Clutter Signal Research Articles

Discarding Unwanted Features from GPR Images Using2DPCA and ICA Techniques

Measurements obtained using ground penetrating radar (GPR) can suffer from large amount of noise and clutter. Current methods, such as time gating and background averaging can be applied to remove reflections from air-ground interface but do not perform well when removal of unwanted clutter signals originating from the objects other than the target is needed. This work describes and evaluates performance of two signal processing techniques - two-dimensional Principal Component Analysis (2DPCA) and Independent Component Analysis (ICA) in this kind of tasks. Experimental data using simple geometric shapes under laboratory conditions, containing strong clutter components are used to demonstrate the effectiveness of the proposed techniques.

Deterministic Aided STAP for Target Detection in Heterogeneous Situations

Classical space-time adaptive processing (STAP) detectors are strongly limited when facing highly heterogeneous environments. Indeed, in this case, representative target free data are no longer available. Single dataset algorithms, such as the MLED algorithm, have proved their efficiency in overcoming this problem by only working on primary data. These methods are based on the APES algorithm which removes the useful signal from the covariance matrix. However, a small part of the clutter signal is also removed from the covariance matrix in this operation. Consequently, a degradation of clutter rejection performance is observed. We propose two algorithms that use deterministic aided STAP to overcome this issue of the single dataset APES method. The results on realistic simulated data and real data show that these methods outperform traditional single dataset methods in detection and in clutter rejection.

Ultrasonic Flaw Detection and Imaging through Reverberant Layers via Subspace Analysis and Projection

Ultrasonic flaw detection and imaging through reverberant layers are challenging problems owing to the layer-induced reverberations and front surface reflections. These undesired signals present a strong clutter and mask the flaw echoes. In this paper, a subspace-based approach is developed for removing, or significantly reducing, the unwanted reverberations, enabling proper flaw detection and imaging. The technique utilizes a set of independent clutter-only reference measurements of the material through the layer. If these measurements are not available, array measurements of the material with flaws are used instead. The clutter, due to its high strength relative to the flaw reflections, forms a subspace spanned by the eigenvectors corresponding to the dominant eigenvalues of the data covariance matrix. The clutter subspace is estimated and removed using orthogonal subspace projection. The clutter usually occupies multidimension subspace that is dependent on the level of coupling, material inhomogeneity, surface roughness, and the sampling rate of the measurements. When the clutter-only reference is not available, information theoretic techniques are used to estimate the dimension of the clutter subspace so that clutter signals are sufficiently suppressed without distorting the flaw signals. The effectiveness of the proposed approach is demonstrated using simulations and real measurement results.

Ionospheric and auroral clutter models for HF surface wave and over‐the‐horizon radar systems

The detection performance of high frequency surface wave radar (HFSWR) and high frequency over‐the‐horizon radar (OTHR) systems is heavily influenced by the presence of radar clutter. In HFSWR systems, the clutter has its origins in vertical‐incidence ionospheric reflections, whereas in OTHR systems, the origin is Bragg backscatter from plasma structures in the auroral zone. This paper models the spreading of the radar clutter signal in the Doppler and angle‐of‐arrival domains that arises from forward‐scattering effects as the radar pulse propagates through regions of ionospheric plasma irregularities. The models use a geometric optics approach to determine the power spectrum of the radar signal phase. This power spectrum is then used to simulate three‐dimensional space‐time‐range radar data cubes. The accuracy of the models is tested by comparing the simulated data to measured data cubes. As an application, the data are then used to evaluate the performance of the newly developed fast fully adaptive (FFA) space‐time adaptive processing (STAP) scheme to improve the extraction of target echoes from a clutter background.

GLRT Detection of Moving Targets via Multibaseline Along-Track Interferometric SAR Systems

Along-track interferometric synthetic aperture radar systems can be used for ground moving target indication. We analyze a scheme for detecting moving targets with unknown parameters (velocity and signal-to-clutter ratio) and with constant false-alarm rates, based on the generalized likelihood ratio test (GLRT), and adopting a Gaussian model for target and clutter signals. We compare its performance with the one obtained in the ideal case of known target parameters applying the likelihood ratio test (LRT). A closed form for the LRT receiver operating characteristic is derived and used as reference for GLRT performance assessment. The analysis is carried out on simulated TerraSAR-X data.

