There are many types of radar active deception false target jamming that are highly correlated with the real target.Recognizing the real and false targets under a low Signal-to-Noise Ratio(SNR)is difficult.To solve th...There are many types of radar active deception false target jamming that are highly correlated with the real target.Recognizing the real and false targets under a low Signal-to-Noise Ratio(SNR)is difficult.To solve the above problem,this article proposes a real/false target recognition method based on the features of multi-pulse joint frequency response by analyzing the differences in the scattering characteristics and modeling real target echoes as a synthesis of multi-scattering center echoes.Firstly,in the range-doppler domain,the real and false targets are truncated along the range dimension,and a fast Fourier transform is performed to extract the features of multi-pulse joint frequency response.Then,a two-channel feature fusion network is designed for real and false target recognition.Finally,a Multi-Coherent Processing Interval Joint Decision Method(M-CPIJDM)based on temporal information is proposed to improve the recognition performance.Experiments using the measured data show that the proposed method can well recognize real and false target signals under four jamming backgrounds:distance false target,velocity false target,distance-velocity composite false target,and forwarding dense false target.展开更多
Ubiquitous radar is a new radar system that provides continuous and uninterrupted multifunction capability within a coverage volume. Continuous coverage from close-in ＂pop-up＂ targets in clutter to long-range target...Ubiquitous radar is a new radar system that provides continuous and uninterrupted multifunction capability within a coverage volume. Continuous coverage from close-in ＂pop-up＂ targets in clutter to long-range targets impacts selection of waveform parameters. The coherent processing interval （CPI） must be long enough to achieve a certain signal-to-noise ratio （SNR） that ensures the efficiency of detection. The condition of detection in the case of low SNR is analyzed, and three different cases that would occur during integration are discussed and a method to determine the CPI is presented. The simulation results show that targets detection with SNR as low as -26 dB in the experimental system can possibly determine the CPI.展开更多
The authors recently developed a kind of non-probabilistic analysis method, named as‘non-random vibration analysis’, to deal with the important random vibration problems, in which the excitation and response are bot...The authors recently developed a kind of non-probabilistic analysis method, named as‘non-random vibration analysis’, to deal with the important random vibration problems, in which the excitation and response are both given in the form of interval process rather than stochastic process. Since it has some attractive advantages such as easy to understand, convenient to use and small dependence on samples, the non-random vibration analysis method is expected to be an effective supplement of the traditional random vibration theory. In this paper, we further extend the nonrandom vibration analysis into the general viscous damping system, and formulate a method to calculate the dynamic response bounds of a viscous damping vibration system under uncertain excitations. Firstly, the unit impulse response matrix of the system is obtained by using a complex mode superposition method. Secondly, an analytic formulation of the system dynamic response middle point and radius under uncertain excitations is derived based on the Duhamel’s integral, and thus the upper and lower response bounds of the system can be obtained. Finally, two numerical examples are investigated to demonstrate the effectiveness of the proposed method.展开更多
In recent years, the authors have extended the traditional interval method into the time dimension to develop a new mathematical tool called the “interval process model” for quantifying time-varying or dynamic uncer...In recent years, the authors have extended the traditional interval method into the time dimension to develop a new mathematical tool called the “interval process model” for quantifying time-varying or dynamic uncertainties. This model employs upper and lower bounds instead of precise probability distributions to quantify uncertainty in a parameter at any given time point. It is anticipated to complement the conventional stochastic process model in the coming years owing to its relatively low dependence on experimental samples and ease of understanding for engineers. Building on our previous work, this paper proposes a spectrum analysis method to describe the frequency domain characteristics of an interval process, further strengthening the theoretical foundation of the interval process model and enhancing its applicability for complex engineering problems. In this approach, we first define the zero midpoint function interval process and its auto/cross-power spectral density(PSD) functions. We also deduce the relationship between the auto-PSD function and the auto-covariance function of the stationary zero midpoint function interval process. Next, the auto/cross-PSD function matrices of a general interval process are defined, followed by the introduction of the concepts of PSD function matrix and cross-PSD function matrix for interval process vectors. The spectrum analysis method is then applied to random vibration problems, leading to the creation of a spectrum-analysis-based interval vibration analysis method that determines the PSD function for the system displacement response under stationary interval process excitations. Finally, the effectiveness of the formulated spectrum-analysis-based interval vibration analysis approach is verified through two numerical examples.展开更多
Traditional econometrics has long employed ＂points＂ to measure time series data. In real life situations, however, it suffers the loss of volatility information, since many variables are bounded by intervals in a gi...Traditional econometrics has long employed ＂points＂ to measure time series data. In real life situations, however, it suffers the loss of volatility information, since many variables are bounded by intervals in a given period. To address this issue, this paper provides a new methodology for interval time series analysis. The concept of ＂interval stochastic process＂ is formally defined as a counterpart of ＂stochastic process＂ in point-based econometrics. The authors introduce the concepts of interval stationarity, interval statistics （including interval mean, interval variance, etc.） and propose an interval linear model to investigate the dynamic relationships between interval processes. A new interval-based optimization approach for estimation is proposed, and corresponding evaluation criteria are derived. To demonstrate that the new interval method provides valid results, an empirical example on the sterling-dollar exchange rate is presented.展开更多
基金supported by the Basic Research Program,China(No.514010503-208)the China Aerospace Science and Technology Corporation Stabilization Support Project(No.ZY0110020009)the Equipment Pre-research Project,China(No.304060201).
