Separating ADS-B signal with unknown interweaving multiplicity


	using DPRNN

Abstract

Abstract:

The interweaving of multiple signals is an inevitable problem in automatic dependent surveillance -broadcast

(ADS-B) system. Existing single-antenna ADS-B signal de-interweaving methods mostly require accurate estimation of parameters such as the interweaving multiplicity and relative delay, and the estimation accuracy often seriously affects the final separation performance. To address this problem, this paper uses dual-path recurrent neural

network (DPRNN) to separate ADS-B reception signals without the need to estimate any parameters. The network input is ADS-B signals with unknown interweaving multiplicity, including pure noise signal, no interweaving

signal, double interweaving signal and triple interweaving signal. The network is fixed to output three signals, and

when the interweaving signal contains i ADS-B signals, the corresponding source signals have i ADS-B signals

and (3 - i) noise signals. Simulation experiments show that the separation accuracy is above 95% when the interweaving multiplicity is no more than three. This study provides a new solution for ADS-B signal interweaving separation, which has practical value.

Key words:

automatic dependent surveillance-broadcast (ADS-B), signal interweaving, signal separation, dual-path recurrent neural network (DPRNN)

CLC Number:

WANG Wenyi, WANG Jianshe.

Separating ADS-B signal with unknown interweaving multiplicity

using DPRNN

[J]. Journal of Civil Aviation University of China, 2025, 43(3): 75-80.

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Separating ADS-B signal with unknown interweaving multiplicity

using DPRNN

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