Artificial intelligence techniques for clutter identification with polarimetric radar signatures

The use of different artificial intelligence (AI) techniques for clutter signals identification in the context of radar based precipitation estimation is presented. The clutter signals considered are because of ground clutter, sea clutter and anomalous propagation whereas the explored AI techniques include the support vector machine (SVM), the artificial neural network (ANN), the decision tree (DT), and the nearest neighbour (NN) systems. Eight different radar measurement combinations comprising of various polarimetric spectral signatures — the reflectivity (ZH), differential reflectivity (ZDR), differential propagation phase (ΦDP), cross-correlation coefficient (ρHV), velocity (V) and spectral width (W) from a C-band polarimetric radar are taken into account as input vectors to the AI systems. The results reveal that all four AI classifiers can identify the clutter echoes with around 98–99% accuracy when all radar input signatures are used. As standalone input vectors, the polarimetric textures of the ΦDP and the ZDR have also demonstrated excellent skills distinguishing clutter echoes with an accuracy of 97–98% approximately. If no polarimetric signature is available, a combination of the texture of ZH, V and W representing typical measurements from a single-polarization Doppler radar may be used for clutter identification, but with a lower accuracy when compared to the use of polarimetric radar measurements. In contrast, the use of ZH or W alone is found less reliable for clutter classification. Among the AI techniques, the SVM has a slightly better score in terms of various clutter identification indicators as compared to the others. Conversely, the NN algorithm has shown a lower performance in identifying the clutter echoes correctly considering the standalone radar signatures as inputs. Despite this, the performance among the different AI techniques is comparable indicating the suitability of the developed systems, and this is further supported when results are compared with the fuzzy logic and Bayes classifiers.

Optimal Gabor-Frame-Expansion-Based Intermittent-Clutter-Filtering Method for Radar Wind Profiler

Abstract Intermittent clutter signals are frequently observed by radar wind profilers during the seasonal bird migration. A novel statistical filtering algorithm based on a simultaneous time–frequency analysis of the profiler’s raw data was recently proposed to address shortcomings of existing methods. The foundation of this method is a Gabor frame expansion of the complex time series of the demodulated receiver voltage. In this paper, two objective criteria are suggested to obtain an optimal setup for the discrete Gabor frame expansion from the multitude of possibilities: first, the choice of almost-tight frames for a predefined maximum redundancy and second, the requirement that the analyzing bandwidth of the used Gaussian window function should provide a simultaneously sparse representation of both atmospheric signal components and intermittent clutter. The question of optimal sampling settings, especially dwell time, for a maximum reduction of bird interference is also discussed. Using data obtained during intense bird migration events it is shown that a combination of filtering and quality control of the result is required to prevent the occurrence of significant systematic and correlated errors in the final wind measurement.

PRINCIPLE COMPONENT ANALYSIS AND FUZZY LOGIC BASED THROUGH WALL IMAGE ENHANCEMENT

Principle component analysis based through wall image enhancement is proposed which is capable of discriminating target, noise and clutter signals. The overlapping boundaries of clutter, noise and target signals are separated using fuzzy logic. Fuzzy inference engine is used to assign weights to principle components. The proposed scheme works well signiflcantly for extracting multiple targets having difierent range proflles in heavy cluttered through wall images. Simulation results are compared on the basis of mean square error, peak signal to noise ratio and visual inspection.

Wind Turbine Radar Cross Section

The radar cross section (RCS) of a wind turbine is a figure of merit for assessing its effect on the performance of electronic systems. In this paper, the fundamental equations for estimating the wind turbine clutter signal in radar and communication systems are presented. Methods of RCS prediction are summarized, citing their advantages and disadvantages. Bistatic and monostatic RCS patterns for two wind turbine configurations, a horizontal axis three-blade design and a vertical axis helical design, are shown. The unique electromagnetic scattering features, the effect of materials, and methods of mitigating wind turbine clutter are also discussed.

CLUTTER SIGNAL FILTERING FOR DOPPLER SIGNAL

Embodiments for filtering clutter signal from Doppler signal in an ultrasound system are disclosed. In one embodiment, a Doppler signal acquiring unit may transmit and receive ultrasound signals to and from a target object to acquire Doppler signal. A signal processing unit performs filtering upon the Doppler signal by using a first clutter filter having a first cutoff frequency and compute an input signal power to filtered input signal power rate (IFR) for the Doppler signal. The signal processing unit is further configured to be responsive to the IFR to modulate the Doppler signal and perform filtering upon the modulated Doppler signal by using the first clutter filter or to perform filtering the Doppler signal by using a second clutter filter having a second cutoff frequency.

