Estimating Location Accuracy of Stationary Emitter in Presence of Biasing in Receiver Position and Velocity by Exploiting Cross Ambiguity Function
Anupam Sharma1, Suman Agrawal2, Charul Bhatnagar3, D. S. Chauhan4

1Anupam Sharma*, Defence Electronics Research Laboratory, DRDO, Hyderabad, India.
2Suman Agrawal, Defence Electronics Research Laboratory, DRDO, Hyderabad, India.
2Prof.Charul Bhatnagar, Professor in the Department of Computer Engineering and Applications, GLA University, Mathura, India.
4Prof. D. S. Chauhan, Pro-Chancellor, GLA University, Mathura, India. 

Manuscript received on February 01, 2020. | Revised Manuscript received on February 05, 2020. | Manuscript published on February 30, 2020. | PP: 271-276 | Volume-9 Issue-3, February, 2020. | Retrieval Number: C4738029320/2020©BEIESP | DOI: 10.35940/ijeat.C4738.029320
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Abstract: This paper proposes CAF algorithm to estimate localisation accuracy of a stationary emitter which is being monitored by a pair of sensors mounted on high altitudes. It computes joint Time Difference of Arrival (TDOA) and Frequency Difference of Arrival (FDOA) using Cross Ambiguity Function (CAF) and measures geolocation accuracy in presence of biasing in sensor position and velocity. Previous work in this area utilizes TDOA and FDOA measurements with known sensor kinematics which is fed to Maximum Likelihood or Least Squares algorithm for post processing. However it is computation demanding. In the present work, surface peaks of TDOA and FDOA values are directly mapped to geographic coordinates. This method is computationally efficient. As sensor and emitter geometry keeps changing over time due to moving sensors, multiple CAF snapshots are taken for emitter geolocation. Simulations are carried out using MATLAB. It is observed that at 30 dB SNR, location accuracy of stationary emitter is 100 m at known sensor kinematics and by introducing bias in the receiver position and velocity, it is 200 meters. These measurements are well within and in accordance with theoretical developments.
Keywords: Cross Ambiguity Function, Frequency Difference of Arrival, Localisation, Time Difference of Arrival, Unmanned Aerial Vehicle.