A COMPARATIVE STUDY OF DOA ESTIMATION ALGORITHMS WITH APPLICATION TO TRACKING USING KALMAN FILTER

Tracking the Direction of Arrival (DOA) Estimation of a moving source is an important and challenging task in the field of navigation, RADAR, SONAR, Wireless Sensor Networks (WSNs) etc. Tracking is carried out starting from the estimation of DOA, considering the estimated DOA as an initial value, the Kalman Filter (KF) algorithm is used to track the moving source based on the motion model which governs the motion of the source. This comparative study deals with analysis, significance of Non-coherent, Narrowband DOA (Direction of Arrival) Estimation Algorithms in perception to tracking. The DOA estimation algorithms Multiple Signal Classification (MUSIC), Root-MUSIC& Estimation of Signal Parameters via Rotational Invariance Technique (ESPRIT) are considered for the purpose of the study, a comparison in terms of optimality with respect to Signal to Noise Ratio (SNR), number of snapshots and number of Antenna elements used and Computational complexity is drawn between the chosen algorithms resulting in an optimum DOA estimate. The optimum DOA Estimate is taken as an initial value for the Kalman filter tracking algorithm. The Kalman filter algorithm is used to track the optimum DOA Estimate