Over the past decade, spatial audio awareness evolved into an in-demand feature in audio entertainment. The addition of sound source locations to, for instance, movies or music adds a level of auditory envelopment and spatial awareness to the audio experience. Expensive setups present in, for instance, cinema's, are able to create this envelopment by means of a large set of loudspeakers with which the desired sound fields are created. Creating this spatial envelopment in practical consumer living rooms or home cinema setups proves to be a more challenging task due to the impractical amount of loudspeakers required. To create the audio envelopment with a small amount of loudspeakers, crosstalk cancellation can be used. Crosstalk cancellation as posed in literature is, however, not robust enough to be used in practical appliances. The main cause of these bad characteristics is the objective cost function it optimizes which results in an ill-posed problem. In this thesis, the crosstalk cancellation problem is relaxed by aiming for perceptually sufficient results instead of aiming for objectively optimal results. The human auditory system has its limitations in both the perception of audio and localization of audio sources. Exploiting the limitations of the auditory system generates mathematical freedom that can be used to construct a more robust and stable crosstalk cancellation algorithm. This thesis provides the first steps towards the incorporation of audio perception in the domain of crosstalk cancellation and audio envelopment with a limited amount of loudspeakers.

A robot swarm of zebros (Dutch: ZEs Benige RObots) is being developed at the TU Delft, with the purpose of forming a large self-deploying sensor network that survey remote locations without the need for any pre-existing infrastructure, or be used for evaluating swarming algorithms in the field. Towards this goal, zebros need to be able to communicate with and localise nearby neighbours. Existing localisation solutions showed to be inadequate for the task, because they generally do not allow for localisation without existing infrastructure, do not allow scaling to a large swarm or because they rely on robot behaviour, which introduces a dependency of localisation on a specific swarming algorithm.
We present a fully-localised method of localisation called Tangolation, which estimates the location of a neighbour as a range and an angle, accompanied by a confidence value from 1 (low) to 5 (high). With Tangolation, two nodes estimate each other’s next location from a range measurement using Two-Way Ranging (TWR), and the exchange of the nodes’ displacement since the last range measurement. Therefore, the two nodes only need each other to determine each other’s location as long as at least one of the two is moving, allowing Tangolation to scale well to larger swarms. No knowledge about the network topology or control over robot behaviour is needed, and communication between localising nodes only needs to happen sparsely, at a frequency of once every five seconds per neighbour. A fully distributed Time Division Multiple Acces (TDMA) protocol named Anarchic TDMA (AN-TDMA) was devised to support communcation within the robot swarm, where nodes synchronise the slot start times without the need for a special coordinating node that indicates the start of slots or frames. AN-TDMA showed to increase the reachable channel utilisation ratios by 55% to 67% compared to the ALOHA method recommended by Decawave.

For TWR, the Decawave DW1000 transceiver was used, and the BNO055 IMU aided in displacement measuring. Tangolation was tested in a simulation with realistic conservative estimates of the noise over the range and displacement measurements. Even using conservative noise estimates, at least 95% of the estimated location angles are within the required 22◦ of the true value. Depending on the confidence level that Tangolation reports, 65% to 84% of the angle estimates are within 10◦ of the true value, which shows that Tangolation more than meets the set requirements.