Clutter Signal Research Articles

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.

Small Floating Target Detection Method Based on Chaotic Long Short-Term Memory Network

In order for the detection ability of floating small targets in sea clutter to be improved, on the basis of the complete ensemble empirical mode decomposition (CEEMD) algorithm, the high-frequency parts and low-frequency parts are determined by the energy proportion of the intrinsic mode function (IMF); the high-frequency part is denoised by wavelet packet transform (WPT), whereas the denoised high-frequency IMFs and low-frequency IMFs reconstruct the pure sea clutter signal together. According to the chaotic characteristics of sea clutter, we proposed an adaptive training timesteps strategy. The training timesteps of network were determined by the width of embedded window, and the chaotic long short-term memory network detection was designed. The sea clutter signals after denoising were predicted by chaotic long short-term memory (LSTM) network, and small target signals were detected from the prediction errors. The experimental results showed that the CEEMD-WPT algorithm was consistent with the target distribution characteristics of sea clutter, and the denoising performance was improved by 33.6% on average. The proposed chaotic long- and short-term memory network, which determines the training step length according to the width of embedded window, is a new detection method that can accurately detect small targets submerged in the background of sea clutter.

A weather signal detection algorithm based on EVD in elevation for airborne weather radar

Interest is growing in the application of the spatial location information from weather scenario to the monitoring of meteorological signal for airborne weather radar system. Here, a new algorithm utilizes the differences between ground clutter and meteorological signal in terms of the spatial location in elevation dimension to achieve weather signal detection (WSD). Specifically, weather signal submerged in ground clutter background is detected over a so-called diagram of the second eigenvalue to improve weather observation. Generally, this diagram is acquired by implementing eigenvalue decomposition (EVD) operation on raw data which is collected via dual-channel in elevation. For WSD, the second eigenvalue in the diagram is employed as the test statistic. And this study investigates the statistics of the second eigenvalue in the case that weather signal component, ground clutter component and Gaussian noise are all complex Gaussian distributed and they are statistically independent. Based on the statistics, a constant false-alarm rate (CFAR) detector is also designed and then the second eigenvalue of the cell under test undergoes the CFAR detector to screen out the pixels containing meteorological signal component. Simulations, as well as experimental results, are presented to demonstrate the theoretical analysis and to evaluate the detection performance of the proposed EVD-based algorithm. As compared to most of the current WSD approaches, the presented EVD-based method really shows increased detection capability and great robustness.

High Frame Rate Volumetric Imaging of Microbubbles Using a Sparse Array and Spatial Coherence Beamforming.

Volumetric ultrasound imaging of blood flow with microbubbles enables a more complete visualization of the microvasculature. Sparse arrays are ideal candidates to perform volumetric imaging at reduced manufacturing complexity and cable count. However, due to the small number of transducer elements, sparse arrays often come with high clutter levels, especially when wide beams are transmitted to increase the frame rate. In this study, we demonstrate with a prototype sparse array probe and a diverging wave transmission strategy, that a uniform transmission field can be achieved. With the implementation of a spatial coherence beamformer, the background clutter signal can be effectively suppressed, leading to a signal to background ratio improvement of 25 dB. With this approach, we demonstrate the volumetric visualization of single microbubbles in a tissue-mimicking phantom as well as vasculature mapping in a live chicken embryo chorioallantoic membrane.

Preliminary investigation on clutter filtering based on deep learning

In recent years, singular value decomposition (SVD)-based clutter filters have received widespread attention in ultrasound flow imaging owing to their high performance over traditional clutter filters in suppressing clutter signals. The excellent performance of the SVD clutter filter depends on its adaptive nature. The SVD clutter filter adaptively rejects echoes from slowly moving clutters, allowing visualization of echoes from blood cells. Owing to this property, the SVD filter works well throughout a cardiac cycle. Recently, deep neural networks have been used for a variety of tasks. The adaptive nature of deep neural networks would be beneficial for clutter filtering in ultrasonic blood flow imaging. In the present study, we conducted a preliminary study on clutter filtering using a long short-term memory neural network. Experimental results suggested that the proposed deep-learning clutter filter achieved a comparable performance than SVD one in terms of contrast values.

