Abstract
We present a target localization method using an approximated error covariance matrix based weighted least squares (WLS) solution, which integrates received signal strength (RSS) and angle of arrival (AOA) data for wireless sensor networks. We approximated linear WLS errors via second-order Taylor approximation, and further approximated the error covariance matrix using a least-squares solution and the variance in measurement noise over the sensor nodes. The algorithm does not require any prior knowledge of the true target position or noise variance. Simulations validated the superior performance of our new method.
Highlights
Q Performance is evaluated by calculating the root mean square error (RMSE), defined as RMSE =
We evaluate the performance of the real error covariance WLS” (ECWLS)
We present a novel target localization algorithm based on hybrid received signal strength (RSS)/angle of arrival (AOA) measurements, termed the ECWLS algorithm
Summary
Localization using wireless sensor networks (WSNs) has gained considerable attention given the emerging development of services based on location awareness [1,2,3,4,5,6,7,8,9,10,11,12] A WSN is a group of spatially dispersed sensors (anchors) that monitor and record the physical condition of the environment. We present an elaborate weighting scheme based on the approximated error covariance matrix for the approximated linear equation of the WLS algorithm as a replacement for distance-based weights. Using a second-order Taylor approximation, we derived an approximate error covariance matrix assuming perfect knowledge of the true target position and the variance in Gaussian measurement noise. To compute the approximated error covariance matrix without any knowledge of the target position or noise, we replaced the true target position with an LS solution and the variance in measurement noise over the sensor nodes. The main contribution of this paper is the presentation of a range-based hybrid RSS/AOA target localization algorithm that outperforms existing range-based state-of-the-art WLS without prior knowledge of the noise level.
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