Mobile transmitter AOA estimation under multipath conditions using an MLE based superresolution algorithm and comparison with weighted spectrum methods

Several techniques have been proposed and discussed for mobile handset location. These include the use of the Global Position System (GPS), time difference of arrival (TDOA), time of arrival (TOA) and angle of arrival (AOA). Each of these systems and techniques has various advantages and disadvantages. However, the aim of this paper is to demonstrate the advantage of using maximum likelihood (ML) based superresolution direction finding (SRDF) techniques for AOA estimation over weighted spectrum type approaches in the presence of multipath propagation in the cellular environment. This shows that ML based techniques are more suitable than weighted spectrum type approaches for handset location systems that use AOA information. Direction finding (DF) of a radio signal involves estimating a signal's AOA at a receiver. The DF techniques considered in this paper operate on the covariance matrix formed from the complex samples recorded at an array of antennas. All the techniques are SRDF algorithms, implying that they have the ability to resolve signals impinging on the array when their angular separation is less than the natural beamwidth of the antenna array.