Geolocation by Direction of Arrival Using Arrays With Unknown Orientation

It takes a great deal of care to accurately align direction of arrival (DOA) sensors to a reference direction. Any error in alignment degrades the localization accuracy of the entire system. We propose a method that enables the alignment using DOA measurements of arbitrary sources. This saves the efforts to set and maintain the alignment by external means. To simplify the exhibition, it is assumed that the sensors and the sources are confined to a plane. The method is based on the maximum-likelihood estimator. The main challenge is the discontinuities in the cost function due to the circular nature of angle measurements. The proposed method is verified by simulations, and the performance is compared to lower bounds and also to systems with perfect alignment.

[1]  J.L. Poirot,et al.  Application of Linear Statistical Models to Radar Location Techniques , 1974, IEEE Transactions on Aerospace and Electronic Systems.

[2]  Pravas Mahapatra.  Emitter Location Independent of Systematic Errors in Direction Finders , 1980, IEEE Transactions on Aerospace and Electronic Systems.

[3]  Marc Oispuu,et al.  Multiple emitter localization using a realistic airborne array sensor , 2011, 14th International Conference on Information Fusion.

[4]  Lester Ingber,et al.  Adaptive simulated annealing (ASA): Lessons learned , 2000, ArXiv.

[5]  B. R. Badrinath,et al.  Ad hoc positioning system (APS) using AOA , 2003, IEEE INFOCOM 2003. Twenty-second Annual Joint Conference of the IEEE Computer and Communications Societies (IEEE Cat. No.03CH37428).

[6]  Y. Bar-Shalom,et al.  Exact multisensor dynamic bias estimation with local tracks , 2004, IEEE Transactions on Aerospace and Electronic Systems.

[7]  Arye Nehorai,et al.  Concentrated Cramer-Rao bound expressions , 1994, IEEE Trans. Inf. Theory.

[8]  Yaakov Bar-Shalom,et al.  New assignment-based data association for tracking move-stop-move targets , 2004 .

[9]  Anthony J. Weiss,et al.  Array shape calibration using sources in unknown locations-a maximum likelihood approach , 1989, IEEE Trans. Acoust. Speech Signal Process..

[10]  Krishna R. Pattipati,et al.  Efficient 2D Sensor Location Estimation using Targets of Opportunity , 2013, J. Adv. Inf. Fusion.

[11]  Don Torrieri,et al.  Statistical Theory of Passive Location Systems , 1984, IEEE Transactions on Aerospace and Electronic Systems.

[12]  Daniel Cremers,et al.  Multiple source localization based on biased bearings using the intensity filter - approach and experimental results , 2011, 2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP).

[13]  M. Gavish,et al.  Effect of bias on bearing-only target location , 1990 .