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This chapter opens with a brief history of estimation from astronomy, navigation at sea, and space exploration. It defines the problem of estimation and gives some modern sensor fusion examples. A description of how the book is organized and how to read it is provided. The book is compared to other great volumes on estimation and robotics in order to understand how it fits into the larger landscape.
In fighter pilot training, much of upgrade pilots’ (UPs’) learning takes place during mission debriefs. A debrief provides instructor pilots (IPs) the opportunity to correct situation awareness (SA) upon which the UPs base their tactical decisions. Unless the debrief is conducted with proper depth and breadth, the IPs’ feedback on UPs’ SA and tactical decision-making may be incomplete or false, resulting in poor, or even negative learning. In this study, a new debrief protocol based on the Critical Decision Method (CDM) is introduced. The protocol specifically addresses the SA of UPs. An evaluation was conducted to examine if a short CDM training programme to IPs would enhance their ability to provide performance feedback to UPs regarding their SA and tactical decision-making. The IPs were qualified flying instructors and the UPs were air force cadets completing their air combat training with BAe Hawk jet trainer aircraft. The impact of the training intervention was evaluated using Kirkpatrick’s four-level model. The first three levels of evaluation (Reactions, Learning and Behaviour) focused on the IPs, whereas the fourth level (Results) focused on the UPs. The training intervention had a positive impact on the Reactions, Learning and debrief Behaviour of the IPs. In air combat training missions, the UPs whose debriefs were based on the CDM protocol, had superior SA and overall performance compared to a control group.
Aerodynamic design of a high-efficiency two-stage axial turbine is carried out using a hybrid method through implantation of a two-step design procedure. In the first step, the well-known streamline curvature (SLC) and free vortex (FV) methods are properly combined to establish three-dimensional geometries of the blades at each row and to obtain the flow field properties. The second step is provided to obtain the highest aerodynamic efficiency by optimum clocking of the second stator blades relative to the first ones through executing steady and unsteady computational fluid dynamics (CFD) of three-dimensional viscous flow. Slight discrepancies were observed between gas dynamics results of the SLC and those of CFD. Total pressure and temperature at the turbine outlet, obtained from SLC method, differed from those obtained by 3D-CFD technique by 13.06% and 1.88% respectively. Aerodynamic efficiency of the turbine is obtained about 91.83%, based on 3D-CFD. Time-averaged results showed that under the optimum clocking of the second row stator blades, inlet total pressure and output power of the second rotor increase by 0.23%, and 0.93%, respectively, in comparison to the worst clocking case. These augmentations resulted in increased total to total efficiency of the second stage by 0.444%. Additionally, the total output power of the two stages increased by 0.71% through the optimum clocking. Modeling the unsteady wake flow trajectory within the blades passages confirmed that all of these beneficial effects happen if the upstream wake impinges on the leading edge region of the second stator blades.
This appendix is a collection of topics that were slightly peripheral to the main flow of the book, but still potentially interesting to some readers. The derivations of Fisher's information matrix in several forms as well as Stein's lemma are both important tools employed in the main parts of the book.
The characterisation and the modelling of air concentration distributions in self-aerated free-surface flows has been subject to sustained research interest since the 1970s. Recently, a novel two-state formulation of the structure of a self-aerated flow was proposed by Kramer & Valero (J. Fluid Mech., vol. 966, 2023, A37), which physically explains the air concentration through the weak interaction of two canonical flow momentum layers, comprising a turbulent boundary layer and a turbulent wavy layer (TWL). The TWL was modelled using a Gaussian error function, assuming that the most dominant contribution are wave troughs. Here, it is shown that air bubbles form an integral part of the TWL, and its formulation is expanded by adopting a superposition principle of entrapped air (waves) and entrained air (bubbles). Combining the superposition principle with the two-state formulation, an expression for the depth-averaged (mean) air concentration is derived, which allows us to quantify the contribution of different physical mechanisms to the mean air concentration. Overall, the presented concepts help to uncover new flow physics, thereby contributing fundamentally to our understanding of self-aerated flows.
