Abstract
Some passive sensors can measure only directions of arrival of signals, but the real positions of signal sources are often desirable, which can be estimated by combining distributed passive sensors as a network. However, passive observations should be correctly associated first. This paper studies the multi-target data association and signal localization problem in distributed passive sensor networks. With angle-only measurements from distributed passive sensors, multiple lines in a 3-dimensional (3D) scenario can be built and then those that will intersect in a small volume in 3D are classified into the same source. The center of the small volume is taken as an estimate of the signal source position, whose statistical distributions are formulated. If the minimum distance is less than an association threshold, then two lines are considered to be from the same signal source. In numerical results, the impacts of angle measurement accuracy and platform self-positioning accuracy are analyzed, indicating that this method can achieve a prescribed data association rate and a high positioning performance with a low computation cost.
Highlights
Academic Editors: RatnasinghamUnlike active sensors such as radars, passive sensors do not transmit signals and have no anti-jamming problem [1,2]
Consider a scenario where two sensors are installed on two aircraft and three targets of interest are in the scope
This paper studies the data association and signal source localization problems with distributed passive sensors with angle-only observations
Summary
Unlike active sensors such as radars, passive sensors do not transmit signals and have no anti-jamming problem [1,2]. In order to estimate the positions of signal sources, passive sensors with angle-only observations can be connected with communication links into a network to measure signals sources from different spatial locations. In this case, an algorithm to combine the angle-only observations is needed [3,4,5,6,7]. The basic concept is that if multiple passive sensors simultaneously measure the signal sources without measurement error, these measurement lines of sight will intersect to the target position. The data association process and target location process of this method are closely combined, which ensures a lower algorithm complexity and a better positioning performance. Stands for the block-diagonal matrix formed by the matrices A1 , A2 , . . . , A N
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