Relative location estimation in wireless sensor networks

Abstract

In nonlinear deterministic parameter estimation, the maximum likelihood estimator (MLE) is unable to attain the Cramer-Rao lower bound at low and medium signal-to-noise ratios (SNR) due the threshold and ambiguity phenomena. In order to evaluate the achieved mean-squared-error (MSE) at those SNR levels, we propose new MSE approximations (MSEA) and an approximate upper bound by using the method of interval estimation (MIE). The mean and the distribution of the MLE are approximated as well. The MIE consists in splitting the a priori domain of the unknown parameter into intervals and computing the statistics of the estimator in each interval. Also, we derive an approximate lower bound (ALB) based on the Taylor series expansion of noise and an ALB family by employing the binary detection principle. The accurateness of the proposed MSEAs and the tightness of the derived approximate bounds 1 are validated by considering the example of time-of-arrival estimation.

Modeling and Optimizing Positional Accuracy Based on Hyperbolic Geometry for the Adaptive Radio Interferometric Positioning System

“…It claims to achieve 5 centimeters positional accuracy in a partially obstructed 80m×50m outdoor. Given that signal propagates with constant velocity, TOA (time of arrival) methods [18] estimate distance by measuring the signal propagation time. AOA (angle of arrival) [19] is a network-based solution that exploits the geometric property of the arriving signal.…”

“…These techniques exploit the decaying model of electronic-magnetic field to translate RSSI to the corresponding distance [6] [7] [9]. Also, the frequency bands used for transmission vary.…”

“…Using these data, we validated the correctnesss of our error prediction model by comparing the real error and the estimation error. Figure 8 plots the real ground-truth error vs. the estimation error (p error ) calculated from our model in Equation (18). The red line plots a perfect diagonal line representing perfect error prediction, and the blue dots are our measurements.…”

Performance bounds for cooperative RSS emitter tracking using diffusion particle filters

“…Gaussian noise process with known variance σ 2 r , x r is the r-th sensor position, ||.|| is the Euclidean norm, and the parameters (P 0 , d 0 , ζ r ) are known model parameters (see [11] for details) and H is a 2 × 4 projection matrix such that H(1, 1) = H(2, 3) = 1 and H(i, j) = 0 otherwise. In the remainder of the paper, the closed neighborhood of node r, denoted N(r), is defined as the union of {r} and the set of all indices { } such that node is a neighbor of node r according to the topology of the network graph.…”

Uncertainty-aware Wireless Sensor Networks

“…Location uncertainty emerges when the placement of sensors is required in a sensor field and the exact locations of the sensors are not known. From the viewpoint of location uncertainty, routing and location protocols have been proposed for event reporting to mobile sink or target tracking (Howard, Matari and Sukhatme, 2001;Patwari and Hero, 2003;Zou and Chakrabarty, 2004). Zou and Chakrabarty (2004) developed a model to optimise the number of sensors and their location in a distributed sensor network.…”