Sea clutter constituent synthesis approach based on a new decomposition model

Zhang, Shengmiao; Li, Jun; He, Zishu; Zhang, Wei; Li, Huiyong

doi:10.1186/s13634-015-0264-4

Cited by 1 publication

(1 citation statement)

References 27 publications

(28 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In 2002, McDonald et al [9] used RBF network and a local linear technique to predict sea clutter that collected by AN/APS 506 airborne maritime surveillance radar, the prediction errors of these two methods is approximately 0.0032 that it is unclear whether the RBF network predictor is better under the real world detection scenarios. Zhang et al [13] proposed a decomposition model for sea clutter processing, and used RBF predictor for sea clutter prediction under different sea states, and obtained stable fitting performance.…”

Section: Introductionmentioning

confidence: 99%

Sea Clutter Amplitude Prediction Using a Long Short-Term Memory Neural Network

Zhang

et al. 2019

Remote Sensing

View full text Add to dashboard Cite

In the marine environment, shore-based radars play an important role in military surveillance and sensing. Sea clutter is one of the main factors affecting the performance of shore-based radar. Affected by marine environmental factors and radar parameters, the fluctuation law of sea clutter amplitude is very complicated. In the process of training a sea clutter amplitude prediction model, the traditional method updates the model parameters according to the current input data and the parameters in the current model, and cannot utilize the historical information of sea clutter amplitude. It is only possible to learn the short-term variation characteristics of the sea clutter. In order to learn the long-term variation law of sea clutter, a sea clutter prediction system based on the long short-term memory neural network is proposed. Based on sea clutter data collected by IPIX radar, UHF-band radar and S-band radar, the experimental results show that the mean square error of this prediction system is smaller than the traditional prediction methods. The sea clutter suppression signal is extracted by comparing the predicted sea clutter data with the original sea clutter data. The results show that the proposed sea clutter prediction system has a good effect on sea clutter suppression.

show abstract

Section: Introductionmentioning

confidence: 99%

Sea Clutter Amplitude Prediction Using a Long Short-Term Memory Neural Network

Zhang

et al. 2019

Remote Sensing

View full text Add to dashboard Cite

show abstract

Sea clutter constituent synthesis approach based on a new decomposition model

Cited by 1 publication

References 27 publications

Sea Clutter Amplitude Prediction Using a Long Short-Term Memory Neural Network

Sea Clutter Amplitude Prediction Using a Long Short-Term Memory Neural Network

Contact Info

Product

Resources

About