An effective method of multiple input multiple output （MIMO） radar weak target detection is proposed based on the Hough transform. The detection time duration is divided into multiple coherent processing intervals ...An effective method of multiple input multiple output （MIMO） radar weak target detection is proposed based on the Hough transform. The detection time duration is divided into multiple coherent processing intervals （CPIs）. Within each CPI, conventional methods such as fast Fourier transform （FFT） is exploit to coherent inte- grating in same range cell. Furthermore, noncoherent integration through several range cells can be implemented by Hough transform among all CPIs. Thus, higher integration gain can be obtained. Simulation results are also given to demonstrate that the detection performance of weak moving target can be dramatically improved.展开更多
This paper focuses on the sea-surface weak target detection based on memory-fully convolutional network(M-FCN)in strong sea clutter.Firstly,the constant false alarm rate(CFAR)detection method utilizes a low threshold ...This paper focuses on the sea-surface weak target detection based on memory-fully convolutional network(M-FCN)in strong sea clutter.Firstly,the constant false alarm rate(CFAR)detection method utilizes a low threshold with high probability of false alarm to detect sea-surface weak targets after non-coherent integration.Reducing the detection threshold can generate a large number of false alarms while increasing the detection rate,and how to suppress a large number of false alarms is the key to improve the performance of weak target detection.Then,the detection result of the low threshold is operated to construct the target matrix suitable for the size of fully convolutional networks and the convolution operator form.Finally,the M-FCN architecture is designed to learn the different accumulation characteristics of the target and the sea clutter between different frames.For improving the detection performance,the historical multi-frame information is memorized by the network,and the end-to-end structure is established to detect sea-surface weak target automatically.Experimental results on measured data demonstrate that the M-FCN method outperforms the traditional track before detection(TBD)method and reduces false alarm tracks by 35.1%,which greatly improves the track quality.展开更多
The target on the sea surface is complex and difficult to detect due to the interference of backscattered returns from the sea surface illuminated by the radar pulse. Detrended fluctuation analysis （DFA） has been us...The target on the sea surface is complex and difficult to detect due to the interference of backscattered returns from the sea surface illuminated by the radar pulse. Detrended fluctuation analysis （DFA） has been used successfully to extract the time-domain Hurst exponent of sea-clutter series. Since the frequency of the sea clutter mainly concentrates around Doppler center so that we consider to extract frequency-do- main fractal characterization and then detect a weak target within sea clutter by using the difference of frequency-domain fractal characterization. The generalized detrended fluctuation analysis （GDFA） is more flexible than traditional DFA owing to its smoothing action for the clutters. In this paper, we apply the GDFA to evaluate the generalized Hurst exponent of sea-clutter series in the frequency domain. The difference of generalized Hurst exponents between different sea-clutter range bins would be used to determine whether the target exists. Moreover, some simulations with the real IPIX radar data have also been demonstrated in order to suooort this conclusion.展开更多
Traditional multiframe Track-Before-Detect(TBD)may incur adverse integration loss resulting from model mismatch in sensor coordinates.Its suboptimal integration strategy may cause target envelope degradation.To addres...Traditional multiframe Track-Before-Detect(TBD)may incur adverse integration loss resulting from model mismatch in sensor coordinates.Its suboptimal integration strategy may cause target envelope degradation.To address these issues,a pseudo-spectrum-based multiframe TBD in mixed coordinates is proposed firstly.The data search for energy integration is conducted based on an accurate model in the x-y plane while target energy is integrated based on pseudo-spectrum in sensor coordinates.The algorithm performance is improved since the model mismatch is eliminated,and the pseudo-spectrum based integration facilitates well maintained target envelope.The detailed multiframe integration procedure and theoretical target integrated envelope are derived.Secondly,to cope with the unknown target velocity,a velocity filter bank based on pseudo-spectrum in mixed coordinates is proposed.The effect of velocity mismatch on algorithm performance is analyzed and an efficient method for filter bank design is presented.Thirdly,a parameter estimation method using characteristics of integrated envelope is presented for improved target polar position and Cartesian velocity estimation.Finally,numerical results are provided to demonstrate the effectiveness of the proposed method.展开更多