ASSESSMENT OF PLAQUE VULNERABILITY USING A NOVEL TECHNIQUE: MULTI-SPECTRAL PHOTOACOUSTIC IMAGING (CVENT-PAI)

Objective: Identification of intraplaque haemorrhage using magnetic resonance imaging (MRI) is expensive, technically demanding and sometimes uncomfortable. Photoacoustic imaging (PAI) is a new non-invasive technique combining multi-wavelength infrared laser light and ultrasound (US) imaging, able to discriminate blood and other components in the tissues. The measurement by the multi-spectral PAI is likely to be more applicable and therefore could be complementary to MRI. Validate a portable multimodal and multi-wavelength PAI system, for the identification of intraplaque haemorrhage and compare with MRI. Design and method: Patients underwent carotid MRI, and US. Photoacoustic (PAI) acquisitions are performed and analysed posteriori blindly of the status of the subjects and results of MRI/US. All patients gave written informed consent. Histology of plaque will be performed as gold standard. Results: We included 22 patients (71 ± 5 years old) with carotid stenosis grade of >70% (NASCET). PAI imaging was feasible in all patients. On raw images, only clutter signal can be observed. After adequate post processing, carotid wall with the intima media and plaques were clearly visible with PAI. PAI signal from the lumen can be observed, also strong PAI signals are visible inside the plaque in a reproducible manner. Correspondence between MRI and PAI will be analysed later. Conclusions: The use of the PAI technique for carotid plaque assessment is feasible, and PAI signal are observed inside the plaque. Further analyses are needed to assess the reflected PA signal inside the plaque, for haemorrhage detection in the future.

Tapered Vector Doppler for Improved Quantification of Low Velocity Blood Flow.

A new vector velocity estimation scheme is developed, termed tapered vector Doppler (TVD), aiming to improve the accuracy of low velocity flow estimation. This is done by assessing the effects of singular value decomposition (SVD) and finite impulse response (FIR) filters and designing an estimator which accounts for signal loss due to filtering. Synthetic data created using a combination of in vivo recordings and flow simulations were used to investigate scenarios with low blood flow, in combination with true clutter motion. Using this approach, the accuracy and precision of TVD was investigated for a range of clutter-to-blood and signal-to-noise ratios. The results indicated that for the investigated carotid application and setup, the SVD filter performed as a frequency-based filter. For both SVD and FIR filters, suppression of the clutter signal resulted in large bias and variance in the estimated blood velocity magnitude and direction close to the vessel walls. Application of the proposed tapering technique yielded significant improvement in the accuracy and precision of near-wall vector velocity measurements, compared to non-TVD and weighted least squares approaches. In synthetic data, for a blood SNR of 5 dB, and in a near-wall region where the average blood velocity was 9 cm/s, the use of tapering reduced the average velocity magnitude bias from 26.3 to 1.4 cm/s. Complex flow in a carotid bifurcation was used to demonstrate the in vivo performance of TVD, and it was shown that tapering enables vector velocity estimation less affected by clutter and clutter filtering than what could be obtained by adaptive filter design only.

A FOD Detection Approach on Millimeter-Wave Radar Sensors Based on Optimal VMD and SVDD

Foreign object debris (FOD) on airport runways can cause serious accidents and huge economic losses. FOD detection systems based on millimeter-wave (MMW) radar sensors have the advantages of higher range resolution and lower power consumption. However, it is difficult for traditional FOD detection methods to detect and distinguish weak signals of targets from strong ground clutter. To solve this problem, this paper proposes a new FOD detection approach based on optimized variational mode decomposition (VMD) and support vector data description (SVDD). This approach utilizes SVDD as a classifier to distinguish FOD signals from clutter signals. More importantly, the VMD optimized by whale optimization algorithm (WOA) is used to improve the accuracy and stability of the classifier. The results from both the simulation and field case show the excellent FOD detection performance of the proposed VMD-SVDD method.

First-Order Sea Clutter Suppression for High-Frequency Surface Wave Radar Using Orthogonal Projection in Spatial–Temporal Domain

The broadening first-order sea clutters caused by the signals from different directions with various radial current velocities create severe disturbance for target detection using high-frequency surface wave radar (HFSWR). Conventional sea clutter suppression methods tend to remove the sea clutter and target signals when they are mixed in the Doppler spectrum. Based on the characteristics of the target signal and sea clutter in spatial–temporal domain, a new first-order sea clutter suppression method for HFSWR using orthogonal projection is proposed. The proposed method uses the data from multichannels and slow-time domain at the adjacent range cell to construct a covariance matrix, which can be used to obtain the sea clutter subspace by eigendecomposition. Later, original signals are projected onto the sea clutter subspace. Finally, subtract the component of the original signals in the sea clutter subspace from the original signals to achieve the suppression of sea clutter by retaining the target signals. The simulation and experimental results for a single target and multiple targets cases indicate that the proposed method can suppress the first-order sea clutter effectively, which enhances the target detection capacity in the sea clutter zone for HFSWR.

