Some Results On Closed Loop Identification Of Quadcopters
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Some results on closed-loop identification of quadcopters
Author: Du Ho
language: en
Publisher: Linköping University Electronic Press
Release Date: 2018-11-21
In recent years, the quadcopter has become a popular platform both in research activities and in industrial development. Its success is due to its increased performance and capabilities, where modeling and control synthesis play essential roles. These techniques have been used for stabilizing the quadcopter in different flight conditions such as hovering and climbing. The performance of the control system depends on parameters of the quadcopter which are often unknown and need to be estimated. The common approach to determine such parameters is to rely on accurate measurements from external sources, i.e., a motion capture system. In this work, only measurements from low-cost onboard sensors are used. This approach and the fact that the measurements are collected in closed-loop present additional challenges. First, a general overview of the quadcopter is given and a detailed dynamic model is presented, taking into account intricate aerodynamic phenomena. By projecting this model onto the vertical axis, a nonlinear vertical submodel of the quadcopter is obtained. The Instrumental Variable (IV) method is used to estimate the parameters of the submodel using real data. The result shows that adding an extra term in the thrust equation is essential. In a second contribution, a sensor-to-sensor estimation problem is studied, where only measurements from an onboard Inertial Measurement Unit (IMU) are used. The roll submodel is derived by linearizing the general model of the quadcopter along its main frame. A comparison is carried out based on simulated and experimental data. It shows that the IV method provides accurate estimates of the parameters of the roll submodel whereas some other common approaches are not able to do this. In a sensor-to-sensor modeling approach, it is sometimes not obvious which signals to select as input and output. In this case, several common methods give different results when estimating the forward and inverse models. However, it is shown that the IV method will give identical results when estimating the forward and inverse models of a single-input single-output (SISO) system using finite data. Furthermore, this result is illustrated experimentally when the goal is to determine the center of gravity of a quadcopter.
Timing-Based Localization using Multipath Information
Author: Andreas Bergström
language: en
Publisher: Linköping University Electronic Press
Release Date: 2020-01-09
The measurements of radio signals are commonly used for localization purposes where the goal is to determine the spatial position of one or multiple objects. In realistic scenarios, any transmitted radio signal will be affected by the environment through reflections, diffraction at edges and corners etc. This causes a phenomenon known as multipath propagation, by which multiple instances of the transmitted signal having traversed different paths are heard by the receiver. These are known as Multi-Path Components (MPCs). The direct path (DP) between transmitter and receiver may also be occluded, causing what is referred to as non-Line-of-Sight (non-LOS) conditions. As a consequence of these effects, the estimated position of the object(s) may often be erroneous. This thesis focuses on how to achieve better localization accuracy by accounting for the above-mentioned multipath propagation and non-LOS effects. It is proposed how to mitigate these in the context of positioning based on estimation of the DP between transmitter and receiver. It is also proposed how to constructively utilize the additional information about the environment which they implicitly provide. This is all done in a framework wherein a given signal model and a map of the surroundings are used to build a mathematical model of the radio environment, from which the resulting MPCs are estimated. First, methods to mitigate the adverse effects of multipath propagation and non-LOS conditions for positioning based on estimation of the DP between transmitter and receiver are presented. This is initially done by using robust statistical measurement error models based on aggregated error statistics, where significant improvements are obtained without the need to provide detailed received signal information. The gains are seen to be even larger with up-to-date real-time information based on the estimated MPCs. Second, the association of the estimated MPCs with the signal paths predicted by the environmental model is addressed. This leads to a combinatorial problem which is approached with tools from multi-target tracking theory. A rich radio environment in terms of many MPCs gives better localization accuracy but causes the problem size to grow large—something which can be remedied by excluding less probable paths. Simulations indicate that in such environments, the single best association hypothesis may be a reasonable approximation which avoids the calculation of a vast number of possible hypotheses. Accounting for erroneous measurements is crucial but may have drawbacks if no such are occurring. Finally, theoretical localization performance bounds when utilizing all or a subset of the available MPCs are derived. A rich radio environment allows for good positioning accuracy using only a few transmitters/receivers, assuming that these are used in the localization process. In contrast, in a less rich environment where basically only the DP/LOS components are measurable, more transmitters/receivers and/or the combination of downlink and uplink measurements are required to achieve the same accuracy. The receiver’s capability of distinguishing between multiple MPCs arriving approximately at the same time also affects the localization accuracy.
