Distinction of Target and Chaff Signals by Suggesting the Optimal Waveform in Cognitive Radar using Artificial Neural Network

Subject Areas : Majlesi Journal of Telecommunication Devices

Seyed Mohammad Mahdi  Ziaei ¹ (PhD Candidate, Faculty of Electrical Engineering Department, Imam Hossein University, Tehran, Iran)
Pouriya  Etezadifar ² (Assistant Professor, Faculty of Electrical Engineering Department, Imam Hossein University, Tehran, Iran.)
Yaser  Norouzi ³ (Assistant Professor, Faculty of Electrical Engineering Department, Amirkabir University, Tehran, Iran)
Nadali  Zarei ⁴ (Assistant Professor, Faculty of Electrical Engineering Department, Imam Hossein University, Tehran, Iran)

Received: 2023-02-19 Accepted : 2023-04-28 Published : 2023-06-01

Keywords: Chaff, Target, radar, Waveform, Artificial Neural Network,

Abstract :

Using chaff to deflect missile guidance radar or missile seeker is a common and effective defense method in military vessels. To deal with this defensive method, focus on specific characteristics of the target and chaff signals. These features should be able to perform properly in different operating conditions of the radar or different environmental conditions that change the behavior of the radar’s return signals. But there is no feature that can distinguish the target from the target with appropriate accuracy in all conditions. In this article, a structure is presented for detecting chaff and target in a radar and has been able to improve the accuracy of target detection in presence of chaff. Also, to improve the performance of the radar with a cognitive approach, its transmitted waveform is optimally selected and changed at each stage. For this purpose, a feedback neural network with LSTM layers has been used. The general structure of the proposed method uses pre-processing on the received radar signals and extracts symmetry characteristics, Doppler spread and AGCD from it to contain the information for separating the target and chaff. Then, to remove the effect of noise on the features. Finally, these features are used to correctly distinguish the target from the chaff in a feed-forward neural network with fully connected layers. At the end, the effectiveness of this method is compared to the previous methods. It can be seen that the performance of the proposed system has made a significant improvement in accuracy of detection.

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