GRNN-Based Target L&T Using RSSI

Abstract

The traditional RSSI-based moving target L&T using WSN generally employs trilateration technique. Although being a very simple technique, it creates significant errors in localization estimations due to nonlinear relationship between RSSI and distance. To deal with such a highly nonlinear mapping between an input and an output, a suitable artificial neural network (ANN) technique can be the better alternative to achieve a high target tracking accuracy. The generalized regression neural network (GRNN) is a one-pass learning algorithm, which is well-known for its ability to get trained quickly with very few training samples. Once trained with the RSSI measurements and associated locations in the off-line phase, it can learn the dynamicity of any given indoor environment quickly to give location estimates of the mobile target in the online phase. This chapter presents an application of GRNN to solve the problem of target L&T. The GRNN can estimate the location of mobile target moving in WSN, which can be then further smoothed using KF framework. Utilizing this idea to improve target tracking accuracy GRNN+KF and GRNN+UKF algorithms is presented in this chapter. The GRNN is trained with the RSSI measurements received at mobile target from anchor nodes and the corresponding actual target 2-D locations. Extensive simulation experiments are carried out to prove the efficacy of these proposed algorithms. In Case I, the performance of GRNN-based L&T algorithm is compared with the traditional trilateration-based localization technique. In Case II, the GRNN+KF and GRNN+UKF algorithms are compared with trilateration technique, whereas in Case III, the efficacy of GRNN+KF and GRNN+UKF algorithms is compared with the previously proposed trilateration+KF and trilateration+UKF algorithms. The proposed GRNN- and KF-based target L&T algorithms demonstrate a superior target tracking performance (tracking accuracy in the scale of few centimeters) irrespective of abrupt variations in the target velocity, environmental dynamicity as well as nonlinear system dynamics.