Theorem for the combination of bistatic radar measurements using least squares

In order to improve position finding performance, least squares method is often used to combine the measurement sets of bistatic radar system. However, which measurement sets can be combined by least squares method and which cannot have received little attention until now. We address this issue based on 2 measurements of bistatic radar system, for example (/spl rho/, /spl theta//sub T/), and present a new theorem, with proof, which shows that the least squares estimator can be obtained by combining any two measurement sets if these measurement pairs are disjoint. We provide an example that satisfies this condition, which shows that the least square combination of measurement sets yield improved performance especially in the vicinity of the transmitter and the receiver.     

An approach for machining distortion measurements and evaluation of thin-walled blades with small datum

Inspection techniques for aero-engine blades are a hot topic in industry. Since these blades have a sculptured surface and a small datum, measurement results may deviate from an actual position. There are few proper approaches compensating for non-uniform distribution errors that are within specified tolerance ranges. This study aimed to develop a meshing structure measur-ing approach for the distortion of blades via non-contact optical 3D scanning. A rough measure-ment and a registration procedure are initially adopted to rectify the coordinate system of a blade, which avoids the initial coordinate system errors caused by the small datum. A measurement path with meshing structure is then unfolded on the blade surface. For non-uniform distribution errors, the meshing structure measurement is more visual and clear than the traditional constant height curves method. All measuring points take only 7 min to complete, and the distribution of error is directly and accurately presented by the meshing structure. This study provides a basis for future research on distortion control and error compensation.     

Multi-UAV reconnaissance task allocation for heterogeneous targets using an opposition-based genetic algorithm with double-chromosome encoding

This paper presents a novel multiple Unmanned Aerial Vehicles (UAVs) reconnaissance task allocation model for heterogeneous targets and an effective genetic algorithm to optimize UAVs’ task sequence. Heterogeneous targets are classified into point targets, line targets and area targets according to features of target geometry and sensor’s field of view. Each UAV is regarded as a Dubins vehicle to consider the kinematic constraints. And the objective of task allocation is to minimize the task execution time and UAVs’ total consumptions. Then, multi-UAV reconnaissance task allocation is formulated as an extended Multiple Dubins Travelling Salesmen Problem (MDTSP), where visit paths to the heterogeneous targets must meet specific constraints due to the targets’ feature. As a complex combinatorial optimization problem, the dimensions of MDTSP are further increased due to the heterogeneity of targets. To efficiently solve this computationally expensive problem, the Opposition-based Genetic Algorithm using Double-chromosomes Encoding and Multiple Mutation Operators (OGA-DEMMO) is developed to improve the population variety for enhancing the global exploration capability. The simulation results demonstrate that OGA-DEMMO outperforms the ordinary genetic algorithm, ant colony optimization and random search in terms of optimality of the allocation results, especially for large scale reconnaissance task allocation problems.     

An adaptive fast fixed-time guidance law with an impact angle constraint for intercepting maneuvering targets

Sliding mode guidance laws based on a conventional terminal sliding mode guarantees only finite-time convergence, which verifies that the settling time is required to be estimated by selecting appropriate initial launched conditions. However, rapid convergence to a desired impact angle within a uniform bounded finite time is important in most practical guidance applications. A uniformly finite-time/fixed-time convergent guidance law means that the convergence (settling) time is predefined independently on initial conditions, that is, a closed-loop convergence time can be estimated a priori by guidance parameters. In this paper, a novel adaptive fast fixed-time sliding mode guidance law to intercept maneuver targets at a desired impact angle from any initial heading angle, with no problems of singularity and chattering, is designed. The proposed guidance law achieves system stabilization within bounded settling time independent on initial conditions and achieves more rapid convergence than those of fixed-time stable control methods by accelerating the convergence rate when the system is close to the origin. The achieved acceleration-magnitude constraints are rigorously enforced, and the chattering-free property is guaranteed by adaptive switching gains. Extensive numerical simulations are presented to validate the efficiency and superiority of the proposed guidance law for different initial engagement geometries and impact angles.     

A new SINS/GPS sensor fusion scheme for UAV localization problem using nonlinear SVSF with covariance derivation and an adaptive boundary layer

The state estimation strategy using the smooth variable structure filter(SVSF) is based on the variable structure and sliding mode concepts. As presented in its standard form with a fixed boundary layer limit, the value of the boundary layer width is not precisely known at each step and may be selected based on a priori knowledge. The boundary layer width reflects the level of uncertainty in the model parameters and disturbance characteristics, where large values of the boundary layer width lead to robustness without optimality and small values of the boundary layer width provide optimality with poor robustness. As a solution and to overcome these limitations, an adaptive smoothing boundary layer is required to achieve greater robustness and suitable accuracy.This adapted value of the boundary layer width is obtained by minimizing the trace of the a posteriori covariance matrix. In this paper, the proposed new approach will be considered as another alternative to the extended Kalman filters(EKF), nonlinear H1 and standard SVSF-based data fusion techniques for the autonomous airborne navigation and self-localization problem. This alternative is based on strapdown inertial navigation system(SINS) and GPS data using the nonlinear SVSF with a covariance derivation and adaptive boundary layer width.Furthermore, the full mathematical model of the SINS/GPS navigation system considering the unmanned aerial vehicle(UAV) position, velocity and Euler angle as well as gyro and accelerometer biases will be used in this paper to estimate the airborne position and velocity with better accuracy.