This paper derives a mathematical description of the complex stretch processor’s response to bandlimited Gaussian noise having arbitrary center frequency and bandwidth. The description of the complex stretch processo...This paper derives a mathematical description of the complex stretch processor’s response to bandlimited Gaussian noise having arbitrary center frequency and bandwidth. The description of the complex stretch processor’s random output comprises highly accurate closed-form approximations for the probability density function and the autocorrelation function. The solution supports the complex stretch processor’s usage of any conventional range-sidelobe-reduction window. The paper then identifies two practical applications of the derived description. Digital-simulation results for the two identified applications, assuming the complex stretch processor uses the rectangular, Hamming, Blackman, or Kaiser window, verify the derivation’s correctness through favorable comparison to the theoretically predicted behavior.展开更多
Feature reduction is a key process in pattern recognition. This paper deals with the feature reduction methods for a time-shift invariant feature, power spectrum, in Radar Automatic Target Recognition (RATR) using Hig...Feature reduction is a key process in pattern recognition. This paper deals with the feature reduction methods for a time-shift invariant feature, power spectrum, in Radar Automatic Target Recognition (RATR) using High-Resolution Range Profiles (HRRPs). Several existing feature reduction methods in pattern recognition are analyzed, and a weighted feature reduction method based on Fisher's Discriminant Ratio (FDR) is proposed in this paper. According to the characteristics of radar HRRP target recognition, this proposed method searches the optimal weight vector for power spectra of HRRPs by means of an iterative algorithm, and thus reduces feature dimensionality. Compared with the method of using raw power spectra and some existing feature reduction methods, the weighted feature reduction method can not only reduce feature dimensionality, but also improve recognition performance with low computation complexity. In the recognition experiments based on measured data, the proposed method is robust to different test data and achieves good recognition results.展开更多
This article introduces a new class of ideals, namely, the sπr ideals. It is shown that every regular square matrix over sπr ideals of a ring admits a diagonal reduction.
Autonomous underwater vehicles(AUVs)have various applications in both military and civilian fields.A wider operation area and more complex tasks require better overall range performance of AUVs.However,until recently,...Autonomous underwater vehicles(AUVs)have various applications in both military and civilian fields.A wider operation area and more complex tasks require better overall range performance of AUVs.However,until recently,there have been few unified criteria for evaluating the range performance of AUVs.In the present work,a unified range index,i.e.,L^(*),considering the cruising speed,the sailing distance,and the volume of an AUV,is proposed for the first time,which can overcome the shortcomings of previous criteria using merely one single parameter,and provide a uniform criterion for the overall range performance of various AUVs.After constructing the expression of the L^(*)index,the relevant data of 49 AUVs from 12 countries worldwide have been collected,and the characteristics of the L^(*)range index in different countries and different categories were compared and discussed.Furthermore,by analyzing the complex factors affecting the range index,methods to enhance the L^(*)range index value,such as efficiency enhancement and drag reduction,have been introduced and discussed.Under this condition,the work proposes a unified and scientific criterion for evaluating the range performance of AUVs for the first time,provides valuable theoretical insight for the development of AUVs with higher performance,and then arouses more attention to the application of the cutting-edge superlubricity technology to the field of underwater vehicles,which might greatly help to accelerate the coming of the era of the superlubricitive engineering.展开更多
In order to extract the richer feature information of ship targets from sea clutter, and address the high dimensional data problem, a method termed as multi-scale fusion kernel sparse preserving projection(MSFKSPP) ba...In order to extract the richer feature information of ship targets from sea clutter, and address the high dimensional data problem, a method termed as multi-scale fusion kernel sparse preserving projection(MSFKSPP) based on the maximum margin criterion(MMC) is proposed for recognizing the class of ship targets utilizing the high-resolution range profile(HRRP). Multi-scale fusion is introduced to capture the local and detailed information in small-scale features, and the global and contour information in large-scale features, offering help to extract the edge information from sea clutter and further improving the target recognition accuracy. The proposed method can maximally preserve the multi-scale fusion sparse of data and maximize the class separability in the reduced dimensionality by reproducing kernel Hilbert space. Experimental results on the measured radar data show that the proposed method can effectively extract the features of ship target from sea clutter, further reduce the feature dimensionality, and improve target recognition performance.展开更多
文摘This paper derives a mathematical description of the complex stretch processor’s response to bandlimited Gaussian noise having arbitrary center frequency and bandwidth. The description of the complex stretch processor’s random output comprises highly accurate closed-form approximations for the probability density function and the autocorrelation function. The solution supports the complex stretch processor’s usage of any conventional range-sidelobe-reduction window. The paper then identifies two practical applications of the derived description. Digital-simulation results for the two identified applications, assuming the complex stretch processor uses the rectangular, Hamming, Blackman, or Kaiser window, verify the derivation’s correctness through favorable comparison to the theoretically predicted behavior.
