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Abstract:
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Sensors are used for obtaining information from their operating environment. Recently, the use of multiple sensory units as arrays or networks has become popular. This has been caused by developments in sensor technology and the inherent application potential. Costs of sensory units and systems have decreased with developments in electronics. In addition, advances in communication technologies, such as wireless operation, make it easier to deploy systems with a large number of sensory units. Several applications exist for these systems in the areas of monitoring, control, surveillance, communications and multimedia devices.
With developments, come additional requirements. Sensor systems are expected to operate for long periods of time, possibly unattended. Furthermore, environmental and signal conditions may be adverse. As a result, errors caused by disturbances occur in system output and hardware malfunctions may develop between scheduled maintenance. Automatic operation is still expected and even if human operators are available, manual inspection of the data from individual units is not feasible in large systems. This raises the need for error-tolerant processing, that is able to assess the quality of produced data and detect potential malfunctions.
This thesis addresses these issues within acoustic direction of arrival (DOA) estimation. An array of microphones is utilized to acquire acoustic pressure signals from a far-field source. Time differences of arrival (TDOAs) between the sensors are estimated and used to compute the direction estimate. Specifically, the plane wave slowness vector (SV) formulation of the problem is examined, allowing a linear model to be used for estimation.
In practice, signals are noisy and time delay estimates contain large errors making the directional estimates inaccurate. This work examines confidence scores that can be used to evaluate the instantaneous estimation error and to remove highly erroneous delay estimates from the processing. A robust and scalable DOA estimator is introduced. It is further demonstrated with experiments that confidence scores can be used for signal activity detection, outlier removal and sensor failure compensation.
One of the examined confidence scores is based on closed-path properties of TDOAs. This score is generalized beyond the plane wave DOA domain to a difference quantity model. Examples of difference quantities include voltages and spherical-wave TDOAs used in source localization. The scoring principle is brought into a statistical framework and outlier detection is formulated as a hypothesis testing problem. An optimum detector is derived and its properties are analyzed. The results of this work provide simple and computationally light means for sensor arrays to diagnose their operation instantaneously in dynamic conditions. |