The steered covariance matrix(STCM) and its inverse matrix should be calculated in each beam for steered minimum variance(STMV) . The inverse matrix needs complex computation and restricts its application in engineeri...The steered covariance matrix(STCM) and its inverse matrix should be calculated in each beam for steered minimum variance(STMV) . The inverse matrix needs complex computation and restricts its application in engineering. Combining the integration character of one-phase regressive filter with the iterative formula of inverse matrix,an STMV iterative algorithm is proposed. The computational cost of the iterative algorithm is reduced approximately to be 2/M times of the original one when there are M sensors,and is more advantaged for the realization of the algorithm in real time. Simulation results show that the STMV iterative algorithm can preserve the characters of STMV on high azimuth resolution and weak target detection while the computational cost reduced sharply. The analysis on sea trial data proves that the proposed algorithm can estimate each target's azimuth even when the source powers differ in large scales or their bearings are very approximate.展开更多
A novel approach, which can handle ambiguous data from weak targets, is proposed within the randomized Hough transform track-before-detect(RHT-TBD) framework. The main idea is that, without the pre-detection and ambig...A novel approach, which can handle ambiguous data from weak targets, is proposed within the randomized Hough transform track-before-detect(RHT-TBD) framework. The main idea is that, without the pre-detection and ambiguity resolution step at each time step, the ambiguous measurements are mapped by the multiple hypothesis ranging(MHR) procedure. In this way, all the information, based on the relativity in time and pulse repetition frequency(PRF) domains, can be gathered among different PRFs and integrated over time via a batch procedure. The final step is to perform the RHT with all the extended measurements, and the ambiguous data is unfolded while the detection decision is confirmed at the end of the processing chain.Unlike classic methods, the new approach resolves the problem of range ambiguity and detects the true track for targets. Finally, its application is illustrated to analyze and compare the performance between the proposed approach and the existing approach. Simulation results exhibit the effectiveness of this approach.展开更多
A small and weak target detection method is proposed in this work that outperforms all other methods in terms of real-time capability.It is the first time that two-dimensional(2D)images are processed using only one-di...A small and weak target detection method is proposed in this work that outperforms all other methods in terms of real-time capability.It is the first time that two-dimensional(2D)images are processed using only one-dimensional1D structuring elements in a morphology-based approach,enabling the real-time hardware implementation of the whole image processing method.A parallel image readout and processing structure is introduced to achieve an ultra-low latency time on the order of nanoseconds,and a hyper-frame resolution in the time domain can be achieved by combining the row-by-row structure and the electrical rolling shutter technique.Experimental results suggest that the expected target can be successfully detected under various interferences with an accuracy of 0.1 pixels(1σ)under the worst sky night test condition and that a centroiding precision of better than 0.03 pixels(1σ)can be reached for static tests.The real-time detection method with high robustness and accuracy is attractive for application to all types of real-time small target detection systems,such as medical imaging,infrared surveillance,and target measurement and tracking,where an ultra-high processing speed is required.展开更多
