Signal Clutter Ratio Research Articles

Chaotic Behavior Analysis and Multifractal Feature Learning of Specular Reflection Ionosphere Clutter for Target Detection

Because ionosphere condition is dynamically varying in the Earth’s middle-latitude, the long-range detection performance of operating high frequency surface wave radar (HFSWR) systems is severely restricted by the presence of ionosphere clutter. The development and performance evaluation of target detection under the F-layer specular reflection ionosphere clutter with concentrated power (Fs-concentrated clutter) will be described in this paper. First, this article pays attention to the variable chaotic behavior of Fs-concentrated clutter by using amplitude and phase echo data. Then, this article investigates the multifractal property of the power spectrum between the Fs-concentrated clutter and mixed echo component. The various fractal characteristics of different types of echo component and Doppler scale are analyzed. Next, the singularity intensity correlation function width, the accumulation area of multifractal correlation spectrum and asymmetry parameter are extracted as the learning features. These multifractal feature parameters are fed into the improved AlexNet and Resnet networks for distinguishing the types of echo component. Experimental results using real HFSWR echoes datasets have demonstrated that this proposed method can increase the detection probability by about 20% than the state-of-art detection methods and classical target detection methods under various signal-clutter ratio (SCR) background.

A novel full-polarization SAR image ship detector based on scattering mechanisms and wave polarization anisotropy

Synthetic aperture radar (SAR) is suitable for earth observation due to its unique advantages. In this study, we constructed an adaptive ship detector using full-polarization SAR images. First, we thoroughly investigated the differences in scattering characteristics between ships, their background, and the wave polarization anisotropy. We then constructed a novel ship detector that incorporates scattering and anisotropy (known as joint scattering–anisotropy [joint-SA]). We found that joint-SA is an effective physical quantity representing the difference between the ship and its background; thus, joint-SA can be used for ship detection using full-polarization image data. Second, we used generalized gamma distribution to characterize joint-SA statistics of sea clutter with a large homogeneity range. Third, an adaptive constant false alarm rate (CFAR) method was implemented based on joint-SA. Finally, RADARSAT-2 and GF-3 data in the C-band and ALOS data in the L-band were used for verification. We tested five datasets, and the experimental results verified the correctness and superiority of the CFAR method based on joint-SA. The results show that the signal–clutter ratio of the proposed ship detector (33.17 dB, 35.98 dB, and 57.25 dB) was higher than that of DBSP (8.92 dB, 3.43 dB, and 25.40 dB) and RsDVH (17.28 dB, 11.17 dB, and 54.55 dB). Furthermore, the proposed detector has a higher detection accuracy and lower false alarm rate than those of the other two other methods.

Multifractal Correlation Analysis of Autoregressive Spectrum-Based Feature Learning for Target Detection Within Sea Clutter

Fractal theory has improved the target detection performance under sea clutter background. However, the traditional fractal methods in time domain or Fourier domain cannot accurately characterize the complex sea clutter properties, which leads to the degradation of the target detection performance under low signal-clutter-ratio (SCR) conditions. This article investigates the multifractal correlation property of sea clutter in the autoregressive (AR) spectrum domain for detection performance improvement. In this work, the traditional target detection problem is converted to a binary classification problem of sea clutter and targets. The refined fractal characteristics in various singularity scale interval and range bins are analyzed, and the AR singularity intensity correlation function width together with the accumulation area of AR multifractal correlation spectrum is extracted as intrinsic features. Then, a simple and efficient fully connected network is developed to realize the classification. Experimental results on real measured marine radar datasets demonstrate that the proposed method can increase the detection probability by about 15% than the state-of-art fractal-based methods under a low SCR condition.

