航空科技领先地位保卫战

自二战结束以来,美国在世界航空技术领域一直是无可争议的领先者. 然而,美国在世界航空技术领域的霸主地位正在受到来自联合起来的欧洲的挑战.欧盟国家为与美国航空工业巨头进行抗衡已抛开欧洲各国间存在的贸易壁垒和保护机制,正联合起来探索新的航空技术和解决方案.     

相控阵雷达搜索方式下波位编排与优化

分析不同波位编排方式对相控阵雷达能量需求与搜索性能的影响,给出了搜索方式下较为优化的波位编排方式。然后在分析相控阵雷达波束展宽效应的基础上,提出了非均匀划分扫描空域并对波束展宽效应进行补偿的方法,对波位编排方式进一步优化。最后利用该方法仿真分析了波束宽度与天线增益的变化,结果表明可有效补偿波束展宽效应带来的影响。     

A distributed approach for lidar-based relative state estimation of multi-UAV in GPS-denied environments

In this paper, we present a distributed framework for the lidar-based relative state estimator which achieves highly accurate, real-time trajectory estimation of multiple Unmanned Aerial Vehicles(UAVs) in GPS-denied environments. The system builds atop a factor graph, and only onboard sensors and computing power are utilized. Benefiting from the keyframe strategy, each UAV performs relative state estimation individually and broadcasts very partial information without exchanging raw data. The com...     

准双曲面齿轮冷摆辗方案回弹误差补偿

提出一种冷摆辗方案,该方案简化了模具结构,采用局部线接触连续塑性成形.采用位移修正算法构建回弹误差补偿迭代系统,对冷摆辗模具进行修正来降低回弹误差.该系统的特点是首先用修正算法预估下一次冷摆辗假设无回弹时工件的几何形状,然后由冷摆辗模具设计原理推出所需要的新冷摆辗模具.基于弹塑性有限元法基本理论,借助有限元数值模拟技术进行实例验证,分析结果证明:该回弹误差补偿迭代系统只需3次迭代工件误差已经在允许的范围内,因此其可大大的缩短生产周期和成本.      

Probability of error for BPSK modulation from electrostaticinterference [in orbital situation]

In an orbital situation any spacecraft is bombarded by charged particles that span a broad range of energies. Some of the particles may be trapped within the materials of the spacecraft. Recent data indicates that an S-band phased-array antenna on a spacecraft may experience electrostatic discharges from such ion deposition at the elements of the array. These discharges may induce RF interference of sufficient magnitude to degrade transponder performance. This issue is addressed by considering the degradation to a binary phase shift keying (BPSK) signal. A simple model for the electrostatic phenomenon is described, and error probabilities are evaluated. The discharges and induced pulse rates are shown to be well below the interference level     

A practical filter error method for aerodynamic parameter estimation of aircraft in turbulence

It is common for aircraft to encounter atmospheric turbulence in flight tests. Turbulence is usually modeled as stochastic process noise in the flight dynamics equations. In this paper, parameter estimation of nonlinear dynamic system with both process and measurement noise was studied, and a practical filter error method was proposed. The linearized Kalman filter of first-order approximation was used for state estimation, in which the filter gain, along with the system parameters and the initial states, constituted the parameter vector to be estimated. The unknown parameters and measurement noise covariance were estimated alternately by a relaxation iteration method, and the sensitivities of observations to unknown parameters were calculated by finite difference approximation. Some practical aspects of the method application were discussed. The proposed filter error method was validated by the flight simulation data of a research aircraft. Then, the method was applied to the flight tests of a subscale aircraft, and the aerodynamic stability and control derivatives were estimated. All the estimation results were compared with the results of the output error method to demonstrate the effectiveness of the approach. It is shown that the filter error method is superior to the output error method for flight tests in atmospheric turbulence.     

基于模型辨识的数控机床专用伺服电机位置控制

数控机床精密加工要求伺服电机具有精度高和响应快等特性,而传统的伺服控制器位置环采用的是简单的误差比例(P)控制,这种控制方法的响应性不能满足加工要求。提出一种基于模型辨识的伺服电机角位移控制方法。首先,采用最小二乘法辨识出传统闭环控制系统的模型;然后,分析模型的零、极点,根据模型特性设计补偿控制器;最后,通过试验结果说明所提方法能快速、准确地跟随系统输入。     

Imaginary filtered hindsight experience replay for UAV tracking dynamic targets in large-scale unknown environments

As an advanced combat weapon, Unmanned Aerial Vehicles (UAVs) have been widely used in military wars. In this paper, we formulated the Autonomous Navigation Control (ANC) problem of UAVs as a Markov Decision Process (MDP) and proposed a novel Deep Reinforcement Learning (DRL) method to allow UAVs to perform dynamic target tracking tasks in large-scale unknown environments. To solve the problem of limited training experience, the proposed Imaginary Filtered Hindsight Experience Replay (IFHER) generates successful episodes by reasonably imagining the target trajectory in the failed episode to augment the experiences. The well-designed goal, episode, and quality filtering strategies ensure that only high-quality augmented experiences can be stored, while the sampling filtering strategy of IFHER ensures that these stored augmented experiences can be fully learned according to their high priorities. By training in a complex environment constructed based on the parameters of a real UAV, the proposed IFHER algorithm improves the convergence speed by 28.99% and the convergence result by 11.57% compared to the state-of-the-art Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. The testing experiments carried out in environments with different complexities demonstrate the strong robustness and generalization ability of the IFHER agent. Moreover, the flight trajectory of the IFHER agent shows the superiority of the learned policy and the practical application value of the algorithm.