Continuous Close-Proximity RSSI-Based Tracking in Wireless Sensor Networks

Abstract

Abstract-In this paper we develop a continuous high-precision tracking system based on Received Signal Strength Indicator (RSSI) measurements for small ranges. The proposed system uses minimal number of sensor nodes with RSSI capabilities to track a moving object in close-proximity and high transmission rate. The close-proximity enables conversion of RSSI measurements to range estimates and the high transmission rate enables continuous tracking of the moving object. The RSSI-based tracking system includes calibration, range estimation, location estimation and refinement. We use advanced statistical and signal processing methods to mitigate channel distortion and packet loss. The system is evaluated in indoor settings and achieves tracking resolution of few centimeters. Therefore, it becomes the motion trackers of notice in many applications.

Experimental Exploration of RSSI Model for the Vehicle Intelligent Position System

“…In order to achieve accurate positioning with WSN, a large number of localization algorithms are established, such as (Blumrosen, Hod, Anker, Dolev, & Rubinsky, 2010;Wang, Jia, Lee & Li, 2003;Seidel & Rapport, 1992;Smailagic & Kogan, 2002;Bahl & Padmanabban, 2000;Seidel & Rappaport, 1991;Hightower, Want & Boriello, 2000). The distance between sensor nodes can be simply estimated according signal strength consumption.…”

“…A variant of the fingerprint method (Sun, Lang, Wang & Liu, 2014) interpolates the measured data to give a better fitting on a RSSI value. Advanced statistical and signal processing methods to mitigate channel distortion and packet loss are utilized to achieve tracking resolution of few centimeters (Blumrosen, Hod, Anker, Dolev & Rubinsky, 2010). …”

“…The RSSI-based tracking system includes calibration, range estimation, location estimation and refinement (Blumrosen, Hod, Anker, Dolev & Rubinsky, 2010), which is the core concept of our experimental exploration.…”

Human Body Parts Tracking and Kinematic Features Assessment Based on RSSI and Inertial Sensor Measurements

“…We propose to estimate the solution to the criterion in Equation (2) by using two stages similar to [30,35]: first to approximate the range between the mobile nodes and the N anchor nodes for Q measurements interval; and then to apply geometric and statistical methods on the range approximations to obtain the instant location. Consequently, the MMSE criterion can be refined to two sub-criterions, estimation of the range between the sensor nodes, and then the estimate the location based on the range estimations: $\hat{\text{g}}={\text{argmin}}_{\text{g}}\text{E}{[\text{D}-\text{g}\left({\text{P}}_{\text{r}}\right)]}^2$ $\begin{array}{c}\hat{\text{v}}={\text{argmin}}_{\text{v}}\text{E}{[\text{L}-\text{v}\left(\widehat{\text{D}}\right)]}^2\\ \text{s}.\text{t}.\left|{\text{L}}^{\text{i}+1}-{\text{L}}^{\text{i}}\right|<\delta ,\end{array}$where g is the transformation that operates on the power measurements matrix P r and its output is set of range estimations between the mobile node and the anchor nodes, v is the transformation that operates on the range estimations, and D̂=ĝ(p r ) is a N × K matrix of approximated distances between the mobile nodes and the N anchor nodes over K measurements.…”

Indoor Location Tracking and Orientation Estimation Using a Particle Filter, Ins, and Rssi

“…This has caused RSSI-based tracking to not be a viable solution for the motion tracking problem as it requires much higher accuracy. However, Blumrozen et al [17] suggested and showed that under certain conditions, RSSI can be a valid solution for the human motion tracking problem.…”