A Novel Phase Tracking in ZigBee Receiver Using Extended Kalman Filtering over AWGN Channel This paper aims at investigating the performance of applying Extended Kalman Filter (EKF) to track the phase changes of Binary Phase Shift Keying (BPSK) in ZigBee receivers. EKF is used to optimize the BPSK modulated signal by minimizing the phase error in the presence of the Additive White Gaussian Noise (AWGN) channel. A MATLABbased simulator is developed to implement the EKF in the ZigBee receiver to correctly track the phase changes of the BPSK as the performance of tracking is measured by Mean Square Error (MSE) for different Signal to Noise Ratio (SNR). The MSE is used to measure the phase deviation error from the true phase. The phase tracking measured by MSE for different SNR values show that EKF has less phase error deviation than Costas Phase Locked Loop (CPLL) for all SNR values. The results show that EKF is novel and track the phase changes correctly more than the Costas PLL. For example, for SNR of 5, the MSE for our EKF is 3.5e05 whereas it is 6.25e05 for Costas PLL, i.e. ours has an improvement of 78%. The proposed EKF has less phase error deviation than CPLL for all SNR values and the improvement can be from 5% to 81%, depending on SNR.

CN0 Estimator Based on the Adaptive Strong Tracking Kalman Filter for GNSS Vector Receivers The carriertonoise ratio (CN0) is an important indicator of the signal quality of global navigation satellite system receivers. In a vector receiver, estimating CN0 using a signal amplitude Kalman filter is a typical method. However, the classical Kalman filter (CKF) has a significant estimation delay if the signal power levels change suddenly. In a weak signal environment, it is difficult to estimate the measurement noise for CKF correctly. This article proposes the use of the adaptive strong tracking Kalman filter (ASTKF) to estimate CN0. The estimator was evaluated via simulation experiments and a static field test. The results demonstrate that the ASTKF CN0 estimator can track abrupt variations in CN0 and the method can estimate the weak signal CN0 correctly. When CN0 jumps, the ASTKF estimation method shows a significant advantage over the adaptive Kalman filter (AKF) method in terms of the time delay. Compared with the popular CN0 algorithms, the narrowtowideband power ratio (NWPR) method, and the variance summing method (VSM), the ASTKF CN0 estimator can adopt a shorter averaging time, which reduces the hysteresis of the estimation results.