An algebraic method for moving source localization using TDOA, FDOA, and differential Doppler rate measurements with receiver location errors

Abstract

To weaken the effect of receiver location error on localization accuracy and make the localization model closer to the practical scenario, this paper considers the receiver location errors, usually neglected in prior studies into the measurement model, and proposes an algebraic method for locating a moving source using time difference of arrival (TDOA), frequency difference of arrival (FDOA), and differential Doppler rate measurements. The proposed method is based on the pseudo-linear set of equations and two-step weighted least square estimator. Only noise values of receiver locations and three types of positioning measurements are available for processing. In addition, a new Cramér-Rao lower bound (CRLB) combining TDOA, FDOA, and differential Doppler rate in the presence of receiver location errors is also derived in this paper. Theoretical analysis and simulation results both indicate that the proposed method can attain CRLB at a moderate noise level, avoid the rank deficiency problem efficiently, and achieve a significant improvement over the existing methods.

Gauss Hermite H ∞ Filter for UAV Tracking Using LEO Satellites TDOA/FDOA Measurement—Part I

“…For example, in geolocation techniques, most of the researchers used Time Difference of Arrival (TDOA), Frequency Difference of Arrival (FDOA), Received Signal Strength (RSS), Gain Ratio of Arrival (GROA) angle of Arrival (AOA), and numerous of other conventional techniques [3]- [5]. To achieve high accuracy for emitter geolocation, numerous of the studies had combined between two or more traditional geolocation techniques such as TDOA/FDOA, TDOA/AOA, TDOA/PDOA, TDOA/GROA, as well as, TDOA/FDOA/Doppler frequency shift (DFS) measurement, and TDOA/FDOA/Differential Doppler Rate measurements [6]- [14]. Numerous researchers considered the problem of a mobile target tracking based on TDOA and FDOA measurements using Low Earth Orbit (LEO) satellites [42]- [45].…”

“…Numerous researchers considered the problem of a mobile target tracking based on TDOA and FDOA measurements using Low Earth Orbit (LEO) satellites [42]- [45]. Some conventional geolocation methods have proposed the Taylor chain expansion algorithm [15], some methods have proposed a weighted least squares algorithm [6], [14], and other methods have been worked out to improve these algorithms [17], [18]. To analyze the performance of their algorithms, the Cramer-Rao Lower Bound (CRLB) has been derived by most of these methods.…”

Passive Radar Source Localization Based on Cuckoo Search NVTDOA

“…e passive localization method belongs to a two-step localization system. In the first step, some signal parameters in the target source transmission are extracted, such as received signal strength [5], time difference of arrival (TDOA) [6,7], and angle of arrival (AOA) [8,9]. In the second step, the target position is estimated by solving a set of nonlinear localization equations.…”

An efficient estimator for source localization using TD and AOA measurements in MIMO radar systems

“…Pioneers have researched many methods of the target positioning in MIMO radar systems. Similar to the hyperbolic location [18][19][20], the maximum likelihood (ML) estimator corresponding to elliptic localization leads to a nonlinear and nonconvex optimization problem, whose global solution is difficult to obtain. There are some exist iterative algorithms, such as [6][7] directly solving the ML problem, but they need burdensome computation and an initial point sufficiently close to the actual target position to ensure convergence.…”