Fuzzy logic classifier design for air targets recognition based on HRRP

Liang, Jing; Zhu, Fengchao

doi:10.1016/j.phycom.2014.04.007

Cited by 9 publications

(2 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It can be jammed by electronic countermeasure jamming (ECM). Other than jammers, several sensitivity issues should also be considered in HRRP target recognition [28]. The first one is time-shift sensitivity, since the distance between the target and the radar is unknown, the initial position of the main signal components could vary with the measurements.…”

Section: A Preprocessing and Sensitivity Analysismentioning

confidence: 99%

“…As discussed above, in the process of HRRP data obtaining, time-shift sensitivity is caused by target range profile shift in range unit, which is disadvantage for the recognition especially in the template matching algorithm taking range measurement. So in traditional method, the preprocessing methods such as alignment method and extracting feature of translation invariance are necessary [28]. But these methods increase the complexity of radar auto-recognition systems and have a certain degree of error.…”

Section: ) Radar Hrrp Shift Robust Convolutional Neural Network(rsrnet)mentioning

confidence: 99%

See 1 more Smart Citation

Radar HRRP Target Recognition via Semi-Supervised Multi-Task Deep Network

Zhao

Liang

et al. 2019

IEEE Access

Self Cite

View full text Add to dashboard Cite

Feature representation based on the high resolution range profile (HRRP) is the key technology in radar automatic target recognition(RATR). In this paper, we design a deep-u-blind denoising network(DUBDNet) to extract features with high-noise-stability. The fully convolutional DUBDNet is based on autoencoder and employs fusion layers to transfer input features to high dimensional space. Then radar HRRP shift-robust convolutional neural network(RSRNet) is proposed as the classifier. In the experiment, two radar sensors are used to measure HRRP signals of warplanes and civil airplanes. RSRNet performs high robustness to HRRP time-shift sensitivity via testing translation data. This is also a proof that convolutional neural network(CNN) is shift-robust on HRRP target recognition. Trained with noise-tonoise, DUBDNet can achieve blind-denoising in low signal-to-noise ratio(SNR) and significantly improve the correct recognition rate of targets. When the SNR of input HRRP signals is less than 5 dB, DUBDNet can increase the SNR by 10 dB. When the input SNR is −15 dB, output SNR can be increased by 15 dB and the correct recognition rate of targets can be increased by 15%.

show abstract

Section: A Preprocessing and Sensitivity Analysismentioning

confidence: 99%

Section: ) Radar Hrrp Shift Robust Convolutional Neural Network(rsrnet)mentioning

confidence: 99%