Adaptive CFAR Detectors for Mismatched Signal in Compound Gaussian Sea Clutter With Inverse Gaussian Texture

This letter deals with the adaptive detection problem in compound Gaussian sea clutter with inverse Gaussian texture when the mismatched signal occurs. In order to reject the mismatched signal, we introduce a fictitious signal to the null hypothesis based on the adaptive beamformer orthogonal rejection test (ABORT). We adopt the two-step generalized likelihood ratio test (GLRT) criterion and develop two selective detectors, i.e., the ABORT-like compound Gaussian with inverse Gaussian (A-IGCG) detector and ABORT-like maximum <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a posteriori</i> (MAP) compound Gaussian with inverse Gaussian (AM-IGCG) detector. Both the proposed detectors are proved to be constant false alarm rate (CFAR) detectors. The several experiments that compare the proposed detectors with other selective detectors and compound Gaussian detectors are conducted by using two types of real sea data, i.e., IPIX 1998 and 2019 real sea data. The numerical results indicate that the novel detectors exhibit significant tradeoffs between performances of detection and selectivity property.

An Estimator for Weather Radar Doppler Power Spectrum via Minimum Mean Square Error

This article proposes a method for estimating the Doppler power spectrum (DPS) of a weather radar via minimum mean square error (MMSE). In order to detect severe weather phenomena that mostly occur within the lowest few kilometers of the atmosphere, weather radars have to direct their beams at low elevation angles, and the received signals from such observations usually contain reflections from the ground and buildings, so-called ground clutter. The MMSE estimator, which is an adaptive spectral estimator, allows weather radar DPSs to be obtained with excellently reduced ground clutter contaminations. The MMSE estimator was examined by numerical simulations, which supposed various precipitation and ground clutter scenarios and DPS estimation parameter values. The MMSE estimator provided DPSs almost as accurate as those from the traditional Fourier and windowed Fourier estimators in simulations with no ground clutter and much better DPSs than those estimators in the presence of ground clutter. Furthermore, the MMSE estimator gave better suppression of ground clutter contamination than the Capon estimator, which is another adaptive spectral estimator. As a result of statistical evaluations, ground clutter signals with a strong clutter-to-noise ratio of 70 dB appeared only in a narrow velocity range of the MMSE DPS, from −2 to 2 m/s, and caused degradation of the mean and standard errors outside this velocity range by just 1 dB. The MMSE estimator was also applied to signals received by actual weather radar, and DPS estimation of precipitation signals with similarly low ground clutter contamination was demonstrated.

Using DTMB‐Based Passive Radar for Small Unmanned Aerial Vehicle Detection

There is inevitable polarization angle deviation between the target echo signal and the direct path signal of illuminator of opportunity (IO) in passive radar. In order to investigate the potential performance loss in target detection induced by the random deviation, small unmanned aerial vehicle (UAV) detection experiments with digital television terrestrial multimedia broadcasting‐ (DTMB‐) based passive radar are conducted in this paper. Experimental results show that the polarization angles of the clutter signal and target echo signal are inconsistent. When the polarization diversity technology is used to suppress the clutter signal, the processing performance of the target echo signal may be reduced. On the premise that clutter is effectively suppressed by the processing algorithm, polarization synthesis can maximize the target echo signal processing gain. The effectiveness of the target localization algorithm combining time difference of arrival (TDOA) and direction of arrival (DOA) is also verified with polarization diversity reception in this paper.

