Moving Node Tracking in Sensory Networks by means of Space Time Adaptive Processing

Precise localization and tracking of moving or quasi-stationary nodes in a sensory network system, pose a major challenge among researchers all over the world. The purpose of the present research work is to improve on accuracy of estimation and tracking of moving targets or nodes in a wireless sensor network. This paper introduces the concept of constant modulus (CMA) algorithmic method in space time adaptive processing (STAP) to enhance the exactness of direction-of-arrival (DOA) estimation, by determining the frequency and time points of source signals. To begin with, the cost function is minimized by algebraic CMA weight updating beamformer and the estimation problem is transformed to moving target tracking domain in STAP. Then the optimization of the time-frequency distribution matrix is performed. Comparison by simulation results in terms of gradual change of root-mean-square-error (RMSE), and detection probability with signal-to-noise ratio (SNR) and computational complexity with number of array elements clearly illustrates that the proposed method performs better than the standard and popular existing methods at low signal power conditions.