In radar automatic target recognition(RATR),the high-resolution range profile(HRRP)has garnered considerable attention owing to its minimal computational demands.However,radar HRRP target recognition still faces numer...In radar automatic target recognition(RATR),the high-resolution range profile(HRRP)has garnered considerable attention owing to its minimal computational demands.However,radar HRRP target recognition still faces numerous challenges,primarily due to substantial variations in the amplitude and distribution of HRRP scattering points because of slight azimuthal changes.To alleviate the effect of aspect sensitivity,a novel multi-frame attention network(MFA-Net)comprising a range deformable convolution module(RDCM),multi-frame attention module(MFAM),and global-local Transformer module(GLTM)is proposed.The RDCM is designed to adaptively learn the distance of scattering center migration.Subsequently,the MFAM extracts consistent features across different frames to alleviate the influence of power fluctuation.Finally,the GLTM allocates attention between global and local fea-tures.The feasibility and effectiveness of the proposed method are validated through simulation and experimental datasets,and the recognition rate is enhanced by more than 3%compared to the state-of-the-art methods.展开更多
This paper addresses weak target detection problem for bistatic radar via a pseudo-spectrum(PS)based track-before-detect(TBD).Generally,PS-TBD estimates target position and velocity by means of pseudo-spectrum constru...This paper addresses weak target detection problem for bistatic radar via a pseudo-spectrum(PS)based track-before-detect(TBD).Generally,PS-TBD estimates target position and velocity by means of pseudo-spectrum construction in the discrete measurement space and accurate energy accumulation in mixed coordinates.However,the grids within the polar sensing region of the receivers in the bistatic radar are not aligned.Traditional PS-TBD can not directly process these measurements.In this paper,a PS-TBD method for bistatic radar is proposed to overcome this problem.Each cell in the measurement space of the receivers is mapped to the aligned Cartesian coordinates and predicted to the integration frame according to the assumed filter velocity.A PS is formulated centered on the predicted Cartesian position.Then the samples of the pseudo-spectra are accumulated to the nearest cell around the predicted Cartesian position.The procedure of the energy integration is derived in detail.Simulation results validate the efficacy of the proposed method in terms of detection accuracy and parameter estimation.展开更多
针对天基短波红外图像中弱小目标易被云层、地表杂波淹没,且在低信杂比条件下检测困难的问题,提出一种融合安德森加速的自正则化加权稀疏模型(Self-Regularized Weighted Sparse,SRWS)与相对局部对比度(Relative Local Contrast Measure...针对天基短波红外图像中弱小目标易被云层、地表杂波淹没,且在低信杂比条件下检测困难的问题,提出一种融合安德森加速的自正则化加权稀疏模型(Self-Regularized Weighted Sparse,SRWS)与相对局部对比度(Relative Local Contrast Measure,RLCM)的改进检测方法。通过引入安德森加速机制,显著降低了背景估计的计算复杂度,利用背景残差图和RLCM实现了多尺度目标检测性。实验结果表明,本文算法在复杂背景下仍保持优异性能,接收者操作曲线下面积(Area Under Curve,AUC)最高达0.950,最低不低于0.842;信杂比增益(Signal-to-Clutter Ratio Gain,SCRG)显著优于红外图像块(Infrared Patch Image,IPI)、局部对比度法(Local Contrast Measure,LCM)等传统方法。本研究有效提升了天基短波红外弱小目标的检测精度与稳定性,为复杂背景下的遥感目标检测提供了可靠的解决方案。展开更多
文摘An effective method of multiple input multiple output （MIMO） radar weak target detection is proposed based on the Hough transform. The detection time duration is divided into multiple coherent processing intervals （CPIs）. Within each CPI, conventional methods such as fast Fourier transform （FFT） is exploit to coherent inte- grating in same range cell. Furthermore, noncoherent integration through several range cells can be implemented by Hough transform among all CPIs. Thus, higher integration gain can be obtained. Simulation results are also given to demonstrate that the detection performance of weak moving target can be dramatically improved.
基金This was work supported by the National Natural Science Foundation of China(U19B2031).
文摘This paper focuses on the sea-surface weak target detection based on memory-fully convolutional network(M-FCN)in strong sea clutter.Firstly,the constant false alarm rate(CFAR)detection method utilizes a low threshold with high probability of false alarm to detect sea-surface weak targets after non-coherent integration.Reducing the detection threshold can generate a large number of false alarms while increasing the detection rate,and how to suppress a large number of false alarms is the key to improve the performance of weak target detection.Then,the detection result of the low threshold is operated to construct the target matrix suitable for the size of fully convolutional networks and the convolution operator form.Finally,the M-FCN architecture is designed to learn the different accumulation characteristics of the target and the sea clutter between different frames.For improving the detection performance,the historical multi-frame information is memorized by the network,and the end-to-end structure is established to detect sea-surface weak target automatically.Experimental results on measured data demonstrate that the M-FCN method outperforms the traditional track before detection(TBD)method and reduces false alarm tracks by 35.1%,which greatly improves the track quality.