Improved Clutter Suppression Algorithm for Atmospheric Target Detection Using Polarimetric Doppler Radar

When using dual-orthogonal frequency modulation continuous wave (FMCW) polarimetric radar, the polarimetric range-Doppler spectra of the atmospheric targets are contaminated by noise, clutter, and artificial signals that disturb the analysis of atmospheric target parameters, such as reflectivity, mean Doppler velocity, and Doppler width. This paper describes an improved clutter suppression method that is able to detect most atmospheric targets while suppressing clutter, noise, and artificial signals as well as possible. By using logical decisions that are based on the spectral linear depolarization ratios (SLDRs), a binary mask matrix is constructed, and the characteristics of this original mask matrix are improved via mathematical morphology methods. To suppress clutter, noise, and artificial signals and retain atmospheric targets, we multiply the range-Doppler spectra by the improved mask matrix. The raw atmospheric data are acquired using the Polarimetric Agile Radar S- And X-band (PARSAX) radar, which is able to measure the backscattering matrix of atmospheric targets in one sweep. After calibration, the range-Doppler polarimetric spectra are processed using the proposed clutter suppression method, and the atmospheric targets are successfully detected. Compared with the unprocessed data and the data processed by noise clipping and double SLDR filtering, the data processed by the proposed method show improvement in the ability to determine the atmospheric targets in the range-Doppler spectrogram, the reflectivity, the mean Doppler velocity, and the Doppler width.

Study on Active Coded Radiometric Calibration Technique of SAR

This paper studies Active Coded Radiometric Calibration Technique (ACRCT) of SAR. ACRCT has been widely studied as SAR calibration technique. In some calibration sites, background is complicated, which will affect the radiometric calibration precision. The influence of background clutter signal to the precision of radiometric calibration is analyzed, and an equation to calculate system transfer function error is proposed. With this equation, some simulation experiments are carried out. The principle and signal processing flowchart of ACRCT are studied in detail. Two clutter models are built and based on them, ACRCT simulation experiments are carried out. Experiment results testify that ACRCT can effectively restrain the influence of background clutter signal and improve the precision of radiometric calibration of SAR, especially enhance flexibility of choosing calibration field.

Weak Coding Signal Detection under the Background of Sea Clutter

A new method of detection of coding signals under the background of sea clutter is presented. The process of signal detection consists of three stages: modeling sea clutter signals based on chaos, one-step ahead prediction of chaotic signals and detection decision making. In this method, models of chaotic signals were created in the form of multi-layer perceptron neural networks, coded signals take on 13-element Barker code. The experiment results show detection of coding signal by using this method has higher detection probability and lower false alarm probability and good performance of the whole detection although with a low SNR. This method turned out to be very robust to different chaotic signals.

Bayesian mixture models for source separation in MEG

This paper discusses the problem of imaging electromagnetic brain activity from measurements of the induced magnetic field outside the head. This imaging modality, magnetoencephalography (MEG), is known to be severely ill posed, and in order to obtain useful estimates for the activity map, complementary information needs to be used to regularize the problem. In this paper, a particular emphasis is on finding non-superficial focal sources that induce a magnetic field that may be confused with noise due to external sources and with distributed brain noise. The data are assumed to come from a mixture of a focal source and a spatially distributed possibly virtual source; hence, to differentiate between those two components, the problem is solved within a Bayesian framework, with a mixture model prior encoding the information that different sources may be concurrently active. The mixture model prior combines one density that favors strongly focal sources and another that favors spatially distributed sources, interpreted as clutter in the source estimation. Furthermore, to address the challenge of localizing deep focal sources, a novel depth sounding algorithm is suggested, and it is shown with simulated data that the method is able to distinguish between a signal arising from a deep focal source and a clutter signal.

FM-CW 레이다 시스템에서의 위상잡음 영향 분석에 관한 연구

FM-CW 레이다 시스템에서는 이동 목표물 및 기상 현상 등의 정밀한 원격탐지를 위하여 대상 목표물의 거리 및 이동 속도에 따른 주파수 변이 와 수신 신호의 크기 정도를 정확하게 추출하여야 한다. 따라서 이러한 목적으로 최종 수신 단에서 얻어진 비트 신호에 대한 스펙트럼 추정을 하게 된다. 그러나 대부분의 레이다 운용환경에서는 매우 큰 클러터 반사가 나타나게 되며 레이다 시스템에서의 위상잡음은 이러한 강력한 클러터들뿐만 아니라 목표물반사 신호의 스펙트럼들도 추가적으로 확산시키게 된다. 따라서 본 논문에서는 이러한 위상잡음이 클러터 및 목표물 신호의 비트 스펙트럼에 미치는 영향을 분석하였다. 위상잡음의 특성에 따라 클러터와 비트신호가 확산되며 그정도에 따라 인접신호의 분리 가능정도가 결정됨을 확인할 수 있다. It is necessary to estimate the Doppler spectrum for each range cell for the extraction of useful information from the return echoes in radar systems used for the remote sensing purpose such as detection of moving targets and weather surveillance. The signal amplitude in the beat frequency band is the important parameter in the detection and tracking of targets. However, strong clutter echoes do exist in most radar operation environments and the system phase noise spreads both the clutter and signal echoes of the target. In this paper, the effects of this system phase noise are analyzed concerning the clutter and the signal beat spectrum. It is shown that the separation capability of adjacent beat signal depends on the degree of spread in the clutter and beat signal caused by the radar system phase noise

