Effective Passive Multitarget Localization Using Maximum Likelihood

Yasir Munir,Ali M Aseere,Muhammad Umar Aftab,Habib Shah,Danish Shehzad

doi:10.1155/2021/6567346

Abstract

Localization of multiple targets is a challenging task due to immense complexity regarding data fusion received at the sensors. In this context, we propose an algorithm to solve the problem for an unknown number of emitters without prior knowledge to address the data fusion problem. The proposed technique combines the time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measurement data fusion which further uses the maximum likelihood of the measurements received at each sensor of the surveillance region. The measurement grids of the sensors are used to perform data association. The simulation results show that the proposed algorithm outperforms the multipass grid search and further effectively eliminated the ghost targets created due to the fusion of measurements received at each sensor. Moreover, the proposed algorithm reduces the computational complexity compared to other existing algorithms as it does not use repeated steps for convergence or any biological evolutions. Furthermore, the experimental testing of the proposed technique was executed successfully for tracking multiple targets in different scenarios passively.

Highlights

In the modern era of wireless technologies, the localization of the target sensors such as aircraft, ships, or unmanned vehicles is challenging
Emitter devices have the benefit of providing the frequency difference of arrival (FDOA) which is the result of the relative motion of the source and the target, which improves the accuracy with the estimation of the velocity of the target [2]
This research proposed an algorithm based on time difference of arrival (TDOA)/ FDOA and optimization of the shortlisted measurement

Summary

Introduction

In the modern era of wireless technologies, the localization of the target sensors such as aircraft, ships, or unmanned vehicles is challenging. The proposed algorithm is able to localize the unknown number of targets having the complexity of the multiple sensors with multiple grids; the algorithm is not complex as it is not using repeated steps for convergence, not using any biological evolutions used in the existing algorithms; in a single scan, it computed likelihood for all sensors using only one parameter grid and passes the results for multitarget estimation It eliminates the localization of the ghost target which becomes an issue when the measurement data fusion is received; here, we call them possible candidates when considering the combinations of the measurements from different sensors. The fourth section shows the results of the algorithm, and the last section concludes this research paper

Applications Using Maximum Likelihood for Multitargets

Proposed Multitarget Localization and Tracking

Simulations

Concluding Remarks