MIMO Radar Accurate Imaging and Motion Estimation for 3-D Maneuvering Ship Target

Image deterioration problem occurs in radar imaging for ship target, which results from the complex time-varying motions of ship, the noise in channels, and the clutter on sea surface. It is hard to be solved effectively due to coherent accumulation sampling time and high-dimensional parametric model. Hence, an accurate imaging and motion estimation method based on multiple-input–multiple-out (MIMO) radar is presented. First, the multidimensional signal model is built to characterize target features accurately. To reduce the interference from sea clutters, a preprocessing strategy is exerted based on the space–time adaptive processing (STAP) theory, clutter signals can be suppressed effectually with constructing a Doppler spectrum model. Then, for accurate imaging and motion estimation, a combined trace norm minimization problem is deduced based on the relaxation of tensor rank, where the noise in sea environments is also considered. Meanwhile, generalized tensor total variation constraint is developed to ensure stable estimation and smooth imaging results when separating the noise term. Accordingly, an effective decomposition criterion is formulated based on alternating direction multiplier method (ADMM) strategy, and motion parameters can be precisely calculated based on the least square (LS) method. Finally, theoretical analysis and simulation results present the accurate performance of the proposed method.

Innovative Two-Stage Radar Detection Architectures in Adverse Scenarios Using Two Training Data Sets

This letter focuses on adaptive target detection in the presence of multiple interference sources, which comprise clutter, thermal noise, noise-like jammers, and fully-correlated (or coherent) signals. In order to account for different operating scenarios, we formulate the problem at hand in terms of a multiple hypothesis test with several alternative hypotheses representative of each considered scenario. In this context, we devise a family of two-stage detection architectures capable of classifying the specific scenario and, hence, of working under different operating conditions. The performance analysis shows the effectiveness of the detector based upon the Generalized Information Criterion also in comparison with traditional adaptive decision schemes.

Frequency-Offset Self-Injection-Locked Radar With Digital Frequency Demodulation for SNR Improvement, Elimination of EMI Issue, and DC Offset Calibration

The proposed frequency-offset self-injection-locked (FOSIL) radar combines two independent SIL radar front-ends with up/down-mixers. The transmit frequency can be held constant by properly adjusting the injection phases and amplitudes of two oscillators. Therefore, electromagnetic interference (EMI) in the monitoring of a moving subject using conventional SIL radars is eliminated, and multiple FOSIL radars can be operated simultaneously within a limited bandwidth. In this work, one RF mixer with digital frequency demodulation is used to extract the motion of the target relative to the radar, reducing the system complexity and eliminating the $I/Q$ imbalance and dc offset calibration. The proposed system substantially improves the SNR over that of the conventional SIL radar by 28 dB, by mitigating the flicker noise and the large Doppler shift. In noncontact vital sign sensing experiments, the 5.8-GHz industrial scientific medical (ISM) band FOSIL radar prototype had a sensing range of 8 m, and its frequency swing range was reduced by over 96% without or with clutter signals. Two FOSIL radars with a frequency spacing of 2 MHz can yield exact information on the cardiopulmonary activities of two subjects at the same time in the experimental environment.

Classification of UAV-to-Ground Targets Based on Enhanced Micro-Doppler Features Extracted via PCA and Compressed Sensing

In order to achieve precise operations on specified targets from the unmanned aerial vehicles (UAVs), classifying ground targets correctly is especially important. Micro-Doppler effect which provides unique information of targets has been the basis for targets classification. Due to the effect of ground clutter, noise and complex signal modulation, enhancing micro-Doppler features of UAV-to-ground targets is necessary for accurate classification. This paper firstly establishes the models of UAV-to-ground targets including wheeled vehicles, tracked vehicles and pedestrians to analyze their micro-Doppler differences. Secondly, Principal Components Analysis (PCA) is utilized to remove the ground clutter. Compared with other algorithms, PCA can use a small amount of calculation to remove the ground clutter while retain nearby micro-Doppler signals. Then, micro-Doppler signals are sparsely represented based on Fourier basis. Orthogonal Matching Pursuit (OMP) is chosen to reconstruct micro-Doppler components and refine spectral lines after random projection. The three steps make up Compressed Sensing (CS) together. At last, non-linear transform of Doppler spectrum is conducted to further enhance the distinction of micro-Doppler spectral lines. Distinguishing micro-Doppler features are extracted from pre-processed micro-Doppler signals, which eventually contributes to the accurate targets classification. Comparison with other methods is also made to prove the robustness and anti-noise performance of proposed method.

주파수 가중 MTI를 이용한 FMCW 레이다 손동작 인지 기법

This paper proposes a signal processing technique that recognizes hand motions using a 60-GHz frequency-modulated continuous- wave radar system. In the case of hand motion recognition with a radar, the recognition accuracy decreases owing to a clutter signal generated by the upper body and differences between the hand sizes of different people. To solve this problem, this paper proposes a moving target indicator filter with a weighted frequency for each frequency, which improves hand motion recognition accuracy, removes self-interference clutter signals, and uses a range profile and Doppler profile. Neural networks can recognize hand movements; hence, the proposed five hand gesture recognition techniques exhibited an 87.3% accuracy, regardless of the user.

