Biologically Inspired Decentralized Target Location Estimation Using Neural Networks

Mathew Mithra Noel(1*), Ankit Sharma(2)

(1) School of Electrical Engineering VIT University Vellore-632014, Tamil Nadu, India
(2) 6-Alka Puri near Tagore Nagar Dayal Bagh, Agra-282005, Uttar Pradesh, India
(*) Corresponding author


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Abstract


Animals such as spiders are known to effectively hunt in total darkness with the aid of arrays of vibration sensors that detect acoustic signals received through the ground. Animals also use neural networks to analyze acoustic signals generated by motion of prey to effectively locate prey. Conventional centralized target location systems suffer from communicational and computational bottlenecks. Thus massively parallel robust decentralized neural network based target location schemes are of interest. In this paper a generic feedforward and radial basis network are trained to successfully learn to locate a target using output from a linear array of acoustic sensors. The neural network based approach is compared to a least square estimation scheme. Recent advances in the development of analog and digital hardware implementations of neural networks allow decentralized neural network based target location schemes to be implemented. A decentralized neural network based target location estimation system that avoids communication bottlenecks posed by a centralized information fusion center is proposed. The goal of this research effort is to explore possible schemes for locating a ground based target solely using acoustic signals transmitted through the ground. Such a scheme has an advantage because it does not need to emit or receive electromagnetic radiation and hence can be operated without being detected.
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Keywords


Neural Networks; Target Location; Sensor Networks; Nonlinear Sensors

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References


Watkins S.S., Chau P.M., Tawel, R., "A radial basis function neurocomputer implemented with analog VLSI circuits," IJCNN., International Joint Conference on Neural Networks, vol.2, no., pp.607-612 vol.2, 7-11 Jun 1992

Eberhardt S.P., Tawel R., Brown T.X., Daud T., Thakoor A.P., "Analog VLSI neural networks: implementation issues and examples in optimization and supervised learning, " IEEE Transactions on Industrial Electronics, vol.39, no.6, pp.552-564, Dec 1992

Baxter D.J., Murray A.F., Martin Reekie H., Churcher S., Hamilton A., "Analogue CMOS techniques for VLSI neural networks: process invariant circuits and devices," IEE Colloquium on Advances in Analogue VLSI, vol., no., pp.6/1-6/4, 14 May 1991

Card, H.C., McNeill D.K., "On-chip learning in neurocomputers," Canadian Conference on Electrical and Computer Engineering, vol.1, no., pp.182-185 vol.1, 26-29 May 1996

Ruixin Li, Jun Zhang, Taiyong Wang, Pu Han, Lijing Zhang, "TMS320 DSP based neural networks on fault diagnostic system of turbo-generator, "IEEE International Conference on Systems Man and Cybernetics, vol.4, no., pp. 3781- 3786 vol.4, 5-8 Oct. 2003

Oshman Y., Davidson P., “Optimization of observer trajectories for bearing-only target localization”, IEEE Trans on Aerospace and Electronic Systems, vol. 35, no.3, pp 892-902, July 1999

Hu Y.H., Li D., “Energy based collaborative source localization using acoustic micro-sensor array:, IEEE Workshop on Multimedia Signal Processing. Pp.371-375, Dec 2002

Sheng X., Hu Y.H., “Maximum likelihood multiple-source localization using acoustic energy measurement with wireless sensor networks”, IEEE Trans. Signal Processing, vol.53, no.1, pp.44-53, Jan 2005

Nardone S.C., Lindgren A.G., Gong K.F., “Fundamental properties and performance of conventional bearings-only target motion analysis”, IEEE Trans. On Automatic Control,AC-29(Sept. 1984), 775-787.

Naidu V.P.S, Raol J.R., “Target Tracking with Multi Acoustic Array Sensors Data” Defense Science Journal, Vol. 57, No. 3, May 2007, pp. 289-303

Chiradeja, P., Ngaopitakkul, A., Prediction of fault location in overhead transmission line and underground distribution cable using probabilistic neural network, (2013) International Review of Electrical Engineering (IREE), 8 (2), pp. 762-768.

Ma, T.-T., Adaptive inverse control schemes based on fuzzy neural networks for induction motor drives, (2010) International Review of Electrical Engineering (IREE), 5 (4), pp. 1563-1570.

Mosavi, M.R., Estimation of pseudo-range DGPS corrections using Neural Networks trained by evolutionary algorithms, (2010) International Review of Electrical Engineering (IREE), 5 (6), pp. 2715-2721.

Menniti, D., Scordino, N., Sorrentino, N., A novel approach to forecast day-ahead electricity prices by means of neural networks using groups of similar hours, (2012) International Review of Electrical Engineering (IREE), 7 (4), pp. 5119-5133.


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