Statistical analysis of indoor RSSI read-outs for 433 MHz, 868 MHz, 2.4 GHz and 5 GHz ISM bands

This paper presents statistical analysis of RSSI read-outs recorded in indoor environment. Many papers concerning indoor location, based on RSSI measurement, assume its normal probability density function (PDF). This is partially excused by relation to PDF of radio-receiver's noise and/or together with influence of AWGN (average white Gaussian noise) radio-channel – generally modelled by normal PDF. Unfortunately, commercial (usually unknown) methods of RSSI calculations, typically as "side-effect" function of receiver's AGC (automatic gain control), results in PDF being far different from Gaussian PDF. This paper presents results of RSSI measurements in selected ISM bands: 433/868 MHz and 2.4/5 GHz. The measurements have been recorded using low-cost integrated RF modules (at 433/868 MHz and 2.4 GHz) and 802.11 WLAN access points (at 2.4/5 GHz). Then estimated PDF of collected data is shown and compared to normal (Gaussian) PDF

Indoor positioning and tracking based on the received signal strength

Received Signal Strength Indicator (RSSI)-based indoor Location and Tracking (L&T) is a promising and challenging technology that enables mobile users/nodes to obtain their location information. This dissertation focuses on overcoming the challenges as well as improving the positioning accuracy for the RSSI-based L&T. In particular, the author considers 4 L&T solutions. In the first, the author develops a L&T solution by designing the Kalman Filter (KF) to work linearly within the positioning framework. To elaborate on this implementation, the equations of the KF are presented in a consistent manner with the implementation. In the second, the author designs a L&T solution based on the Iterated Extended Kalman Filter (IEKF) to improve the accuracy compared with the popular Extended Kalman Filter (EKF). In the third, the author overcomes the particular implementation challenges of the EKF by designing a L&T solution based on the implementation of the Scaled Unscented Transformation (SUT) to the KF. The author calls the resulting filter Scaled Unscented Kalman Filter (SUKF). In the forth, the author overcomes the implementation difficulties of the EKF by designing a L&T solution based on the implementation of the Spherical Simplex Unscented Transformation (SSUT) to the KF. The author calls the resulting filter the Spherical Simplex Unscented Kalman Filter (SSUKF). The proposed solutions with their corresponding achievements enhance the role of RSSI-based L&T in wireless positioning systems. The contributions led to significant improvement in the positioning accuracy, reliability and the ease of implementation