Improved Relative Range Equation in Squint SAR for Moving Targets Refocusing

Moving target detection is difficult for synthetic aperture radar (SAR). As SAR is designed for imaging of stationary ground scene, the moving targets would be blurred and displaced in conventional SAR imaging. To increase the signal clutter ratio, the moving targets should be refocused while detecting. Based on relative range equation, one can refocus and detect the moving targets simultaneously by searching the relative velocity. This method has been derived and applied for side looking SAR. In this paper, we extend the relative range equation to squint mode. The procedures of the refocusing method are also illustrated. By introducing a parameter of relative squint angle, the imaging position of the moving target is derived. The refocusing method is validated by simulations. The moving target can be optimally refocused, and the refocused position can be parametrized by the relative motion parameters.

Infrared Small Target Detection Through Multiple Feature Analysis Based on Visual Saliency

Infrared small target detection in extreme environments such as low illumination or complex background with low signal clutter ratio is of crucial significance and counted as a difficult task in infrared search and tracking systems. In this paper, an effective infrared small target detection method is proposed based on the human visual system characteristics and multiple feature analysis. By using the contrast mechanism and visual attention mechanism, spatial gray level-based feature map and saliency extraction based feature map from frequency domain are obtained. Based on the characteristics of infrared dim target images and human visual attention mechanism, the target saliency features are revealed through the feature analysis in the spatial domain and frequency domain respectively. The saliency features from each feature map are applied to the final saliency map. By this means, the background clutter and noise are inhibited and the targets are distinct for the various scenes in the infrared images. The experimental results show that the proposed method has a robust and effective performance in terms of detection and false alarm rates. Comparing with the other methods in the experiments, the proposed method is feasible and adaptable in the various scenes of infrared images.

Adaptive Ship Detection in Hybrid-Polarimetric SAR Images Based on the Power–Entropy Decomposition

Based on its advantages, compact polarimetric (CP) synthetic aperture radar (SAR) is considered to be a good option for earth observations. This paper proposes an adaptive method of ship detection in CP SAR images operating in the hybrid-polarimetric (HP) mode. First, according to the analysis of scattering between ships and sea background, a novel decomposition approach, named power–entropy (PE) decomposition, is developed. Based on this approach, two components of the scattering power, the high-entropy scattering amplitude (HESA) and low-entropy scattering amplitude, are separated. We demonstrate that the HESA component is an effective physical quantity indicating the difference between the ship and its background and hence can potentially be used for ship detection using HP SAR data. The generalized Gamma distribution ( $\text{G}\Gamma \text{D}$ ) is found suitable for the characterization of HESA statistics of sea clutter with a wide range of homogeneity. As a result, the adaptive constant false-alarm rate (CFAR) technique based on the HESA detector is proposed. Experiments performed using HP measurements emulated from L-band ALOS-PALSAR and C-band RADARSAT-2 full polarimetric data validate the soundness and superiority of the proposed CFAR method based on the HESA detector. Both the theoretical proof and experimental results show that the HESA improves the ship–sea contrast (or the signal–clutter ratio) more than popular detectors, such as the entropy and SPAN. Moreover, the $\text{G}\Gamma \text{D}$ is a versatile model for the description of the statistical behavior of both the HESA and comparable detectors.

Using deep learning to detect small targets in infrared oversampling images

According to the oversampling imaging characteristics, an infrared small target detection method based on deep learning is proposed. A 7-layer deep convolutional neural network (CNN) is designed to automatically extract small target features and suppress clutters in an end-to-end manner. The input of CNN is an original oversampling image while the output is a clutter-suppressed feature map. The CNN contains only convolution and non-linear operations, and the resolution of the output feature map is the same as that of the input image. The L1-norm loss function is used, and a mass of training data is generated to train the network effectively. Results show that compared with several baseline methods, the proposed method improves the signal clutter ratio gain and background suppression factor by 3-4 orders of magnitude, and has more powerful target detection performance.