Two-Frequency Radar Experiments for Sounding Glacier Ice and Mapping the Topography of the Glacier Bed

We performed airborne experiments using 150- and 450-MHz radars to measure ice thickness on the Greenland ice sheet. Our objectives were to investigate to what degree surface clutter obscures the basal echo when airborne measurements are made at different elevations and at different frequencies. We also explored interferometric techniques for processing the data to form swath measurements of ice thickness. We found that surface clutter was minimal for either frequency when operated at low aircraft elevations (500 m above the ice sheet surface) or over benign regions of the ice sheet. Because signal-to-clutter ratios were favorable, we found that we could retrieve the swath measurements of ice thickness at both frequencies using an interferometric technique. At high elevation, surface clutter degraded the 150-MHz signal, but the nadir ice thickness was still retrievable. The basal return in high-elevation 450-MHz data was detectable only after additional beam-steering techniques were applied to the data to reduce the surface clutter signal. Results suggest that interferometric cross-track ice-thickness measurements can be successfully made given a sufficient number of antenna elements driven at either 150 or 450 MHz and flown at both high and low elevations over the interior ice sheet.

An Advanced Filter Adaptive to Non-Stationary Clutter in Ultrasonic Color Flow Imaging

Traditional clutter rejection methods are based on the hypothesis that the clutter signal originates from stationary or moving tissue with a constant velocity, and they are not valid when there exists considerable acceleration of tissue motion. In this paper, we propose an advanced clutter filter adapted to accelerated tissue motion. It uses the instantaneous frequency to realize the DC remove, and uses the bandwidth of the clutter signal to choose the cutoff frequency of the wall filter, Simulation shows this advanced adaptive filter can efficiently attenuate the non-stationary clutter signals and improves the accuracy and the flexibility of the blood flow estimation.

PCA-Based Detection Algorithm of Moving Target Buried in Clutter in Doppler Frequency Domain

This letter proposes a novel technique for detecting a target signal buried in clutter using principal component analysis (PCA) for pulse-Doppler radar systems. The conventional detection algorithm is based on the fast Fourier transform-constant false alarm rate (FFT-CFAR) approaches. However, the detection task becomes extremely difficult when the Doppler spectrum of the target is completely buried in the spectrum of clutter. To enhance the detection probability in the above situations, the proposed method employs the PCA algorithm, which decomposes the target and clutter signals into uncorrelated components. The performances of the proposed method and the conventional FFT-CFAR based detection method are evaluated in terms of the receiver operating characteristics (ROC) for various signal-to-clutter ratio (SCR) cases. The results of numerical simulations show that the proposed method significantly enhances the detection probability compared with that obtained using the conventional FFT-CFAR method, especially for lower SCR situations.

High-resolution ultrasound displacement measurement using coded excitations

Resolution of displacement measurements based on ultrasound pulse-echo techniques is limited by the center frequency of the transmitted wave, echo sampling rate, quantization errors, and electronic noises in the measurement system. We developed a new method utilizing the clutter signal in coded excitations to determine the displacement of an object or a desired region of an object with much improved resolution. The method includes transmitting a pair of Golay complementary sequences, receiving echoes from the object or a region of the object, compressing the pulse, eliminating the main lobe, and determining the object displacement between the two transmissions from the residual clutter signal around the main lobe of the compressed pulse. Results of computer simulations showed that the new method improved the resolution by several orders of magnitude and was more robust to noise than traditional pulse-echo methods. The new method was also evaluated using an experimental ultrasound system (10 MHz center frequency, 100 MHz sampling rate, and 8-bit sampling precision). A high precision in the displacement measurement was achieved with a measurement error of ¿5.76 nm ±36.27 nm (mean ± standard deviation). The method has the potential to be applied in biomedical and industrial measurements of distance, displacement, and thickness.

A Robust Detection in the Presence of Clutter and Jammer Signals with Unknown Powers

This work extends the constant false alarm rate (CFAR) detection methodology to detection in the presence of two independent interference sources with unknown powers. The proposed detector is analyzed on the assumption that clutter and jammer covariance structures are known and have relatively low rank properties. The limited-dimensional subspace-based approach leads to a robust false alarm rate (RFAR) detector. The RFAR detection algorithm is developed by an adaptation and extension of Hotelling's principal-component method. The detector performance loss and false alarm stability loss to unknown clutter and jammer powers have been evaluated for example scenario.