基金The National Natural Science Foundation of China Project under contract Nos 41276187 and 41076119the Scientific Research Foundation for Introducing Talents,Nanjing University of Information Science and Technology under contract No.20110310Jiangsu Natural Science Foundation under contract No.BK2011008
文摘The target on the sea surface is complex and difficult to detect due to the interference of backscattered returns from the sea surface illuminated by the radar pulse. Detrended fluctuation analysis （DFA） has been used successfully to extract the time-domain Hurst exponent of sea-clutter series. Since the frequency of the sea clutter mainly concentrates around Doppler center so that we consider to extract frequency-do- main fractal characterization and then detect a weak target within sea clutter by using the difference of frequency-domain fractal characterization. The generalized detrended fluctuation analysis （GDFA） is more flexible than traditional DFA owing to its smoothing action for the clutters. In this paper, we apply the GDFA to evaluate the generalized Hurst exponent of sea-clutter series in the frequency domain. The difference of generalized Hurst exponents between different sea-clutter range bins would be used to determine whether the target exists. Moreover, some simulations with the real IPIX radar data have also been demonstrated in order to suooort this conclusion.
基金supported by the National Natural Science Foundation of China(No.61671181)。
文摘Traditional multiframe Track-Before-Detect(TBD)may incur adverse integration loss resulting from model mismatch in sensor coordinates.Its suboptimal integration strategy may cause target envelope degradation.To address these issues,a pseudo-spectrum-based multiframe TBD in mixed coordinates is proposed firstly.The data search for energy integration is conducted based on an accurate model in the x-y plane while target energy is integrated based on pseudo-spectrum in sensor coordinates.The algorithm performance is improved since the model mismatch is eliminated,and the pseudo-spectrum based integration facilitates well maintained target envelope.The detailed multiframe integration procedure and theoretical target integrated envelope are derived.Secondly,to cope with the unknown target velocity,a velocity filter bank based on pseudo-spectrum in mixed coordinates is proposed.The effect of velocity mismatch on algorithm performance is analyzed and an efficient method for filter bank design is presented.Thirdly,a parameter estimation method using characteristics of integrated envelope is presented for improved target polar position and Cartesian velocity estimation.Finally,numerical results are provided to demonstrate the effectiveness of the proposed method.
文摘The steered covariance matrix(STCM) and its inverse matrix should be calculated in each beam for steered minimum variance(STMV) . The inverse matrix needs complex computation and restricts its application in engineering. Combining the integration character of one-phase regressive filter with the iterative formula of inverse matrix,an STMV iterative algorithm is proposed. The computational cost of the iterative algorithm is reduced approximately to be 2/M times of the original one when there are M sensors,and is more advantaged for the realization of the algorithm in real time. Simulation results show that the STMV iterative algorithm can preserve the characters of STMV on high azimuth resolution and weak target detection while the computational cost reduced sharply. The analysis on sea trial data proves that the proposed algorithm can estimate each target's azimuth even when the source powers differ in large scales or their bearings are very approximate.
基金supported by National Natural Science Foundation of China (Grant Nos. 61179018, 61372027, 61501489)Special Foundation for Mountain Tai Scholars
文摘A novel approach, which can handle ambiguous data from weak targets, is proposed within the randomized Hough transform track-before-detect(RHT-TBD) framework. The main idea is that, without the pre-detection and ambiguity resolution step at each time step, the ambiguous measurements are mapped by the multiple hypothesis ranging(MHR) procedure. In this way, all the information, based on the relativity in time and pulse repetition frequency(PRF) domains, can be gathered among different PRFs and integrated over time via a batch procedure. The final step is to perform the RHT with all the extended measurements, and the ambiguous data is unfolded while the detection decision is confirmed at the end of the processing chain.Unlike classic methods, the new approach resolves the problem of range ambiguity and detects the true track for targets. Finally, its application is illustrated to analyze and compare the performance between the proposed approach and the existing approach. Simulation results exhibit the effectiveness of this approach.
