Multi-Objective Optimisation under Uncertainty with Unscented Temporal Finite Elements

This paper presents a novel method for multi-objective optimisation under uncertainty developed to study a range of mission trade-offs, and the impact of uncertainties on the evaluation of launch system mission designs. A memetic multi-objective optimisation algorithm, named MODHOC, which combines the Direct Finite Elements in Time transcription method with Multi Agent Collaborative Search, is extended to account for model uncertainties. An Unscented Transformation is used to capture the first two statistical moments of the quantities of interest. A quantification model of the uncertainty was developed for the atmospheric model parameters. An optimisation under uncertainty was run for the design of descent trajectories for a spaceplane-based two-stage launch system.

An Experimental Multi-Target Tracking of AM Radio-Based Passive Bistatic Radar System via Multi-Static Doppler Shifts

This paper presents a description of recent research and the multi-target tracking in experimental passive bistatic radar (PBR) system taking advantage of numerous non-cooperative AM radio signals via multi-static doppler shifts. However, it raises challenges for use by multiple spatially distributed AM radio illuminators for multi-target tracking in PBR system due to complex data association hypotheses and no directly used tracking algorithm in the practical scenario. To solve these problems, after a series of key array signal processing techniques in the self-developed system, by constructing a nonlinear measurement model, the novel method is proposed to accommodate nonlinear model by using the unscented transformation (UT) in Gaussian mixture (GM) implementation of iterated-corrector cardinality-balanced multi-target multi-Bernoulli (CBMeMBer). Simulation and experimental results analysis verify the feasibility of this approach used in a practical PBR system for moving multi-target tracking.

Positioning Parameter Determination Based on Statistical Regression Applied to Autonomous Underwater Vehicle

The underwater environment is complex and changeable, and it is hard but irreplaceable to research the time-varying noises that have a significant influence on navigation information determination with higher accuracy. To solve the problems of the inaccurate noise information, this paper proposes a novel statistical regression adaptive Kalman filtering (SRAKF) algorithm that makes better use of the merits of the expectation maximization and unscented transformation. The SRAKF is verified from theoretical perspectives, and meanwhile, the stability and accuracy of the algorithm are evaluated by real lake trials. Relying on the properties of the statistical linear regression and the positioning parameter estimation of latent variables, higher precise positioning parameters can be acquired by the SRAKF, even for the measurement noise values with great variation. Hence, the performance of SRAKF is more useful in underwater positioning applications than other traditional algorithms due to its stronger robustness and higher accuracy.

A Sampling-Based Sensitivity Analysis Method Considering the Uncertainties of Input Variables and Their Distribution Parameters

For engineering products with uncertain input variables and distribution parameters, a sampling-based sensitivity analysis methodology was investigated to efficiently determine the influences of these uncertainties. In the calculation of the sensitivity indices, the nonlinear degrees of the performance function in the subintervals were greatly reduced by using the integral whole domain segmentation method, while the mean and variance of the performance function were calculated using the unscented transformation method. Compared with the traditional Monte Carlo simulation method, the loop number and sampling number in every loop were decreased by using the multiplication approximation and Gaussian integration methods. The proposed algorithm also reduced the calculation complexity by reusing the sample points in the calculation of two sensitivity indices to measure the influence of input variables and their distribution parameters. The accuracy and efficiency of the proposed algorithm were verified with three numerical examples and one engineering example.

ILRS Reference Point Determination Using Close Range Photogrammetry

A global geodetic reference system (GGRS) is realized by physical points on the Earth’s surface and is referred to as a global geodetic reference frame (GGRF). The GGRF is derived by combining several space geodetic techniques, and the reference points of these techniques are the physical points of such a realization. Due to the weak physical connection between the space geodetic techniques, so-called local ties are introduced to the combination procedure. A local tie is the spatial vector defined between the reference points of two space geodetic techniques. It is derivable by local measurements at multitechnique stations, which operate more than one space geodetic technique. Local ties are a crucial component within the intertechnique combination; therefore, erroneous or outdated vectors affect the global results. In order to reach the ambitious accuracy goal of 1 mm for a global position, the global geodetic observing system (GGOS) aims for strategies to improve local ties, and, thus, the reference point determination procedures. In this contribution, close range photogrammetry is applied for the first time to determine the reference point of a laser telescope used for satellite laser ranging (SLR) at Geodetic Observatory Wettzell (GOW). A measurement campaign using various configurations was performed at the Satellite Observing System Wettzell (SOS-W) to evaluate the achievable accuracy and the measurement effort. The bias of the estimates were studied using an unscented transformation. Biases occur if nonlinear functions are replaced and are solved by linear substitute problems. Moreover, the influence of the chosen stochastic model onto the estimates is studied by means of various dispersion matrices of the observations. It is shown that the resulting standard deviations are two to three times overestimated if stochastic dependencies are neglected.