Clutter Statistics Research Articles

GPU-Oriented Designs of Constant False Alarm Rate Detectors for Fast Target Detection in Radar Images

Constant false alarm rate (CFAR) detector is a class of widely used methods for target detection in radar images. Classical CFAR detectors perform target detection on a pixel-by-pixel basis using certain sliding windows for estimating clutter statistics, which run fast for small images. However, as the image size gets large, the time cost of these detectors will increase significantly since the time complexity with respect to <i>N</i> &#x00D7; <i>N</i>-pixel image is <i>O</i>(<i>N</i>²). In practice, radar images, such as those in synthetic aperture radar (SAR), usually have very large numbers of pixels (which can be on the order of 10000 &#x00D7; 10000), making the classical CFAR detectors very time-consuming when applied to these images. In this paper, we present graphics processing unit (GPU)-oriented Designs for speeding up CFAR detectors, including smallest/greatest-of CFAR and order-statistic CFAR. The proposed designs implement CFAR detectors via tensor operations, including tensor convolution, shift, and boolean operation, which can be fast operated by GPU. Experiment results show that the proposed GPU-oriented CFAR detectors running on a high-performance Nvidia RTX 3090 GPU can be thousands of times faster than the classical CFAR detectors, and realize real-time target detection in large-size radar images. Examples using SAR and range-Doppler images are provided as illustrative applications of the proposed GPU CFAR detectors to target detection in radar images.

Cascaded Regional People Counting Approach Based on Two-Dimensional Spatial Attribute Features Using MIMO Radar

A cascaded regional people counting approach based on two-dimensional (2-D) spatial attribute features using multi-input multi-output (MIMO) radar is presented in this letter. The main task is to detect whether there is a target, and to classify the number of people in a closed metal ramp. The difficulty to accurately detect the number of people lies in the presence of strong static clutter and the strong multipath effects in the environment. In this letter, range-angle spectrum is first computed and exploited to extract the 2-D spatial attribute features, namely the effective peaks of range-angle spectrum, the spacing features and the energy block features. These features reflect the relative position relationships between the people and the environment as well as the physical structure of a certain environment. Then, a maximum likelihood classifier and a random forest classifier are cascaded and applied to detect the target presence and classify the number of people. The experiment results show that the average classification accuracy of 0 person, 1 person, and multiple persons is above 92.55%, which outperforms the existing people counting approaches.

Regional People Counting Approach Based on Multi-Motion States Models Using MIMO Radar

The existing radar-based regional people counting methods usually perform a single prediction model to classify the number of people. However, signals of different motion states of the target vary greatly, which results in some features aliasing among different numbers of people. It is difficult to find an optimal single prediction model to adapt the different motion states of the target, especially in the case of limited training data. To address this problem, we propose a regional people counting approach based on multi-motion states models using multi-input multioutput (MIMO) radar. We first use the state classification model with the 2-D spatial attribute features to classify the motion state of the target and then cascade the corresponding motion state model to classify the number of people. In particular, we incorporate the mixed state into the proposed approach to deal with the situations of mixed motion states of targets. The experimental results show that the proposed approach can be implemented in an embedded operating system, and its average classification accuracy of zero person, one person, and multiple persons is above 97.45&#x0025; and much better than that of the existing regional people counting methods in the metal ramp with strong static clutter and strong multipath, which is a small room and too small for many people.

DL-Based Clutter Removal in Migrated GPR Data for Detection of Buried Target

As a nondestructive and nonintrusive geophysical electromagnetic technology, ground-penetrating radar (GPR) has been widely applied to subsurface target detection, such as landmine detection, pipeline detection, and underground cavity detection. The target response received by the GPR system is generally contaminated by clutter, which greatly affects the detection performance of the buried targets. In this letter, a novel clutter removal method combining migration and dictionary learning (DL) is presented. First, the proposed method applies the frequency–wavenumber (F–K) migration to the received GPR B-scan data. Then, since the focused target response and the clutter in the migrated GPR B-scan data present different morphological components, DL can be applied to the migrated GPR B-scan data to separate the focused target response (point-shaped structure) from the clutter (horizontal strip-shaped structure). Both numerical simulated data and experimental data collected by a real GPR system are used to evaluate the performance of the proposed clutter removal method. The experimental results demonstrate the effectiveness of the proposed clutter removal method under irregular clutter conditions, which improves the detection ability of the buried targets.

