Abstract
A passive wireless positioning system could be used to detect the location of low-level airborne targets such as drones or unmanned aircraft systems from the electromagnetic emission detected at spatially deployed ground receiving stations GRSs . The multiangulation system proposed in this paper makes use of the angle of arrival AOA of the transmitted signal from the target to estimate its position through a 2-stage process. The AOA is the positiondependent signal parameter PDSP obtained from the target emission in the first stage, and using the PDSP and GRSs, the target location is estimated in the second stage by the angulation algorithm. Noise in the received signal results in AOA estimation error and subsequently error in the position estimation PE . This paper focuses on the angulation process, which is the second stage of the multiangulation target location estimation process. Analysis is conducted to determine the correlation between the PE error and the condition number of the coefficient matrix of a multiangulation position estimation system. The results based on Monte Carlo simulations show that both condition number and PE error distribution increase with the target range, where the higher condition number values correlate with higher PE error values appearing around 80◦ to 110◦ and 260◦ to 280◦ of both distributions.
Highlights
Position estimation (PE) of aircraft such as drones, attack helicopters, and fighter jets is one of the fundamental functions of electronic warfare (EW) and plays a vital role in defense strategy, security, and anti-hostile actions [1]
Error in angle of arrival (AOA) estimation caused by noise in the received signal subsequently leads to PE error in a multiangulation system
The proposed approach in this paper is based on mathematical derivations to arrive at the condition number of the coefficient matrix and the PE error of the multiangulation system via singular value decomposition (SVD)-based eigenvalue approach and expectation operations
Summary
Position estimation (PE) of aircraft such as drones, attack helicopters, and fighter jets is one of the fundamental functions of electronic warfare (EW) and plays a vital role in defense strategy, security, and anti-hostile actions [1]. Its broad coverage falls in three main subdivisions: electronic support (ES), electronic attack (EA), and electronic protection (EP) [2,3,4] Among this division, ES performs such tasks as characterization or detection of signals used by the aircraft referred to as spectrum surveillance, a technique that records, processes, and evaluates recorded spectrum data [5]. Passive PE systems like MPE systems based on the angle of arrival (AOA) of the signal and multilateration (MLAT) based on time difference of arrival (TDOA) could complement radar in PE of aircrafts [9] While the former uses an angulation algorithm to determine the PE, the later woks with a lateration algorithm to compute the PE.
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