Current methods for radar target detection usually work on the basis of high signal-to-clutter ratios.In this paper we propose a novel convolutional neural network based dual-activated clutter suppression algorithm,to...Current methods for radar target detection usually work on the basis of high signal-to-clutter ratios.In this paper we propose a novel convolutional neural network based dual-activated clutter suppression algorithm,to solve the problem caused by low signal-to-clutter ratios in actual situations on the sea surface.Dual activation has two steps.First,we multiply the activated weights of the last dense layer with the activated feature maps from the upsample layer.Through this,we can obtain the class activation maps(CAMs),which correspond to the positive region of the sea clutter.Second,we obtain the suppression coefficients by mapping the CAM inversely to the sea clutter spectrum.Then,we obtain the activated range-Doppler maps by multiplying the coefficients with the raw range-Doppler maps.In addition,we propose a sampling-based data augmentation method and an effective multiclass coding method to improve the prediction accuracy.Measurement on real datasets verified the effectiveness of the proposed method.展开更多
It is an important issue to study sea clutter suppression because it could interfere with the detection of targets above the sea surface severely. Spatial spectrum analyses show that the majority of sea clutter has lo...It is an important issue to study sea clutter suppression because it could interfere with the detection of targets above the sea surface severely. Spatial spectrum analyses show that the majority of sea clutter has low-frequency characteristics, compared to the high-frequency characteristics of the targets. This paper proposes a frequency-based spatial tracking filter to suppress sea clutter to facilitate targets identification. Experimental results show that the signal-to-clutter ratio can increase by more than 10 dB after filtering and the algorithm is feasible for practical use. In addition, the filtering equation can be optimized to maximize the signal-to-clutter ratio improvement. The equation parameters can also be adjusted to give a proper cut-off frequency for different targets and clutter.展开更多
Conventional scan-to-scan integration correlation （SIC） algorithms can detect small and stationary targets. However, they are ineffective in detecting small and fast-moving targets. This paper presents an improved S...Conventional scan-to-scan integration correlation （SIC） algorithms can detect small and stationary targets. However, they are ineffective in detecting small and fast-moving targets. This paper presents an improved SIC algorithm together with clutter suppression measures that remove or decrease sea clutter. The algorithm divides the scan-to-scan integration （SI） into two branches, one provides optimum clutter attenuation by means of SI weighting while the other ensures that targets are detected even if they are fast-moving. Sea clutter suppression can lower detection thre-sholds and, at the same time, increase signal-to-clutter ratio. Simulation results show that the proposed approach greatly improves the detection capability for warship radar.展开更多
文摘Current methods for radar target detection usually work on the basis of high signal-to-clutter ratios.In this paper we propose a novel convolutional neural network based dual-activated clutter suppression algorithm,to solve the problem caused by low signal-to-clutter ratios in actual situations on the sea surface.Dual activation has two steps.First,we multiply the activated weights of the last dense layer with the activated feature maps from the upsample layer.Through this,we can obtain the class activation maps(CAMs),which correspond to the positive region of the sea clutter.Second,we obtain the suppression coefficients by mapping the CAM inversely to the sea clutter spectrum.Then,we obtain the activated range-Doppler maps by multiplying the coefficients with the raw range-Doppler maps.In addition,we propose a sampling-based data augmentation method and an effective multiclass coding method to improve the prediction accuracy.Measurement on real datasets verified the effectiveness of the proposed method.
文摘It is an important issue to study sea clutter suppression because it could interfere with the detection of targets above the sea surface severely. Spatial spectrum analyses show that the majority of sea clutter has low-frequency characteristics, compared to the high-frequency characteristics of the targets. This paper proposes a frequency-based spatial tracking filter to suppress sea clutter to facilitate targets identification. Experimental results show that the signal-to-clutter ratio can increase by more than 10 dB after filtering and the algorithm is feasible for practical use. In addition, the filtering equation can be optimized to maximize the signal-to-clutter ratio improvement. The equation parameters can also be adjusted to give a proper cut-off frequency for different targets and clutter.
文摘Conventional scan-to-scan integration correlation （SIC） algorithms can detect small and stationary targets. However, they are ineffective in detecting small and fast-moving targets. This paper presents an improved SIC algorithm together with clutter suppression measures that remove or decrease sea clutter. The algorithm divides the scan-to-scan integration （SI） into two branches, one provides optimum clutter attenuation by means of SI weighting while the other ensures that targets are detected even if they are fast-moving. Sea clutter suppression can lower detection thre-sholds and, at the same time, increase signal-to-clutter ratio. Simulation results show that the proposed approach greatly improves the detection capability for warship radar.