Direction of Arrival Estimation for Wildlife Protection
Author: Gustav Zetterqvist
language: en
Publisher: Linköping University Electronic Press
Release Date: 2024-10-03
Direction of arrival (DOA) estimation is a well-established problem in signal processing. It involves determining the direction from which a signal reaches a sensor array, and is fundamental in applications like radar, sonar, and acoustics. Traditionally, DOA estimation relies on comparing the time of arrival of the signal across different sensors in the array. However, this approach is sensitive to the time difference of arrival (TDOA) between sensors, which can be challenging to estimate accurately. Additionally, precise synchronization among the sensors is essential, but this can be difficult to achieve in certain environments or applications. In this thesis, we explore a novel approach to DOA estimation based on the received signal power at the sensors. The method exploits the directional sensitivity of the microphones in the array, which defines how effectively each microphone captures sound from different directions. To model the directional sensitivity, we use a Fourier series (FS) model. The model is then used to estimate the DOA of a sound source across various environments, and for different types of signals. The parametric model enables Cramér-Rao lower bound (CRLB) analysis of the DOA estimation problem. Our findings demonstrate that the directional sensitivity exhibits a significant variation in accordance with the frequency content of the signal, and we exploit this to estimate the DOA for different types of sounds. The proposed method has been validated with a range of signals, including gunshots, elephant trumpets, sirens, and female screams. The results show that the developed method achieves high accuracy in estimating the DOA for the above-mentioned signals. Furthermore, the method performs similarly well in outdoor scenarios with realistic background noise levels. When compared to state-of-the-art DOA estimation techniques, our approach performs better or equally well for the investigated sounds. A key advantage of this method is that it does not require any TDOA measurement between the microphones, enabling the design of smaller, more compact devices. This opens up new possibilities for estimating DOA in environments where traditional methods are impractical. A limitation, however, is that the method requires knowledge of the microphone’s directional sensitivity, which necessitates calibration in an anechoic chamber. Nevertheless, this calibration has proven to be robust, and only needs to be performed once to create a model applicable across different environments. Additionally, this thesis explores a different application of DOA estimation, where geophones are used to estimate the DOA to elephants. As elephants move, they generate ground vibrations, and these signals can be captured by geophones. We show that a traditional delay-and-sum beamformer can accurately estimate the DOA of elephants at distances up to 40 meters. By determining when elephants are approaching and from which direction, park rangers can take early measures to avoid conflicts between humans and elephants, which is a major problem in some parts of the world. Förmågan att höra var ett ljud kommer ifrån, något vi ofta tar för givet, kallas för riktningsuppfattning. Den gör det möjligt för oss att snabbt avgöra om någon ropar på oss och från vilket håll ljudet kommer. Denna förmåga är viktig för att kunna orientera sig i omgivningen och uppfatta hot eller andra viktiga ljud. Våra öron samarbetar genom att jämföra hur ljud når varje öra, både när det gäller ljudets intensitet och hur lång tid det tar för ljudet att nå dem. Det här kallas för interaural tids- och nivåskillnad. Vissa ljud kan dock vara svåra att uppfatta, till exempel om ljudet är kort och impulsivt, eller om det är i en stadsmiljö med mycket bakgrundsljud och reflektioner. I den här avhandlingen undersöker vi nya metoder för att uppskatta ljudets riktning. Vi använder mikrofoner för att mäta ljudet och beräknar därefter riktningen som ljudet kommer ifrån. Traditionella metoder fokuserar på tidsskillnaden mellan ljud som registreras i olika mikrofoner. Vi tar istället en annan väg och undersöker hur ljudets styrka kan användas för att avgöra riktningen, oavsett tidsskillnader mellan mikrofonerna. Vår metod bygger på att vi skapar en modell av mikrofonernas riktningskänslighet, det vill säga hur väl de uppfattar ljud från olika håll. Modellen skapas genom att mäta mikrofonens riktningskänslighet i ett ekofritt rum. Genom att först mäta detta i en kontrollerad miljö, utan ekon, kan vi sedan använda modellen för att beräkna ljudriktningen i mer varierande miljöer och för olika typer av ljud. Till exempel har vi använt ljud såsom pistolskott, elefanttrumpeter, sirener och skrik för att testa vår metod. Resultaten visar att vår metod kan beräkna riktningar med hög noggrannhet för de ovan nämnda ljuden, även i en utomhusmiljö med mer realistiska nivåer av bakgrundsljud. När vi jämfört vår metod med traditionella metoder, presterar vår lösning lika bra eller bättre för de testade ljuden. En stor fördel med vår metod är att den inte kräver att mikrofonerna är placerade på ett visst avstånd från varandra, vilket innebär att vi kan bygga mindre och mer kompakta enheter. Detta kan leda till nya typer av produkter för att identifiera ljudriktningar i olika situationer. En nackdel är dock att mikrofonernas riktningskänslighet måste kalibreras i ett ljudlabb, men denna kalibrering har visat sig vara robust och det räcker att utföra en kalibrering som kan användas i flera olika miljöer. I avhandlingen inkluderas även en annan tillämpning av riktningsskattning, nämligen att uppskatta riktningen till elefanter med hjälp av geofoner som mäter vibrationer i marken. Elefanter är stora djur som skapar tydliga vibrationer i marken när de går. Genom att mäta dessa vibrationer med geofoner kan vi uppskatta riktningen till elefanten. Vi visar att traditionella metoder kan uppskatta riktningen med hög noggrannhet på ett avstånd upp till 40 meter. Genom att avgöra när elefanter närmar sig människor och varifrån de kommer kan parkvakter vidta åtgärder för att undvika konflikter mellan människor och elefanter, vilket är ett stort problem i vissa delar av världen.