Direction of arrival (DOA) estimation is a fundamental problem in signal processing and has applications in various fields such as radar, sonar, and acoustics. In this article, we propose a method for DOA estimation using the received power at each sensor. The method is based on the directional sensitivity of the sensor elements at various frequencies. We model the directional sensitivity using a Fourier series (FS) model, where the parametric model enables Cramér-Rao lower-bound (CRLB) computations. The FS model is estimated from measurements of a wideband noise signal. To estimate the DOA, the measured power profile is compared to the FS model using the least-squares (LS) method. The proposed power-based method offers several advantages over classical time-delay methods, particularly in allowing arbitrarily small arrays and still handling broadband signals. Additionally, it enables low-rate sampling, which simplifies hardware requirements and significantly reduces processor load. In numerical evaluations with a microphone array and natural sound sources, we still benchmark our method against state-of-the-art time-delay methods. Real-world experiments show promising results, performing on par with the best of the other evaluated methods for all natural signals, despite relying on significantly less information. A key benefit is robustness against array size limitations. By utilizing the received signal power instead of time delays or phase information, the method enables small arrays with great DOA resolution. Furthermore, outdoor data collected a year after calibration confirms its robustness, demonstrating consistent performance over time.

Road roughness significantly affects vehicle vibrations and ride quality. We introduce a Kalman filter (KF)-based method for estimating road roughness in terms of the international roughness index (IRI) by fusing inertial and speed measurements, offering a cost-effective solution for pavement monitoring. The method involves system identification on a physical vehicle to estimate realistic model parameters, followed by KF-based reconstruction of the longitudinal road profile to compute IRI values. It explores IRI estimation using vertical and lateral vibrations, the latter more common in modern vehicles. Validation on 230 km of real-world data shows promising results, with IRI estimation errors ranging from 1% to 10% of the reference values. However, accuracy deteriorates significantly when using only lateral vibrations, highlighting their limitations. These findings demonstrate the potential of KF-based estimation for efficient road roughness monitoring.

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.  

As human settlement expands into the natural habitats of wild animals, the conflicts between humans and wildlife increases. The human-elephant conflict causes a tremendous amount of damage, often to poor villages close to the savannah. In this paper, we continue our earlier reported research on a geophone network aimed for elephant localisation by focusing on the detection challenge. We have now collected larger sets of seismic data with footsteps from both elephants and other big animals including humans. To detect the footsteps, a method is developed that analyses features of the geophone signal, which are then compared to those of an elephant footstep. The method detects 54 % of the footsteps and has a classification accuracy of 89 %. Subsequently, the detected elephant footstep is used to calculate the direction of arrival (DOA) angle using a delay-and-sum beamformer. The direction to an elephant is estimated with good precision on distances ranging from 8 to 30 meters. This research, not only, showcases a practical solution for mitigating human-elephant conflicts, but also underscores the potential of seismic technology in wildlife management and conservation efforts.

Human-wildlife conflicts are a global problem which is central to the Global Goal 15 (life on land). One particular case is elephants, that can cause harm to both people, property and crops. An early warning system that can detect and warn people in time would allow effective mitigation measures. The proposed method is based on a small local network of geophones that sense the seismic waves of elephant footsteps. It is known that elephant footsteps induce low frequency ground waves that can be picked up by geophones in the ground. First, a method is described that detect the particular signature of such footsteps, and then the detections are used to estimate the direction of arrival (DOA). Finally, a Kalman filter is applied to the measurements in order to track the elephant. Field tests performed at a local zoo shows promising results with accurate DOA estimates at 15 meters distance and acceptable accuracy at 40 meters.

Conventional direction of arrival (DOA) estimators are based on array processing using either time differences or beam-forming. The proposed approach is based on the received power at each microphone, which enables simple hardware, low sampling frequency and small arrays. The problem is recast into a linear regression framework where the least squares method applies, and the main drawback is that different sound sources are not readily separable.Our proposed approach is based on a training phase where the directional sensitivity of each microphone element is estimated. This model is then used as a fingerprint of the observed power vector in a real-time estimator. The learned power vector is here modeled by a Fourier series expansion, which enables Cramér-Rao lower bound computations. We demonstrate the performance using a circular array with eight microphones with promising results.