A bias‐reduced solution for target localization with illuminator of opportunity passive radar

Abstract

Summary An improved solution for target localization on the basis of the realistic distance‐dependent noises in illuminator of opportunity passive radar and an algebraic reduction method of the bias, which exists in the two‐stage weighted least squares (TSWLS) method is proposed. The classic TSWLS and its improved solutions have great localization performances just on the basis of two approximations; that is, setting the noise to a constant and ignoring the high‐order terms of measurement noises. It is these two approximations that lead to a suboptimal solution with bias. The bias of TSWLS has a significant influence on the target localization in illuminator of opportunity passive radar that has lower measurement accuracy and higher noises than common radars. Therefore, this paper starts by taking into consideration the realistic distance‐dependent characteristics of time‐of‐arrival (TOA) and frequency‐of‐arrival (FOA) noises and improving TSWLS method. Then, the bias of the improved TSWLS method is analyzed and an algebraic bias‐reduced solution on the basis of weighted least squares (WLS) is developed. Numerical simulations demonstrate that, compared with the existing methods, the proposed bias‐reduced solution attains the Cramer‐Rao lower bound (CRLB) better and reduces the bias effectively.

A Mixed-Integer Solution for TDOA Based Direct Positioning of Digital Modulation Signal

“…The conventional decentralized positioning methods require two-steps procedure [1][2][3]. Under the decentralized framework, firstly, the measurements, such as the angle of arrival (AOA) [4,5], the time of arrival (TOA) [6,7], the time difference of arrival (TDOA) [8,9], frequency of arrival (FOA) [10,11], the frequency difference of arrival (FDOA) [12,13], the received signal strength (RSS) [14], and the gain ratios of arrival (GROA) [15,16], are extracted from the received signals, and then the data collected by sensors are stored in data processing center to estimate the source position using various location methods. However, in order to achieve the optimal performance of the two-step methods, measurements must correspond to a single emitter.…”

A Lagrangian Multiplier Method for TDOA and FDOA Positioning of Multiple Disjoint Sources with Distance and Velocity Correlation Constraints

“…It has received significant attention in various areas of signal processing research including unmanned aerial vehicle (UAV) [1,2], passive radar [3][4][5], microseismic sources [6][7][8], and underwater acoustics [9][10][11]. Typical measurements including the angle of arrival (AOA) [12,13], time of arrival (TOA) [14,15], time difference of arrival (TDOA) [15][16][17], frequency of arrival (FOA) [18,19], frequency difference of arrival (FDOA) [20,21], received signal strength (RSS) [22,23], and gain ratios of arrival (GROA) [24,25] (or some combination of them) can be used to estimate the source location. To the best of our knowledge, TDOA positioning is one of the most widely used schemes.…”