In the complex countermeasure environment,the pulse description words(PDWs)of the same type of multi-function radar emitters are similar in multiple dimensions.Therefore,it is difficult for conventional methods to dei...In the complex countermeasure environment,the pulse description words(PDWs)of the same type of multi-function radar emitters are similar in multiple dimensions.Therefore,it is difficult for conventional methods to deinterleave such emitters.In order to solve this problem,a pulse deinterleaving method based on implicit features is proposed in this paper.The proposed method introduces long short-term memory(LSTM)neural networks and statistical analysis to mine new features from similar PDW features,that is,the variation law(implicit features)of pulse sequences of different radiation sources over time.The multi-function radar emitter is deinterleaved based on the pulse sequence variation law.Statistical results show that the proposed method not only achieves satisfactory performance,but also has good robustness.展开更多
In most multi-function phased array radar applications, multiple missions, including airspace searching and target tracking, are usually performed simultaneously by the digital beam-forming technique and the time divi...In most multi-function phased array radar applications, multiple missions, including airspace searching and target tracking, are usually performed simultaneously by the digital beam-forming technique and the time dividing method. This paper presents a novel method to classify pulses of different missions from an interleaved pulse sequence emitted by the same radar, which is significant in radar electronic reconnaissance and electronic support measure. Firstly, two hypotheses, i.e., pulse relativity within the same mission and pulse independence among different missions, are proposed by analyzing the antenna pattern and the beam scheduling method of the phased array radar. Based on the above two hypotheses, an optimal model for pulse classification is exploited with pulse amplitude series, where the absolute-value sum of second order difference is taken as the optimal kernel to measure sequence smooth continuity. Finally, several pieces of sequences under different numbers of missions and tracking data rates are simulated for algorithm verification. The simulation results show that the long data length and the high data rate will increase classification efficiency due to the validity of the two hypotheses in sufficient pulse amplitude sequence.展开更多
基金the National Major Research&Development project of China(2018YFE0206500)the National Natural Science Foundation of China(62071140)+1 种基金the Program of China International Scientific and Technological Cooperation(2015DFR10220)the Technology Foundation for Basic Enhancement Plan(2021-JCJQ-JJ-0301).
文摘In the complex countermeasure environment,the pulse description words(PDWs)of the same type of multi-function radar emitters are similar in multiple dimensions.Therefore,it is difficult for conventional methods to deinterleave such emitters.In order to solve this problem,a pulse deinterleaving method based on implicit features is proposed in this paper.The proposed method introduces long short-term memory(LSTM)neural networks and statistical analysis to mine new features from similar PDW features,that is,the variation law(implicit features)of pulse sequences of different radiation sources over time.The multi-function radar emitter is deinterleaved based on the pulse sequence variation law.Statistical results show that the proposed method not only achieves satisfactory performance,but also has good robustness.
文摘In most multi-function phased array radar applications, multiple missions, including airspace searching and target tracking, are usually performed simultaneously by the digital beam-forming technique and the time dividing method. This paper presents a novel method to classify pulses of different missions from an interleaved pulse sequence emitted by the same radar, which is significant in radar electronic reconnaissance and electronic support measure. Firstly, two hypotheses, i.e., pulse relativity within the same mission and pulse independence among different missions, are proposed by analyzing the antenna pattern and the beam scheduling method of the phased array radar. Based on the above two hypotheses, an optimal model for pulse classification is exploited with pulse amplitude series, where the absolute-value sum of second order difference is taken as the optimal kernel to measure sequence smooth continuity. Finally, several pieces of sequences under different numbers of missions and tracking data rates are simulated for algorithm verification. The simulation results show that the long data length and the high data rate will increase classification efficiency due to the validity of the two hypotheses in sufficient pulse amplitude sequence.
