Open AccessImproving Accuracy of Source Localization Algorithms Using Kalman Filter Estimator
Author(s) -
Sherly George,
Dhanya Nandanan,
P. Muralikrishna
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1921/1/012022
Subject(s) - multilateration , geolocation , fdoa , cramér–rao bound , common emitter , algorithm , estimator , kalman filter , computer science , signal (programming language) , position (finance) , taylor series , acoustics , mathematics , electronic engineering , estimation theory , engineering , statistics , artificial intelligence , node (physics) , physics , mathematical analysis , finance , world wide web , economics , programming language
Passive geolocation of emitters provides benefits to military and civilian surveillance and in the field of aerospace. Passive geolocation can be done by exploiting some of the parameters of the signal transmitted by the source or emitter. In this paper the Time Difference Of Arrival (TDOA) of the signal at different receivers is used to estimate the position of the emitter. This paper aims at presenting the research for the development of an algorithm which can be used in a distributed network of acoustic sensors consisting of an emitter and five stationary receivers. By means of measurement of time difference of arrival of underwater acoustic signal the source position is estimated using three different algorithms. Taylor series, closed form and two stage weighted least square methods are used to estimate the stationary source location by TDOAmeasurements of received signal and compared on the basis of Cramer Rao Lower Bound (CRLB). Comparison of performance between these methods are done and analysed.Use of a kalman filter is proposed to improve the accuracy of all the three methods and the results are compared and analysed.