An Inter-Subband Processing Algorithm for Complex Clutter Suppression in Passive Bistatic Radar

Clutter suppression is a challenging problem for passive bistatic radar systems, given the complexity of actual clutter scenarios (stationary, time-varying and fractional-order clutter). Such complex clutter induces intense sidelobes in the entire range-Doppler plane and thus degrades target-detection performance, especially for low-observable targets. In this paper, a novel method, denominated as the batch version of the extensive cancellation algorithm (ECA) in the frequency domain (ECA-FB), is presented for the first time, to suppress stationary clutter and its sidelobes. Specifically, in this method, the received signal is first divided into short batches in the frequency domain to coarsen the range resolution, and then the clutter is removed over each batch via ECA. Further, to suppress the time-varying clutter, a Doppler-shifted version of ECA-FB (ECA-FBD) is proposed. Compared with the popular ECA and ECA-B methods, the proposed ECA-FB and ECA-FBD obtained superior complex clutter suppression and slow-moving target detection performance with lower computational complexity. A series of simulation and experimental results are provided to demonstrate the validity of the proposed methods.

Accurate Measurement of Human Vital Signs With Linear FMCW Radars Under Proximity Stationary Clutters.

Vital sign detection using linear frequency-modulated continuous-wave (LFMCW) radar may be subject to the proximity stationary clutters. This paper presents a novel technique to synthesize the slow-time I/Q signals, which are equivalent to those in a single tone quadrature CW radar, from a single-channel LFMCW radar. It correlates the two types of radars in such a way that the proximity stationary clutters are translated to direct current (DC) offsets in the synthesized I/Q signals across slow-time. The circle-fitting based DC offsets calibration (DCcal) technique, which was developed for CW radar, can now be applied to eliminate the impact of the proximity stationary clutters in LFMCW radars for accurate vital sign detection. Moreover, the modified differentiate and cross-multiply (MDACM) algorithm can also be leveraged to eliminate the phase ambiguity issue. Thorough theoretical analysis and working principles are presented. Simulations are performed to validate the proposed technique. Moreover, exhaustive experiments are carried out with a millimeter-wave 79 GHz FMCW radar in the office environment. Mechanical vibration and vital signs are extracted with micrometer-level accuracy in the existence of proximity stationary clutters.

CFAR Detection Based on the Nonlocal Low-Rank and Sparsity-Driven Laplacian Regularization for HFSWR

Target detection in high-frequency surface-wave radar (HFSWR) systems is a challenging task due to various sources of interference. In this article, we propose a robust constant false alarm rate (CFAR) detector to solve the problem of target detection for the HFSWR by exploiting the prior information on the detection environment. Clutter amplitudes in the HFSWR are commonly assumed to follow the Weibull distribution. Additionally, distribution parameters of the clutter have nonlocal self-similarity, and targets have sparsity as well as a certain geometric structure. We design an objective function based on the Bayesian framework, which consists of a data fidelity term and some regularization terms. Specifically, we use the nonlocal low-rank (NLR) regularization to represent the prior information regarding distribution parameters of clutter, and we adopt the combination of the sparsity and Laplacian regularization to represent the prior information regarding targets. The proposed objective function inherits advantages of the NLR, sparsity, and Laplacian regularization, thereby improving the performance of the proposed detector in the nonhomogeneous clutter. By optimizing the proposed objective function, we obtain estimates of clutter statistics for every cell under test and perform the CFAR detection. Experiments on both simulated data and experimental HFSWR data demonstrate that the proposed detector has a good false alarm regulation property and detection performance in the nonhomogeneous clutter.

Deteksi Radar Terhadap Multi-Object Bergerak Dengan Pemrosesan Doppler

In general, Radar or Radio Detection and Ranging is an electromagnetic wave system that is useful to measure distance and answer and make maps of surrounding objects. Radar has an advantage compared to other navigation tools, which is that radar does not require a transmitter station as a transmitter. Radar has an electronic wave emission principle that emits short radio wave pulses emitted in a narrow beam by a directional antenna. In this study, a multi-object radar detection simulation was carried out using Dopler processing both MTI and PDP, which later on the radar will detect related objects. Multi-object here is a condition that is achieved when a navigation radar detects more than one object. The result of this research is a multi-object detection process using the MTI and PDP methods and the matched-filter obtained from the predetermined data. So Doppler processing aims to mitigate the clutter signal to improve the detection performance of moving targets even though there is a dominance of signals originating from stationary clutter.

Searching in clutter: Visual behavior and performance of expert action video game players

Searching for targets among distractors in visual scenes can be more difficult due to the presence of clutter. However, studies in various domains have shown differentiated effects according to the expertise of the searcher. The present study extended these findings to the domain of action video games expertise. 58 participants, split in 2 groups (action video game players and non-action video game players) searched for targets in visual scenes under two clutter conditions (uncluttered and high clutter). Reaction times and accuracy served as measures of performance, and the visual behavior was assessed using the number and duration of eye fixations. Our findings suggest that visual clutter has a negative influence on performance and alters the visual behavior during visual search in action video game scenes. Our results also suggest that expert action video game players might use different visual strategies to cope with clutter, leading however to no performance benefits.