With both our estimation and Lie group tools from previous chapters, we now begin to bring the two together. We discuss a classic three-dimensional estimation problem in robotics: pointcloud alignment; this gives us our first example of carrying out optimization over the group of rotations by a few different means. We then present the classic problem of localizing a moving robot using point observations of known three-dimensional landmarks; this involves adapting the extended Kalman filter (EKF) to work with the group of poses. Another common problem in robotics is that of pose-graph optimization, which is easily handled using our Lie group tools. We conclude with a presentation of how to carry out trajectory estimation based on an inertial measurement unit (IMU) both recursively via the EKF and batch using IMU preintegration for efficiency.
This chapter is devoted to the classic simultaneous localization and mapping (SLAM) problem and the related problem, bundle adjustment. In these problems we must estimate not only the trajectory of a robot but also the three-dimensional positions of many point landmarks, based on noisy sensor data and a motion model (in the case of SLAM). We discuss how to adapt the tools presented earlier to include landmarks in the state; the inclusion of landmarks changes the sparsity pattern of the resulting estimation equations and we discuss strategies of continuing to solve them efficiently. Our approach is carried out entirely in three dimensions using our Lie group tools.
As the book attempts to be as stand-alone as possible, this chapter provides up front a summary of all the results in probability theory that will be needed later on. Probability is key to estimation as we not only want to estimate, for example, where something is but how confident we are in that estimate. The first half of the chapter introduces general probability density functions, Bayes' theorem, the notion of independence, and quantifying uncertainty amongst other topics. The second half of the chapter delves into Gaussian probability density functions specifically and establishes the key tools needed in common estimation algorithms to follow in later chapters. This chapter can also simply serve as a reference for readers already familiar with the content.
The free-surface channel flow around a square cylinder is analysed, over a wide range of blocking ratios, using three-dimensional simulations. The state of the flow is characterised in terms of the Froude number upstream and downstream of the square cylinder. The simulations confirm the presence of the subcritical and choked states, and provide new insight into the supercritical state and band-gap through an analysis of how the momentum flux varies with Froude number along the channel. The influence of the blocking ratio on the flow state and drag force is analysed and shows the significant rise of drag in the choked regime.
Floating objects will drift due to the action of surface gravity waves. This drift will depart from that of a perfect Lagrangian tracer due to both viscous effects (non-potential flow) and wave–body interaction (potential flow). We examine the drift of freely floating objects in regular (non-breaking) deep-water wave fields for object sizes that are large enough to cause significant diffraction. Systematic numerical simulations are performed using a hybrid numerical solver, qaleFOAM, which deals with both viscosity and wave–body interaction. For very small objects, the model predicts a wave-induced drift equal to the Stokes drift. For larger objects, the drift is generally greater and increases with object size (we examine object sizes up to $10\,\%$ of the wavelength). The effects of different shapes, sizes and submergence depths and steepnesses are examined. Furthermore, we derive a ‘diffraction-modified Stokes drift’ akin to Stokes (Trans. Camb. Phil. Soc., vol. 8, 1847, pp. 411–455), but based on the combination of incident, diffracted and radiated wave fields, which are based on potential-flow theory and obtained using the boundary element method. This diffraction-modified Stokes drift explains both qualitatively and quantitatively the increase in drift. Generally, round objects do not diffract the wave field significantly and do not experience a significant drift enhancement as a result. For box-shape objects, drift enhancement is greater for larger objects with greater submergence depths (we report an increase of $92\,\%$ for simulations without viscosity and $113\,\%$ with viscosity for a round-cornered box whose size is $10\,\%$ of the wavelength). We identify the specific standing wave pattern that arises near the object because of diffraction as the main cause of the enhanced drift. Viscosity plays a small positive role in the enhanced drift behaviour of large objects, increasing the drift further by approximately $20\,\%$.
A multi-band circularly polarized antenna is proposed for WLAN (2.4/5.3/5.8 GHz) and WiMAX (3.5 GHz) applications. The proposed antenna is constructed of a radiation patch and a reflecting metal ground. Characteristic mode theory is utilized to analyze the modes of the patch and based on these results the antenna is optimized. The −10 dB impedance bandwidths of the proposed antenna are 53.53% (2.4–4.15 GHz) and 47.28% (5.25–8.5 GHz), respectively. The antenna radiates left-handed circular polarization in the lower band and right-handed circular polarization in the upper band. A maximum gain of 10 dBic is achieved for the proposed antenna.