基金Partially supported by the National Natural Science Foundation of China (No.60302009)the National Defense Advanced Research Foundation of China (No.413070501).
文摘Feature reduction is a key process in pattern recognition. This paper deals with the feature reduction methods for a time-shift invariant feature, power spectrum, in Radar Automatic Target Recognition (RATR) using High-Resolution Range Profiles (HRRPs). Several existing feature reduction methods in pattern recognition are analyzed, and a weighted feature reduction method based on Fisher's Discriminant Ratio (FDR) is proposed in this paper. According to the characteristics of radar HRRP target recognition, this proposed method searches the optimal weight vector for power spectra of HRRPs by means of an iterative algorithm, and thus reduces feature dimensionality. Compared with the method of using raw power spectra and some existing feature reduction methods, the weighted feature reduction method can not only reduce feature dimensionality, but also improve recognition performance with low computation complexity. In the recognition experiments based on measured data, the proposed method is robust to different test data and achieves good recognition results.
文摘This article introduces a new class of ideals, namely, the sπr ideals. It is shown that every regular square matrix over sπr ideals of a ring admits a diagonal reduction.
文摘Autonomous underwater vehicles(AUVs)have various applications in both military and civilian fields.A wider operation area and more complex tasks require better overall range performance of AUVs.However,until recently,there have been few unified criteria for evaluating the range performance of AUVs.In the present work,a unified range index,i.e.,L^(*),considering the cruising speed,the sailing distance,and the volume of an AUV,is proposed for the first time,which can overcome the shortcomings of previous criteria using merely one single parameter,and provide a uniform criterion for the overall range performance of various AUVs.After constructing the expression of the L^(*)index,the relevant data of 49 AUVs from 12 countries worldwide have been collected,and the characteristics of the L^(*)range index in different countries and different categories were compared and discussed.Furthermore,by analyzing the complex factors affecting the range index,methods to enhance the L^(*)range index value,such as efficiency enhancement and drag reduction,have been introduced and discussed.Under this condition,the work proposes a unified and scientific criterion for evaluating the range performance of AUVs for the first time,provides valuable theoretical insight for the development of AUVs with higher performance,and then arouses more attention to the application of the cutting-edge superlubricity technology to the field of underwater vehicles,which might greatly help to accelerate the coming of the era of the superlubricitive engineering.
基金supported by the National Natural Science Foundation of China (62271255,61871218)the Fundamental Research Funds for the Central University (3082019NC2019002)+1 种基金the Aeronautical Science Foundation (ASFC-201920007002)the Program of Remote Sensing Intelligent Monitoring and Emergency Services for Regional Security Elements。
文摘In order to extract the richer feature information of ship targets from sea clutter, and address the high dimensional data problem, a method termed as multi-scale fusion kernel sparse preserving projection(MSFKSPP) based on the maximum margin criterion(MMC) is proposed for recognizing the class of ship targets utilizing the high-resolution range profile(HRRP). Multi-scale fusion is introduced to capture the local and detailed information in small-scale features, and the global and contour information in large-scale features, offering help to extract the edge information from sea clutter and further improving the target recognition accuracy. The proposed method can maximally preserve the multi-scale fusion sparse of data and maximize the class separability in the reduced dimensionality by reproducing kernel Hilbert space. Experimental results on the measured radar data show that the proposed method can effectively extract the features of ship target from sea clutter, further reduce the feature dimensionality, and improve target recognition performance.
