Anindya S. Paul

RSSI-Based Indoor Localization and Tracking Using Sigma-Point Kalman Smoothers

Solutions for indoor tracking and localization have become more critical with recent advancement in context and location-aware technologies. The accuracy of explicit positioning sensors such as global positioning system (GPS) is often limited for indoor environments. In this paper, we evaluate the feasibility of building an indoor location tracking system that is cost effective for large scale deployments, can operate over existing Wi-Fi networks, and can provide flexibility to accommodate new sensor observations as they become available. This paper proposes a sigma-point Kalman smoother (SPKS)-based location and tracking algorithm as a superior alternative for indoor positioning. The proposed SPKS fuses a dynamic model of human walking with a number of low-cost sensor observations to track 2-D position and velocity. Available sensors include Wi-Fi received signal strength indication (RSSI), binary infra-red (IR) motion sensors, and binary foot-switches. Wi-Fi signal strength is measured using a receiver tag developed by Ekahau, Inc. The performance of the proposed algorithm is compared with a commercially available positioning engine, also developed by Ekahau, Inc. The superior accuracy of our approach over a number of trials is demonstrated.

Wi-Fi based indoor localization and tracking using sigma-point Kalman filtering methods

Estimating the location of people and tracking them in an indoor environment poses a fundamental challenge in ubiquitous computing. The accuracy of explicit positioning sensors such as GPS is often limited for indoor environments. In this study, we evaluate the feasibility of building an indoor location tracking system that is cost effective for large scale deployments, can operate over existing Wi-Fi networks, and can provide flexibility to accommodate new sensor observations as they become available. At the core of our system is a novel location and tracking algorithm using a sigma-point Kalman smoother (SPKS) based Bayesian inference approach. The proposed SPKS fuses a predictive model of human walking with a number of low-cost sensors to track 2D position and velocity. Available sensors include Wi-Fi received signal strength indication (RSSI), binary infrared (IR) motion sensors, and binary foot-switches. Wi-Fi signal strength is measured using a receiver tag developed by Ekahau Inc. The performance of the proposed algorithm is compared with a commercially available positioning engine, also developed by Ekahau Inc. The superior accuracy of our approach over a number of trials is demonstrated.

MobileRF: a robust device-free tracking system based on a hybrid neural network HMM classifier

We present a device-free indoor tracking system that uses received signal strength (RSS) from radio frequency (RF) transceivers to estimate the location of a person. While many RSS-based tracking systems use a body-worn device or tag, this approach requires no such tag. The approach is based on the key principle that RF signals between wall-mounted transceivers reflect and absorb differently depending on a persons movement within their home. A hierarchical neural network hidden Markov model (NN-HMM) classifier estimates both movement patterns and stand vs. walk conditions to perform tracking accurately. The algorithm and features used are specifically robust to changes in RSS mean shifts in the environment over time allowing for greater than 90% region level classification accuracy over an extended testing period. In addition to tracking, the system also estimates the number of people in different regions. It is currently being developed to support independent living and long-term monitoring of seniors.

A tag-free solution to unobtrusive indoor tracking using wall-mounted ultrasonic transducers

Methods for indoor tracking typically require a person to carry some type of a body worn tag. A novel tag-free solution is presented that utilizes low cost wall-mounted ultrasonic transducers. The active ultrasonic transducers capture analog echoes, which are then digitized and analyzed in order to calculate the 1-D range of the moving person. The tracking algorithm utilizes a number of signal processing techniques including band-pass filtering, Hilbert transformation, and background subtraction to remove interference from other objects in the room. The range data from multiple sensors are treated as observations in a Bayesian framework using the sigma-point Kalman smoother (SPKS) to determine a persons 2-D position and velocity. The SPKS also performs “self-calibration” or simultaneous localization and mapping (SLAM) to determine the location of the wall-mounted transducers. The indoor tracking accuracy of the tag-free system is better than 0.5 meters.

Eigen-Template-Based HRR-ATR with Multi-Look and Time-Recursion

Automatic target recognition (ATR) using high range resolution (HRR) radar signatures is developed using classical Bayesian multiple hypothesis theory. An eigen-template-based matched filtering (ETMF) algorithm is presented where the templates are formed using the dominant range-space eigenvector of detected HRR training profiles and classification is performed using normalized matched filtering (MF). The proposed approach is extended to multi-look and sequential ATR where new observation profiles are recursively combined probabilistically with previous steps to update ATR results, which is useful for simultaneous recognition and tracking of moving targets. An HRR-specific profile normalization scheme is presented to satisfy matched filter requirements. Classification performance of the proposed method has been compared with a linear least-squares method and hidden Markov model (HMM) approach using MSTAR data collection.

A new formulation for nonlinear forward-backward smoothing

A new formulation for nonlinear smoothing is derived using forward-backward sigma-point Kalman filtering (SPKF). The forward filter uses the standard SPKF. The backward filter requires the use of the inverse dynamics of the forward filter. While smoothers based on the extended Kalman filter (EKF) simply invert the linearized dynamics, with the SPKF the forward nonlinear dynamics are never analytically linearized. Thus the backward nonlinear dynamics are not well defined. In previous work, a sigma-point Kalman smoother (SPKS) was derived by learning a nonlinear model of the backward dynamics from empirical data. In this paper, we make use of the relationship between the SPKF and weighted statistical linear regression (WSLR). The resulting pseudo-linearized dynamics obtained by WSLR is more accurate in the statistical sense than using a first order truncated Taylor series expansion as with the EKF. A new backward information filter can then be derived, which is combined with the forward SPKF to form the smoothed estimates.

Measuring in-home walking speed using wall-mounted RF transceiver arrays

In this paper we present a new method for passively measuring walking speed using a small array of radio transceivers positioned on the walls of a hallway within a home. As a person walks between a radio transmitter and a receiver, the received signal strength (RSS) detected by the receiver changes in a repeatable pattern that may be used to estimate walking speed without the need for the person to wear any monitoring device. The transceivers are arranged as an array of 4 with a known distance between the array elements. Walking past the first pair of transceivers will cause a peak followed by a second peak when the person passes the second pair of transceivers. The time difference between these peaks is used to estimate walking speed directly. We further show that it is possible to estimate the walking speed by correlating the shape of the signal using a single pair of transceivers positioned across from each other in a hallway or doorframe. RMSE performance was less than 15 cm/s using a 2-element array, and less than 8 cm/s using a 4-element array relative to a gait mat used for ground truth.

Multiharmonic frequency tracking method using the sigma-point kalman smoother

Several groups have proposed the state-space approach to tracking time-varying frequencies of multiharmonic quasiperiodic signals. The extended Kalman filter/smoother (EKF/EKS) is one of the common frequency tracking approaches seen in the literature. We introduce a multiharmonic frequency tracker based on the forward-backward statistical linearized Sigma-Point Kalman smoother (FBSL-SPKS) and compare its performance to that of the extended Kalman smoother (EKS). In all cases the FBSL-SPKS tracker outperformed the EKS tracker over a wide range of signal-to-noise (SNR) ratios. We also demonstrate its superior performance on real signals.

Multiharmonic tracking using sigma-point Kalman filter

Several groups have proposed the state-space approach to track time-varying frequencies ofmulti-harmonic quasi-periodic signals contaminated with white Gaussian noise. We compared the extended Kalman filter (EKF) and sigma-point Kalman filter (SPKF) algorithms on this problem. On average, the SPKF outperformed the EKF and more accurately tracked the instantaneous frequency over a wide range of signal-to-noise (SNR) ratios.