Small infrared target detection based on image patch ordering

Small infrared (IR) target detection is an important and challenging issue in IR target tracking system. In this paper, a patch ordering-based method is proposed for small IR target detection in an image with complicated background. Inspired by the contrast mechanism of human vision system, a patch ordering-based contrast measure (POCM) is designed to deal with the input image, which cannot only suppress the background noise and clutter but also enhance the targets significantly. In this way, POCM can increase the contrast ratio between target and background. This leads to higher Signal Clutter Ratio (SCR). Then the true target can be detected by applying simple adaptive thresholding method. The experimental results on two sequences show that the proposed method can efficiently detect small IR target from heavy clutter.

A robust infrared dim target detection method based on template filtering and saliency extraction

Dim target detection in infrared image with complex background and low signal-clutter ratio (SCR) is a significant and difficult task in the infrared target tracking system. A robust infrared dim target detection method based on template filtering and saliency extraction is proposed in this paper. The weighted gray map is obtained from the infrared image to highlight the target which is brighter than its neighbors and has weak correlation with its background. The target saliency map is then calculated by phase spectrum of Fourier Transform, so that the dim target detection could be converted to salient region extraction. The potential targets are finally extracted by combining the two maps. Moreover, position discrimination between targets in the two maps is used to exclude the false alarms and extract the targets. Experimental results on measured images indicate that our method is feasible, adaptable and robust in different backgrounds. The ROC (Receiver Operating Characteristic) curves obtained from the simulated images demonstrate the proposed method outperforms some existing typical methods in both detection rate and false alarm rate, for target detection with low SCR.

Weak target detection in sea clutter background using local‐multifractal spectrum with adaptive window length

Weak target detection based on fractal analysis is a hot research topic. The existing methods, using single fractal dimension and Hurst exponent, are not applicable under low signal clutter ratio (SCR) conditions. This study focuses on enhancing the performance of weak target detection by utilising the multifractal properties of sea clutter. The existing global multifractal spectrum based method has limited performance because of its non-stability. Therefore this study proposes a weak target detection method by taking into account the local difference of multifractal spectrum between sea clutter with and without target. The local-multifractal spectrum is obtained by adding a rectangle window to the multifractal spectrum, and the local difference is calculated by the local mean square summation algorithm. In addition, the window length, a key parameter for obtaining the local-multifractal spectrum, is adaptively computed by the slope of the singularity intensity. In comparison with the existing multifractal spectrum based method, the experimental results of real S-band and X-band sea clutter data show that the proposed method is more stable, and could achieve a better detection performance under low SCR conditions.

Principal curvature for infrared small target detection

Small target detection in infrared image with complex background and low signal–noise ratio is an important and difficult task in the infrared target tracking system. In this paper, a principal curvature-based method is proposed. The principal curvatures of target pixels are negative and their absolute values are larger than that of background pixels and noise pixels in a Gaussian-blurred infrared image. The proposed filter takes a composite function of the curvatures for detection. An approximate model is also built for optimizing the parameters. Experimental results show that the proposed algorithm is effective and adaptable for infrared small target detection in complex background. Compared with several popular methods, the proposed algorithm demonstrates significant improvement on detection performance in terms of the parameters of signal clutter ratio gain, background suppression factor and ROC.

Small Floating Target Detection Algorithm Based on Time-Frequency Similarity

The features of radar echo of small floating target within sea clutter exists not only in target bins, but also exist in extend range bins. These lead to certain characteristic are similar between targets and extend bins. Short-time average power and mean-square Doppler bandwidth are extracted as two of these features and their similarities are been proved existing between target bin and extend bins by IPIX data. Although clutter bins may also have this similarity, it could be distinguished when observation time is enhanced. According to this characteristic, a detection algorithm is proposed based on time-frequency similarity analysis, the method works well when the average signal clutter ratio is about 5dB and the observation time is 300ms.

