While the success of improving direction of arrival (DOA) estimation with linear coded covers using a single acoustic vector sensors (AVS) has been established, the extension of this theory to arraybased systems remains unexplored. To address this gap, we employ a specially designed coded cover and leverage compressed sensing (CS) and compressed covariance sensing (CCS) methods, extending their application from single AVS systems to an array-based acoustic measurement system. Our results demonstrate that a 14×10 coded cover with 12 PU probes enables accurate localization of 100 sound sources in 3D, even at a signal to noise ratio (SNR) as low as 10 dB, showcasing the scalability and robustness of this approach. To further enhance localization accuracy, we implement a selfcalibration method in the covariance domain to correct phase and gain errors in each receiving channel. Additionally, we combine selfcalibration with CCS to improve resolution and reduce side lobes. For geometric mismatch, we first investigate the sparsity-cognizant total least-squares (STLS) with multiple measurement vectors (MMV) variant of the fast iterative shrinkage-thresholding algorithm (FISTA) method. Then a grid-searching strategy is employed to compensate for these mismatches, ensuring better estimation accuracy. Experimental validation confirms that these techniques significantly enhance DOA estimation under non-ideal conditions, contributing to the advancement of acoustic sensing and localization methodologies.

The Radar Resource Management (RRM) problem in a multi-sensor multi-target scenario is considered. The problem is defined as a constrained optimization problem in which the predicted error covariance is minimized subject to resource budget constraints. By applying Lagrangian Relaxation (LR) the problem is decoupled into multiple sub-optimization problems. The problem is modeled according to a Partially Observable Markov Decision Process (POMDP). Using a stochastic optimization framework called policy rollout, the POMDP is solved non-myopically by looking ahead into the expected future. Two novel implementations, namely a centralized and distributed implementation are presented as viable approaches for solving this problem for a multi-sensor case. The centralized implementation, defined as the approximately optimal solution, utilizes a global policy per task. As such, the policy rollout for a single target needs to explore the actions of multiple sensors. The distributed implementation is considered as a practical alternative to improve on the computational complexity of the policy rollout of the centralized implementation. Now per sensor and per task a policy rollout is computed. To maintain a similar performance as the centralized implementation, at the beginning of each policy rollout the last known actions of the other sensors are shared. An additional third independent implementation is considered. The independent implementation uses no communication during the optimization process and is considered to be the implementation with the lowest performance with respect to the cost. All implementations have been applied to multiple two-dimensional simulated radar tracking scenarios. A comparison is made between the centralized, distributed and independent implementation based on the average cost and runtime. Results indicate that both the centralized and distributed implementation outperform the independent implementation with respect to the cost by a factor two. Subsequently, the distributed solution converges to similar results as the centralized implementation while requiring significantly less computational resources.

Indoor positioning using Bluetooth addressed a great concern. The properties of narrowband usage, low energy consumption and universality on devices attract numerous customers and promote researchers to investigate potential of Bluetooth in indoor positioning field. It has already supported Angle-of-Arrival (AoA) and Angle-of-Departure (AoD) in angle domain for indoor localization. In range domain, using Received Signal Strength (RSS) is practicable but it cannot provide enough resolution. It is needed to develop a technique that can improve resolution for range finding feature. The challenges of localization in indoor environments are mainly from the multipath propagation of signals. In this thesis, a subspace-based super-resolution algorithm for time delay dispersion estimate is developed. Range finding is realized by computing time-of-arrival (ToA) parameter of signal arriving at direct line-of-sight (DLoS). An essential procedure before the developed algorithm is applied is that to correctly separate subspace into signal space and noise space. Techniques for subspace separation are investigated in this thesis. In this thesis, we want to explore the potential of indoor localization using Bluetooth narrowband radios. To start with, a data model according to the property of the conducted measurement data is developed. The conducted measurement data is radio channel measurements based on channel sounding technique. Then the data model is developed as channel impulse response model and multipath signals are indicated by different time delays. Since an accurate covariance matrix of measurement data is required for super-resolution algorithm, smoothing techniques is employed. The smoothing techniques considered are forward smoothing technique and forward-backward smoothing technique. For the purpose of obtaining an accurate subspace separation, two techniques are investigated in this thesis, namely MDL criteria algorithm and the threshold method. in order to investigate the performance and reliability of those two techniques, experiments are taken out using different parameter values. Comparison is made between the results of these two techniques. Afterwards, subspace-based super-resolution algorithm is taken into consideration. In this thesis, the super-resolution algorithm implemented is MUSIC algorithm. The functionality of MUSIC algorithm on narrowband radios measurements is tested and evaluated firstly by simulation experiments, which demonstrates the practicability of applying MUSIC algorithm on narrowband radios measurements. Then experiments are extended to the measurement data that conducted from real indoor environments, for the purpose of indoor localization realization using narrowband radios.

When the re brigade arrives at a burning building, it is of vital importance that people who are still inside can quickly be found. In this thesis we contribute to an ultrasonic sound sensor for human presence detection in smoke-lled spaces. This type of sensor could assist the re brigade when evacuating a large building by directing them to the places where their help is most needed. The advantage of ultrasonic sound over other sensors or cameras is that its signal is able to pierce through smoke, does not require badges or other wearable devices and introduces little privacy and security risks. In addition, ultrasonic sensors are very inexpensive making it possible to equip every room of a building with an ultrasonic presence detector. In this research an ultrasonic sensor was built for less than 20 Euros and it was found to be unaected by the glycerine based smoke that it was tested in. Using a particle filter based on sequential importance resampling as well as a Filter based on Gaussian approximation of the posterior density the resulting system was reliably able to detect when there was a single person walking in the sensor direction, even when other sources of movement such as doors and chairs were present.

Clock synchronization among the nodes of a wireless acoustic sensor network (WASN) is a significant issue that affects the performance of multi-channel noise reduction schemes. Since independent sensors are utilized, each accompanied by its internal clock, clock offsets are inevitable, even if the mismatch in the sampling frequencies is negligible. In this thesis, clock offsets are mathematically modeled and the problem of multi-channel linear filtering for speech enhancement is addressed through signal subspace methods. For this purpose, the generalized eigenvalue decomposition (GEVD) of the cross-power spectral density (CPSD) matrices of the noise and target speech processes is capitalized. Beamformers based on this technique are proved to be invariant to sensor clock offsets when used in a blind manner, exploiting only network measurements. This result is confirmed through experiments in a simulated environment.

Generative adversarial networks (GANs) are a class of generative models, for which the goal is to learn from training data and then to generate data with similar characteristics. Despite the wide use of GANs, a quantitative evaluation method of their performance is lacking. In the current work, we invented a series of artificial datasets, consisting of images with one or two spheres in arbitrary locations, to evaluate the performance of GANs from two aspects: the quality, which evaluates the visual quality of generated images, and the diversity, which evaluates GANs’ ability to generate samples with different modes in the real data distribution. We further explored the validity of an alternative evaluation metric, the Wasserstein distance, as an indicator of the quality and the diversity for Wasserstein GANs. Furthermore, we explored two improving techniques that boosted the performance of GANs, namely the addition of regularization terms and the ‘Smooth-to-Sharp’ training algorithm, and validate their efficacy on the MINIST dataset. Our proposed quantitative evaluation method could help researchers select better models and promote the improvement of GANs’ performances. Future work to increase the complexity of our artificial datasets, to validate our improving techniques in more complicated real-world datasets, and to investigate the use of estimated Wasserstein distance is planned.