Unscented Kalman Filter for Indoor Bluetooth Voice Transmission Interference over Non-Gaussian Fading Channel

This study proposes a novel signal detection and modulation optimization scheme using the unscented Kalman filter (UKF) for the Bluetooth voice communication system for the indoor noise environment at Oakland University. It is designed for filtering received signals at the front-end of the receiver stage and established to be robust due to the usage of UKF. The UKF model is capable of handling modulated waveforms represented by the state-space model to reduce the non-Gaussian noise over a multipath fading channel. Moreover, this novel work addresses the characterization of the fading channel with typical non-Gaussian noise, which can be applicable in Bluetooth systems with 802.11b (Wi-Fi) standard interference. The simulation results are used to calculate the bit error rate and frame error rate.

[1]  Jeffrey K. Uhlmann,et al.  Unscented filtering and nonlinear estimation , 2004, Proceedings of the IEEE.

[2]  Lutz H.-J. Lampe,et al.  Diversity combining for coherent and differential M-PSK in fading and class-A impulsive noise , 2005, IEEE Transactions on Wireless Communications.

[3]  Mahdi N. Ali,et al.  Interactive Kalman Filtering for Differential and Gaussian Frequency Shift Keying Modulation with Application in Bluetooth , 2012 .

[4]  Chung G. Kang,et al.  MIMO-OFDM Wireless Communications with MATLAB , 2010 .

[5]  Sylvie Marcos,et al.  A network of adaptive Kalman filters for data channel equalization , 2000, IEEE Trans. Signal Process..

[6]  Mahdi Noman-Mosleh Ali.  Interactive Kalman Filtering for differential and continuous phase modulation optimizations with applications in Bluetooth communication , 2012 .

[7]  J.E. Mazo,et al.  Digital communications , 1985, Proceedings of the IEEE.

[8]  Richard A. Brown,et al.  Introduction to random signals and applied kalman filtering (3rd ed , 2012 .

[9]  Andrea Conti,et al.  On Bluetooth performance with diversity reception in fading channels , 2004, 2004 IEEE 15th International Symposium on Personal, Indoor and Mobile Radio Communications (IEEE Cat. No.04TH8754).

[10]  David Middleton,et al.  Non-Gaussian Noise Models in Signal Processing for Telecommunications: New Methods and Results for Class A and Class B Noise Models , 1999, IEEE Trans. Inf. Theory.

[11]  Xianghong Cheng,et al.  A Direct and Non-Singular UKF Approach Using Euler Angle Kinematics for Integrated Navigation Systems , 2016, Sensors.

[12]  Wang Jia,et al.  Performance of a Bluetooth system in multipath fading channels and interference , 2002, 4th International Workshop on Mobile and Wireless Communications Network.

[13]  Asst. Professor,et al.  Performance Analysis Of Bluetooth System Using Optimized Differential GFSK Demodulator , 2012 .

[14]  H. Sorenson,et al.  Nonlinear Bayesian estimation using Gaussian sum approximations , 1972 .

[15]  Jing Zhang,et al.  Maximum Likelihood-Based Iterated Divided Difference Filter for Nonlinear Systems from Discrete Noisy Measurements , 2012, Sensors.

[17]  Jun S. Liu,et al.  Mixture Kalman filters , 2000 .

[18]  Huaping Liu,et al.  Performance of frequency hopped noncoherent GFSK in correlated Rayleigh fading channels , 2003, IEEE International Conference on Communications, 2003. ICC '03..

[19]  Theodore S. Rappaport,et al.  Measurements and Models of Radio Frequency Impulsive Noise for Indoor Wireless Communications , 1993, IEEE J. Sel. Areas Commun..

[20]  Mustafa Kuzuoglu,et al.  Continuous-time nonlinear estimation filters using UKF-aided Gaussian sum representations , 2013, Proceedings of the 16th International Conference on Information Fusion.

[21]  Ali S. Alghamdi,et al.  Robust Non-Coherent Demodulation Scheme for Bluetooth Voice Transmission Using Linear, Extended, and Unscented Kalman Filtering , 2015 .