A novel target detection approach based on adaptive radar waveform design

To resolve problems of complicated clutter, fast-varying scenes, and low signal-clutter-ratio (SCR) in application of target detection on sea for space-based radar (SBR), a target detection approach based on adaptive waveform design is proposed in this paper. Firstly, complicated sea clutter is modeled as compound Gaussian process, and a target is modeled as some scatterers with Gaussian reflectivity. Secondly, every dwell duration of radar is divided into several sub-dwells. Regular linear frequency modulated pulses are transmitted at Sub-dwell 1, and the received signal at this sub-dwell is used to estimate clutter covariance matrices and pre-detection. Estimated matrices are updated at every following sub-dwell by multiple particle filtering to cope with fast-varying clutter scenes of SBR. Furthermore, waveform of every following sub-dwell is designed adaptively according to mean square optimization technique. Finally, principal component analysis and generalized likelihood ratio test is used for mitigation of colored interference and property of constant false alarm rate, respectively. Simulation results show that, considering configuration of SBR and condition of complicated clutter, 9 dB is reduced for SCR which reliable detection requires by this target detection approach. Therefore, the work in this paper can markedly improve radar detection performance for weak targets.

Application of PCA-CFAR-MNF to Image Enhancement for a UGV FLGPR

The UGV (Unmanned Ground Vehicle) FLGPR (Forward-Looking Ground Penetrating Radar) imaging model spatially variable characteristics lead to the relatively low image SCR (Signal-Clutter Ratio). In order to improve the image quality and improve the SCR, the coherence properties of the target in image sequences is used for image enhancement. That is, images from the same region at different time sequence are enhanced two times. First of all, CFAR (Constant False Alarm Rate) is a preliminary step to enhance the image sequence, and then PCA-CFAR-MNF enhancement algorithm is proposed: according to the iterative nature of the MNF (Maximum Noise Fraction), PCA (Principal Component Analysis) method is first used to transform selecting and then CFAR detection is used to estimate the image background in the iteration,. The measured data show that the proposed method not only keeping the target characteristics, but also have remarkable image enhancement effect, and have greatly improved SCR.

Hand-Held GPR Imaging Using Migration for Irregular Data

Handheld ground-penetrating radar (GPR) system is one of a number of technologies that has been researched as a means of improving landmine detection efficiency. But it is difficult to display landmine imaging in horizontal slices, because human being operator cannot collect regular GPR data along the survey line and the measurement positions are random. Also detection of buried landmines by GPR normally suffers from very strong clutter that will decrease the image quality. So we proposed two processing procedures to achieve hand-held GPR subsurface imaging. One processing procedure is composed of interpolation and migration algorithm. The interpolation is chosen to produce the regular grid data set. The migration is used to improve signal-clutter ratio and reconstruct subsurface imaging. In the other processing procedure we developed a simply modified migration algorithm that can directly processes irregular GPR data. Two processing procedures, migration with interpolation and modified migration without interpolation, were applied to a field experiment data respectively. Both of processing procedures can efficiently process irregular GPR data and achieve subsurface imaging of high quality. The imaging processed by migration with interpolation is used in the dual sensor, advanced landmine imaging system (ALIS), and two sets of ALIS have detected more than 80 AP-mines since 2009. Also the modified migration algorithm can save the processing time, which is important for field real-time signal processing. We concluded that to achieve clear hand-held GPR imaging, the interpolation is not necessary algorithm and the migration is the key algorithm.

Direct data domain STAP using sparse representation of clutter spectrum

In the field of space–time adaptive processing (STAP), direct data domain (D3) methods avoid non-stationary training data and can effectively suppress the clutter within the test cell. However, this benefit comes at the cost of a reduced system degree of freedom (DOF), which results in performance loss. In this paper, by exploiting the intrinsic sparsity of the spectral distribution, a new direct data domain approach using sparse representation (D3SR) is proposed, which seeks to estimate the high-resolution space–time spectrum only with the test cell. The simulation of both side-looking and non-side-looking cases has illustrated the effectiveness of the D3SR spectrum estimation using focal underdetermined system solution (FOCUSS) and L 1 norm minimization. Then the clutter covariance matrix (CCM) and the corresponding adaptive filter can be effectively obtained. D3SR maintains the full system DOF so that it can achieve better performance of output signal-clutter-ratio (SCR) and minimum detectable velocity (MDV) than current D3 methods, e.g., direct data domain least squares (D3LS). Therefore D3SR can deal with the non-stationary clutter scenario more effectively, where both the discrete interference and range-dependent clutter exists.