Phase-Based GLRT Detection of Moving Targets with Pixel Tracking in Low-Resolution SAR Image Sequences

Spaceborne synthetic aperture radar (SAR) can provide ground area monitoring with large coverage. However, achieving a wide observation scope comes at the cost of resolution reduction owing to the trade-off between these parameters in conventional SAR. In low-resolution imaging, the moving target appears unresolved, weakly scattered, and slow moving in the image sequence, which can be generated by the subaperture technique. This article proposes a novel moving target detection method. First, interferometric phase statistics are combined with the generalized likelihood ratio test detector. A pixel tracking strategy is further exploited to determine whether a motion signal is present. These methods rely on the approximation of both clutter and noise statistics using Gaussian distributions in a low-resolution scenario. In addition, the motion signals are imaged with a subpixel offset. The proposed method is primarily validated using four real image sequences from TerraSAR-X data, which represent two types of homogeneous areas. The results reveal that moving targets can be detected in nearby areas using this strategy. The method is compared with the stack averaged coherence change detection and particle-filter-based tracking strategies.

RPCA-Based High Resolution Through-the-Wall Human Motion Feature Extraction and Classification

Radar-based assisted living has received significant amount of research interest in recent years. However, most of the existing works are focused only on free space detection. When it comes to the through-the-wall human motion detection, the wall media and indoor static non-human targets would cause clutters and significantly corrupt the motion information of human targets behind wall. In this work, we first propose to use a low center-frequency ultra-wideband (UWB) radar system to probe the behind wall scene. Then, a Robust Principal Component Analysis (RPCA) based subspace decomposition technique is employed not only to remove the stationary clutters in raw range slow-time map but also to mitigate the multipath effects in the time-frequency map. Onsite experiments of human motions detection behind a single layer of reinforced concrete wall are carried out to investigate the performance of the technique. Lastly, a two-dimensional (2D) PCA based method and a convolutional neural network (CNN) based method are employed for the motion classification. The results based on the enhanced features obtained by the proposed method show a higher accuracy than those based on the traditional features through mean value subtraction.

Riemannian distance based generalized side-lobe cancellation method for nonstationary clutter suppression

High-frequency mixed-mode surface wave radar combines advantages of both surface wave radar and sky wave radar and is capable of ocean remote sensing and early warning. However, the non-stationary clutter is the biggest challenge for the practical radar system which can submerge the low velocity vessels. Generalized side-lobe cancellation method has been successfully developed to suppress the clutter in high-frequency radar, but the performance of the method degrades in heterogeneous environment for the clutter statistics changes significantly. The invalid training data degrades the clutter suppression performance. To suppress the non-stationary clutter efficiently, a Riemannian distance based generalized side-lobe cancellation method is proposed to select the valid training data and improve the performance. Firstly, a single notch filter and virtual sliding sub-array method are utilized to block the main-lobe component. Then, a Riemannian distance based training data selection strategy is proposed to suppress the non-stationary clutter. The validity of the proposed method has been examined by measured data.

Object Tracking in Hyperspectral-Oriented Video with Fast Spatial-Spectral Features

This paper presents a correlation filter object tracker based on fast spatial-spectral features (FSSF) to realize robust, real-time object tracking in hyperspectral surveillance video. Traditional object tracking in surveillance video based only on appearance information often fails in the presence of background clutter, low resolution, and appearance changes. Hyperspectral imaging uses unique spectral properties as well as spatial information to improve tracking accuracy in such challenging environments. However, the high-dimensionality of hyperspectral images causes high computational costs and difficulties for discriminative feature extraction. In FSSF, the real-time spatial-spectral convolution (RSSC) kernel is updated in real time in the Fourier transform domain without offline training to quickly extract discriminative spatial-spectral features. The spatial-spectral features are integrated into correlation filters to complete the hyperspectral tracking. To validate the proposed scheme, we collected a hyperspectral surveillance video (HSSV) dataset consisting of 70 sequences in 25 bands. Extensive experiments confirm the advantages and the efficiency of the proposed FSSF for object tracking in hyperspectral video tracking in challenging conditions of background clutter, low resolution, and appearance changes.

Radar detection of multi-target vital signs based on blind source separation

Radar non-contact monitoring of vital signs has a broad application prospect in clinical monitoring. Aiming at the problem of strong interference in non-contact vital signs detection (Such as multi-target, random body motion), a blind source separation (BSS) signal detection method based on Fast-ICA is proposed to reduce the interference of multi-target. In this algorithm, entropy is used to evaluate the non Gaussian property, and the appropriate transformation matrix is selected, according to the statistical independence of the signals, the source signals are separated from the observed mixed signals. On this basis, the traditional blind source separation process is improved, and the wavelet transform preprocessing algorithm based on translation invariant is added to suppress the interference of static clutter. The feasibility of this method is verified by simulation experiments.

