Interactive Kalman Filtering for Differential and Gaussian Frequency Shift Keying Modulation with Application in Bluetooth

Abstract

Some applications are constrained only to implement low cost receivers. In this case, designers are required to use less complex and non-expensive modulation techniques. Differential Quadrature Phase Shift Keying (DQPSK) and Gaussian Frequency Shift Keying (GFSK) can be non-coherently demodulated with simple algorithms. However, these types of demodulation are not robust and suffer from poor performance. This paper proposes a new method to enhance the performance of DQPSK and GFSK using Interactive Kalman Filtering (IKF) technique, in which a one Unscented Kalman Filter (UKF) and two Kalman Filters (KF) are coupled to optimize the demodulated signals. This method consists of simple but very effective algorithms without adding complexity to the demodulators comparing to other very complex methods. UKF is used in this method due to its superiority in approximating and estimating nonlinear systems and its ability to handle non-Gaussian noise environments. The proposed method has been validated by creating a MATLAB/SIMULINK Bluetooth system model, in which the IKF is integrated into the receiver, which implement both DQPSK and GFSK, and run simulation in Gaussian and Non-Gaussian noise environments. Results have shown the effectiveness of this method in optimizing the received signals, and that the UKF outperforms the Extended Kalman Filter (EKF)

Extended Kalman Filtering and Pathloss modeling for Shadow Power Parameter Estimation in Mobile Wireless Communications

“…Wiener-Kolmogorov filter was the predecessor that Kalman filter [2]. While KF can be applied to linear systems is not a good solution for systems with nonlinearities.…”

“…EKF Techniques have been proposed to modify KF to be applied to nonlinear systems. For example, EKF has been proposed by linearizing estimated state variables through Jacobian matrices [2]. However, EKF may not be a good choice in system with high nonlinearity, or systems that are very difficult to calculate their Jacobian matrices.…”

“…There are two main causes of fading between a mobile station (MS) and a base station (BS) [1][2][3]. One is multipath propagation loss, where the received signal strength fluctuates due to multiple paths, and shadowing (Local Mean), where the transmitted signal is lost through physical phenomena, such as absorption, refraction (Figure 1), scattering and diffraction.…”

“…One is multipath propagation loss, where the received signal strength fluctuates due to multiple paths, and shadowing (Local Mean), where the transmitted signal is lost through physical phenomena, such as absorption, refraction (Figure 1), scattering and diffraction. Shadowing is caused by obstacles, such as buildings or trees along the path of a signal from the base station (BS) to the mobile station [1][2][3].…”

“…Shadowing is described as the effect of the power fluctuation of the received power due to objects obstructing the propagation path between the transmitter and receiver [1][2][3].…”