Registration-based compensation using sparse representation in conformal-array STAP

Space–time adaptive processing (STAP) is a well-known technique in detecting slow-moving targets in the clutter-spreading environment. When considering the STAP system with conformal radar array (CFA), the training data are range-dependent, which results in poor detection performance of traditional statistical-based algorithms. Current registration-based compensation (RBC) is implemented based on a sub-snapshot spectrum using temporal smoothing. In this case, the estimation accuracy of the configuration parameters and the clutter power distribution is limited. In this paper, the technique of sparse representation is introduced into the spectral estimation, and a new compensation method is proposed, namely RBC with sparse representation (SR-RBC). This method first establishes the relationship between the clutter covariance matrix (CCM) and the clutter spectral distribution. Based on this, it avoids the problem of lacking stationary training data and converts the CCM estimation into the solving of the underdetermined equation only with the test cell. Then sparse representation method, like iterative reweighted least square (IRLS) is used to guide the solution of the underdetermined equation towards the actual clutter distribution. Finally, the transform matrix is designed using the CCM estimation so that the processed training data behaves nearly stationary with the test cell. Because the configuration parameters and the clutter spectral response are obtained with full-snapshot using sparse representation, SR-RBC provides more accurate clutter spectral estimation, and the transformed training data are more stationary so that better signal-clutter-ratio ( SCR) improvement is achieved.

Ray Tracing of CMP Antenna Array GPR System

There is much interest in vehicle mounted array GPR systems for landmine detec- tion and work is being carried out on various national, because high speed array GPR systems can sweep a large area in a relatively short time to improve performance and e-ciency. We devel- oped the vehicle mounted stepped-frequency (SF) CMP antenna array GPR system. The system has three pairs of Vivaldi antennas that conflgure a symmetrical CMP antenna array. Based on this conflguration, multi-ofiset CMP data gather can be acquired at every measurement posi- tion. Migration is an important signal processing method that can improve signal-clutter ratio and reconstruct subsurface image. Difiraction stacking migration and Kirchhofi migration sum amplitudes along the migration trajectory that generally is hyperbolic. But when the ground surface varies acutely, we have to modify the migration trajectory by the technique of ray tracing. We compute the travel time between transmitter, receiver and each subsurface scattering point, and search the propagation ray depending on the Fermat's principle. The method is tested by an experiment data acquired by the SF CMP antenna array GPR system. The target is a metal ball that is buried under a sand mound. A nice result of ray tracing is shown in the case.

Pre-stack migration applied to GPR for landmine detection

Detection of buried landmines by ground penetrating radar (GPR) normally suffers from very strong clutter that will decrease the image quality in GPR data. Problems are also encountered when imaging steeply dipping landmines by GPR. To solve these problems, we have developed a stepped-frequency continuous-wave array antenna ground penetrating radar system, called SAR-GPR, that can acquire common middle point multi-offset data. As an approximate solution to the general wavefield inversion problem, migration algorithms were used to refocus the scattered landmine information to improve signal–clutter ratio and re-construct the landmine image. Also, pre-stack migration was found to efficiently deal with the steeply dipping landmine problem. Before migration processing, subtracting antenna coupling was used. The SAR-GPR system was tested under two conditions. The first condition is designed to simulate inhomogeneous soil under rough ground conditions and the second condition is to simulate steeply dipping buried landmine. Very strong clutter in the GPR data was exhibited in the first condition. After pre-stack migration, strong clutter was efficiently suppressed and a high quality landmine image was re-constructed in both experiments.