Optimum Model for Tracking of Moving Objects

In wireless communication, it is a challenge to find the accurate position of moving objects. The presented work proposes the GPS receiver to measure the position and velocity of moving objects. These measurements are dynamic, the Least Squares (LS) technique is used to linearize the measurement for further processing. Afterward, the time difference of arrival (TDOA) mythology is applied. The obtained data is then processed through a Kalman filter to mitigate non-line-of-sight errors and smoothen the range values. The Kalman filter applies standard deviation on received data and performs an NLOS/LOS hypothesis test. By processing the received data, the algorithm generates readings that mitigate the NLOS error and reduces position error. The simulations demonstrate that the proposed algorithm reduced the noise by 28.64% and 34.4% in LOS and NLOS regions respectively. These findings indicate that the accuracy of object tracking was significantly improved as compared to other algorithms while also being less computationally intensive and cost efficient.