Recently, the need of monitoring parking places, airports, and harbours has increased. Microwaves, infrared based techniques, vision, or acoustics are the key techniques but each of them requires a specific kind of post-processing. Far field target localization methods based on Angle Of Arrival (AOA) often neglect the possibility of erroneous angle observations. Three different methods for increasing the accuracy of cross fixing based localization are compared. Average of the AOAs is easily corrupted by outliers, "m out of k"-selection of AOAs suffers from loss of data. Signal energy based target location circle is used to validate the cross fixing result, thus improving reliability. The energies of averaged target signals from two arrays are used to calculate a circle on which the target resides. Distance from the cross fixed location to the circle is used to validate the location. Experiments are carried out with simulated and real data.
Recently, the need to monitor restricted areas has increased.
Acoustics is one of the available key techniques but there are some restrictions and constraints to consider. In situations with unknown noise and low SNR the performance of time delay based direction of arrival (DOA) estimators collapses rapidly as SNR decreases. Outliers are introduced into estimation results when signals of interest are masked by noise.
There exist several methods for compensation of noise induced errors, such as averaging within subarrays, time delay selection or various
minimizations. These compensation methods provide an optimum solution with respect to some criteria, but are uneffective against large
errors in multiple time delays.
In this paper, we present a method for removing outlayers caused by errors in time delays. First, we utilize signal propagation speed to measure an error criterion for DOA estimates. Second, estimates with sufficiently large error criterion are identified as outlayers and discarded.
Effectiveness of our method is verified through experiments with
simulations and real data. In both cases we are able to identify and
discard outlayers and thus improve estimation reliability. Results
indicate that the given method can be used to gain efficiency and
robustness in DOA estimation applications, such as automatic acoustic
surveillance of large areas.