Recognition Chaff from target by determining the optimal waveform in the radar detector using artificial neural network

Deflecting missile’s radar guidance or missile’s seeker by chaff is a common and effective defensive method which is used in military vessels. To counter this defensive measure, methods for recognition targets from chaff have been developed, which generally focus on the special features of chaff or target. 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. But there is no effective feature that can distinguish target from chaff with appropriate accuracy in all conditions, and different features do not have the same performance in different environmental conditions or radar working parameters such as different waveforms and as a result their performance changes. In this article, by using artificial neural network, a structure is presented for detecting chaff and target in a radar, whose performance in different environmental conditions and waveforms has been better than the existing methods and significantly improved the accuracy of target detection from chaff and led to appropriate accuracy. 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, which suggest the optimal waveform according to changes in the environment. The general structure of the proposed method is so that first of all, by using pre-processing on the received radar data, the features of symmetry, Doppler spread and AGCD are extracted, which contain information that separates the target from the chaff. Then, to remove the effect of noise on these features, thresholding is used. Finally, these features are used to correctly distinguish the target from the chaff in a feed-forward neural network with fully connected layers. On the other hand, in each step, by using the waveform suggestion network, the optimal waveform is selected and used for the next moment. Thus, the proposed structure is an intelligent machine that, in addition to recognizing the target from the signal at each moment, determines what the optimal waveform should be at the next moment. At the end, the effectiveness of this method in comparison to the previous methods, that is, thresholding on the characteristics of symmetry, Doppler and AGCD in distinguishing the target from the chaff is evaluated. It is observable the performance of the proposed system has made a significant improvement.