基金support by the China NSF projects(Nos.61505094,61377012 and 51522505).
文摘A small and weak target detection method is proposed in this work that outperforms all other methods in terms of real-time capability.It is the first time that two-dimensional(2D)images are processed using only one-dimensional1D structuring elements in a morphology-based approach,enabling the real-time hardware implementation of the whole image processing method.A parallel image readout and processing structure is introduced to achieve an ultra-low latency time on the order of nanoseconds,and a hyper-frame resolution in the time domain can be achieved by combining the row-by-row structure and the electrical rolling shutter technique.Experimental results suggest that the expected target can be successfully detected under various interferences with an accuracy of 0.1 pixels(1σ)under the worst sky night test condition and that a centroiding precision of better than 0.03 pixels(1σ)can be reached for static tests.The real-time detection method with high robustness and accuracy is attractive for application to all types of real-time small target detection systems,such as medical imaging,infrared surveillance,and target measurement and tracking,where an ultra-high processing speed is required.
基金The National Natural Science Foundation of China(No.62388102)the Natural Science Foundation of Shandong Province(No.ZR2021MF134).
文摘In radar automatic target recognition(RATR),the high-resolution range profile(HRRP)has garnered considerable attention owing to its minimal computational demands.However,radar HRRP target recognition still faces numerous challenges,primarily due to substantial variations in the amplitude and distribution of HRRP scattering points because of slight azimuthal changes.To alleviate the effect of aspect sensitivity,a novel multi-frame attention network(MFA-Net)comprising a range deformable convolution module(RDCM),multi-frame attention module(MFAM),and global-local Transformer module(GLTM)is proposed.The RDCM is designed to adaptively learn the distance of scattering center migration.Subsequently,the MFAM extracts consistent features across different frames to alleviate the influence of power fluctuation.Finally,the GLTM allocates attention between global and local fea-tures.The feasibility and effectiveness of the proposed method are validated through simulation and experimental datasets,and the recognition rate is enhanced by more than 3%compared to the state-of-the-art methods.
基金supported by the National Natural Science Foundation of China(62371155)the Heilongjiang Outstanding Youth Science Fund(JQ2022F002)。
文摘This paper addresses weak target detection problem for bistatic radar via a pseudo-spectrum(PS)based track-before-detect(TBD).Generally,PS-TBD estimates target position and velocity by means of pseudo-spectrum construction in the discrete measurement space and accurate energy accumulation in mixed coordinates.However,the grids within the polar sensing region of the receivers in the bistatic radar are not aligned.Traditional PS-TBD can not directly process these measurements.In this paper,a PS-TBD method for bistatic radar is proposed to overcome this problem.Each cell in the measurement space of the receivers is mapped to the aligned Cartesian coordinates and predicted to the integration frame according to the assumed filter velocity.A PS is formulated centered on the predicted Cartesian position.Then the samples of the pseudo-spectra are accumulated to the nearest cell around the predicted Cartesian position.The procedure of the energy integration is derived in detail.Simulation results validate the efficacy of the proposed method in terms of detection accuracy and parameter estimation.
文摘针对天基短波红外图像中弱小目标易被云层、地表杂波淹没,且在低信杂比条件下检测困难的问题,提出一种融合安德森加速的自正则化加权稀疏模型(Self-Regularized Weighted Sparse,SRWS)与相对局部对比度(Relative Local Contrast Measure,RLCM)的改进检测方法。通过引入安德森加速机制,显著降低了背景估计的计算复杂度,利用背景残差图和RLCM实现了多尺度目标检测性。实验结果表明,本文算法在复杂背景下仍保持优异性能,接收者操作曲线下面积(Area Under Curve,AUC)最高达0.950,最低不低于0.842;信杂比增益(Signal-to-Clutter Ratio Gain,SCRG)显著优于红外图像块(Infrared Patch Image,IPI)、局部对比度法(Local Contrast Measure,LCM)等传统方法。本研究有效提升了天基短波红外弱小目标的检测精度与稳定性,为复杂背景下的遥感目标检测提供了可靠的解决方案。