文摘There are many types of radar active deception false target jamming that are highly correlated with the real target.Recognizing the real and false targets under a low Signal-to-Noise Ratio(SNR)is difficult.To solve the above problem,this article proposes a real/false target recognition method based on the features of multi-pulse joint frequency response by analyzing the differences in the scattering characteristics and modeling real target echoes as a synthesis of multi-scattering center echoes.Firstly,in the range-doppler domain,the real and false targets are truncated along the range dimension,and a fast Fourier transform is performed to extract the features of multi-pulse joint frequency response.Then,a two-channel feature fusion network is designed for real and false target recognition.Finally,a Multi-Coherent Processing Interval Joint Decision Method(M-CPIJDM)based on temporal information is proposed to improve the recognition performance.Experiments using the measured data show that the proposed method can well recognize real and false target signals under four jamming backgrounds:distance false target,velocity false target,distance-velocity composite false target,and forwarding dense false target.
文摘Ubiquitous radar is a new radar system that provides continuous and uninterrupted multifunction capability within a coverage volume. Continuous coverage from close-in ＂pop-up＂ targets in clutter to long-range targets impacts selection of waveform parameters. The coherent processing interval （CPI） must be long enough to achieve a certain signal-to-noise ratio （SNR） that ensures the efficiency of detection. The condition of detection in the case of low SNR is analyzed, and three different cases that would occur during integration are discussed and a method to determine the CPI is presented. The simulation results show that targets detection with SNR as low as -26 dB in the experimental system can possibly determine the CPI.
基金supported by the Science Challenge Project of China (No. TZ2018007)the National Science Fund for Distinguished Young Scholars (No. 51725502)+1 种基金the National Key R&D Program of China (No. 2016YFD0701105)the Open Project Program of Key Laboratory for Precision & Non-traditional Machining of Ministry of Education, Dalian University of Technology of China (No. JMTZ201701)
文摘The authors recently developed a kind of non-probabilistic analysis method, named as‘non-random vibration analysis’, to deal with the important random vibration problems, in which the excitation and response are both given in the form of interval process rather than stochastic process. Since it has some attractive advantages such as easy to understand, convenient to use and small dependence on samples, the non-random vibration analysis method is expected to be an effective supplement of the traditional random vibration theory. In this paper, we further extend the nonrandom vibration analysis into the general viscous damping system, and formulate a method to calculate the dynamic response bounds of a viscous damping vibration system under uncertain excitations. Firstly, the unit impulse response matrix of the system is obtained by using a complex mode superposition method. Secondly, an analytic formulation of the system dynamic response middle point and radius under uncertain excitations is derived based on the Duhamel’s integral, and thus the upper and lower response bounds of the system can be obtained. Finally, two numerical examples are investigated to demonstrate the effectiveness of the proposed method.
基金supported by the National Natural Science Foundation of China (Grant No. 52105253)the State Key Program of National Science Foundation of China (Grant No.52235005)。
文摘In recent years, the authors have extended the traditional interval method into the time dimension to develop a new mathematical tool called the “interval process model” for quantifying time-varying or dynamic uncertainties. This model employs upper and lower bounds instead of precise probability distributions to quantify uncertainty in a parameter at any given time point. It is anticipated to complement the conventional stochastic process model in the coming years owing to its relatively low dependence on experimental samples and ease of understanding for engineers. Building on our previous work, this paper proposes a spectrum analysis method to describe the frequency domain characteristics of an interval process, further strengthening the theoretical foundation of the interval process model and enhancing its applicability for complex engineering problems. In this approach, we first define the zero midpoint function interval process and its auto/cross-power spectral density(PSD) functions. We also deduce the relationship between the auto-PSD function and the auto-covariance function of the stationary zero midpoint function interval process. Next, the auto/cross-PSD function matrices of a general interval process are defined, followed by the introduction of the concepts of PSD function matrix and cross-PSD function matrix for interval process vectors. The spectrum analysis method is then applied to random vibration problems, leading to the creation of a spectrum-analysis-based interval vibration analysis method that determines the PSD function for the system displacement response under stationary interval process excitations. Finally, the effectiveness of the formulated spectrum-analysis-based interval vibration analysis approach is verified through two numerical examples.
基金This work was partially supported by the National Natural Science Foundation of China and Research Granting Committee of Hong Kong
文摘Traditional econometrics has long employed ＂points＂ to measure time series data. In real life situations, however, it suffers the loss of volatility information, since many variables are bounded by intervals in a given period. To address this issue, this paper provides a new methodology for interval time series analysis. The concept of ＂interval stochastic process＂ is formally defined as a counterpart of ＂stochastic process＂ in point-based econometrics. The authors introduce the concepts of interval stationarity, interval statistics （including interval mean, interval variance, etc.） and propose an interval linear model to investigate the dynamic relationships between interval processes. A new interval-based optimization approach for estimation is proposed, and corresponding evaluation criteria are derived. To demonstrate that the new interval method provides valid results, an empirical example on the sterling-dollar exchange rate is presented.
