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Abstract
This paper investigates the linear separation requirements for Angle-of-Arrival (AoA) and range sensors, in order to achieve the optimal performance in estimating the position of a target from multiple and typically noisy sensor measurements. We analyse the sensor-target geometry in terms of the Cramer–Rao inequality and the corresponding Fisher information matrix, in order to characterize localization performance with respect to the linear spatial distribution of sensors. Here in this paper, we consider both fixed and adjustable linear sensor arrays.
Highlights
Different techniques can be used to localize an emitting or non-emitting target [1,2,3,4]
In this paper we provide a more rigorous characterization of the relative sensor-target geometry for linear sensor arrays based on AoA-only and range-only localization, and to the best of our knowledge, no such analysis exists in the literature
We have provided a characterization of optimal sensor-target geometry for linear arrays of AoA and range sensors in passive localization problems in R2
Summary
Different techniques can be used to localize an emitting or non-emitting target [1,2,3,4]. AoA and range-based localizations are common passive measurement techniques where the location of an emitter is obtained by triangulation of bearing or range information collected at a number of sensors. In this paper we provide a more rigorous characterization of the relative sensor-target geometry for linear sensor arrays based on AoA-only and range-only localization, and to the best of our knowledge, no such analysis exists in the literature. AoA/range sensors located as a linear array (uniform and non-uniform) In this case, the Cramer–Rao lower bound with the corresponding Fisher information determinant is used to investigate the optimality of the relative sensor-target geometry, exploring the intrinsic relation with the spatial diversity and the underlying measurement model. The results presented in this paper provide fundamental information about how the localization performance is affected by the sensor-target geometry for linear sensor arrays. This information is of significant value to users of multiple sensor (linear arrays) based localization systems
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