Three-dimensional evaluation of microvascular networks using contrast-enhanced ultrasound and microbubble tracking.

Evaluating tumor microvascular networks with use of contrast-enhanced ultrasound (CEUS) imaging and one-dimensional (1D) linear array transducers have inherit limitations as tumors exist in volume space. The use of a mechanical sweep allows users to overcome this limitation. To that end, we have developed a new method by which a 1D linear array transducer can be mechanically scanned over a region-of-interest to capture a volume of data allowing for the evaluation of microvasculature structures in 3D space. After intravascular injection of a microbubble (MB) contrast agent into a developing chicken embryo, a sequence of CEUS images were acquired using a Vevo 3100 scanner (VisualSonics Inc) and taken at multiple tissue cross-sections. The CEUS images were processed with a singular value filter (SVF) to help remove any clutter signal. MB localization was performed, and frame-to-frame MB movement was analyzed to produce spatial maps depicting blood flow and velocity at each tissue cross-section. Reconstruction of all images allowed visualization of microvascular networks and blood velocity distribution in volume space.

Intelligent recognition of milling cutter wear state with cutting parameter independence based on deep learning of spindle current clutter signal

In the process of milling, tool wear directly affects the quality and accuracy of workpieces. Online recognition of milling cutter wear state has been and remains a growing interest in intelligent manufacturing to increase the machining efficiency and control the unqualified rate of workpieces. The effective value of spindle current can effectively characterize the wear state of milling cutter, but it will change along with machining process parameters, which are not suitable for the wear state recognition of milling cutter (WSRMC) under complex working conditions. We present LeNet-WSRMC network, a novel approach to recognize wear state of milling cutter based on the clutter signal of spindle current. The cutting vibration and tool wear are the main reasons for exciting the dynamic cutting force and the clutter signal of spindle current. In order to fully describe the generation mechanism of the clutter signal, we divide the wear state of milling cutter into four categories (i.e., normal wear, severe wear, abnormal vibration caused by tool wear, and abnormal vibration caused by improper selection of cutting parameter when the tool is sharp). LeNet-WSRMC network uses the deep convolutional neural network (DCNN) model to extract features from the spindle current clutter signal (SCCS) as the wear state of milling cutter classification index. A series of experiments with different cutting parameters and conditions are implemented to validate the effectiveness and generalization of our proposed methodology. The experimental results show that this method can realize the online accurate recognition of the wear state of milling cutter under the condition of complex working condition. This study lays a foundation for the prediction of the remaining life of the milling cutter under complex working conditions and the reasonable formulation of the replacement rules of the milling cutter.

Tumor Vascular Networks Depicted in Contrast-Enhanced Ultrasound Images as a Predictor for Transarterial Chemoembolization Treatment Response

Hepatocellular carcinoma (HCC) is prevalent worldwide. Among the various therapeutic options, transarterial chemoembolization (TACE) can be applied to the tumor vascular network by restricting the nutrients and oxygen supply to the tumor. Unique morphologic properties of this network may provide information predictive of future therapeutic responses, which would be significant for decision making during treatment planning. The extraction of morphologic features from the tumor vascular network depicted in abdominal contrast-enhanced ultrasound (CEUS) images faces several challenges, such as organ motion, limited resolution caused by clutter signal and segmentation of the vascular structures at multiple scales. In this study, we present an image processing and analysis approach for the prediction of HCC response to TACE treatment using clinical CEUS images and known pathologic responses. This method focuses on addressing the challenges of CEUS by incorporating a two-stage motion correction strategy, clutter signal removal, vessel enhancement at multiple scales and machine learning for predictive modeling. The morphologic features, namely, number of vessels (NV), number of bifurcations (NB), vessel to tissue ratio (VR), mean vessel length, tortuosity and diameter, from tumor architecture were quantified from CEUS images of 36 HCC patients before TACE treatment. Our analysis revealed that NV, NB and VR are the dominant features for the prediction of long-term TACE response. The model had an accuracy of 86% with a sensitivity and specificity of 89% and 82%, respectively. Reliable prediction of the TACE therapy response using CEUS-derived image features may help to provide personalized therapy planning, which will ultimately improve patient outcomes.