A Moving Target Velocity Estimation Method Based on the MC-MASA SAR Mode

Imaging position shift based on the multiple azimuth squint angles (MASA) mode is effective for target azimuth velocity estimation, whereas accuracy is low when target range velocity is high. In this paper, the estimation problem for both target azimuth and range velocities is considered based on the multi-channels MASA (MC-MASA) mode. Firstly, the acquisition geometry of MC-MASA mode and Doppler characteristics of a moving target are analyzed in detail, especially in squint mode. Then, for better moving target estimation, the stationary background clutter is removed using the displacement phase center antenna (DPCA) technique, and the failure in range velocity estimation with sequential SAR images is also discussed. Furthermore, a modified along-track interferometry (ATI) is proposed to preliminarily reconstruct the azimuth-and-range velocity map based on the MC-MASA mode. Since the velocity estimation accuracy is dependent on squint angle and signal-to-clutter ratio (SCR), the circumstances are divided into three cases with different iteration estimation strategies, which could expand the scene application scope of velocity estimation and achieve a high estimation accuracy along both azimuth and range directions. Finally, the performance of the proposed method is demonstrated by experimental results.

The InflateSAR Campaign: Testing SAR Vessel Detection Systems for Refugee Rubber Inflatables

Countless numbers of people lost their lives at Europe’s southern borders in recent years in the attempt to cross to Europe in small rubber inflatables. This work examines satellite-based approaches to build up future systems that can automatically detect those boats. We compare the performance of several automatic vessel detectors using real synthetic aperture radar (SAR) data from X-band and C-band sensors on TerraSAR-X and Sentinel-1. The data was collected in an experimental campaign where an empty boat lies on a lake’s surface to analyse the influence of main sensor parameters (incidence angle, polarization mode, spatial resolution) on the detectability of our inflatable. All detectors are implemented with a moving window and use local clutter statistics from the adjacent water surface. Among tested detectors are well-known intensity-based (CA-CFAR), sublook-based (sublook correlation) and polarimetric-based (PWF, PMF, PNF, entropy, symmetry and iDPolRAD) approaches. Additionally, we introduced a new version of the volume detecting iDPolRAD aimed at detecting surface anomalies and compare two approaches to combine the volume and the surface in one algorithm, producing two new highly performing detectors. The results are compared with receiver operating characteristic (ROC) curves, enabling us to compare detectors independently of threshold selection.

Advancing Ground-Based Radar Processing for Bridge Infrastructure Monitoring.

In this study, we further develop the processing of ground-based interferometric radar measurements for the application of bridge monitoring. Applying ground-based radar in such complex setups or long measurement durations requires advanced processing steps to receive accurate measurements. These steps involve removing external influences from the measurement and evaluating the measurement uncertainty during processing. External influences include disturbances caused by objects moving through the signal, static clutter from additional scatterers, and changes in atmospheric properties. After removing these influences, the line-of-sight displacement vectors, measured by multiple ground-based radars, are decomposed into three-dimensional displacement components. The advanced processing steps are applied exemplarily on measurements with two sensors at a prestressed concrete bridge near Coburg (Germany). The external influences are successfully removed, and two components of the three-dimensional displacement vector are determined. A measurement uncertainty of less than 0.1 mm is achieved for the discussed application.

An Automatic Target Detection Method Based on Multidirection Dictionary Learning for HFSWR

Target detection in a high-frequency surface-wave radar (HFSWR) system is a challenging task because radar returns are strongly polluted by various sources of interference. To address the detection problem, this letter presents an automatic detection method based on multidirection dictionary learning for HFSWR. First, we perform clutter identification and statistical analysis to become aware of the time-varying clutter environment. The analysis of real data shows that the clutter in an HFSWR system has spatial correlations and geometric directions. Second, motivated by this information, we design a multidirection dictionary learning-based constant false alarm rate (MDDL-CFAR) detector in which the spatial and geometric direction information is well represented by multidirectional dictionaries. The MDDL-CFAR can simultaneously learn dictionaries and estimate clutter statistics to adaptively set the detection threshold. Experimental results on HFSWR data sets demonstrate the effectiveness of the proposed detection method.

Detection and Localization of Multiple Humans Based on Curve Length of I/Q Signal Trajectory Using MIMO FMCW Radar

A method to detect people in multiple locations using a multiple-input and multiple-output (MIMO) frequency-modulated continuous wave (FMCW) radar system is proposed. A 2-D range-angle map is estimated by digital beamforming with virtual receiver arrays obtained via the operation of time-division multiplexing (TDM) MIMO radar. Phase variations due to human vital signs and movements allow the identification of people from stationary clutter in the range-angle map. To improve the sensitivity of phase-based human detection, a curve-length (CL) method that estimates the length of the I/Q signal trajectory is proposed. The proposed method can overcome the codomain limitation of a conventional method through its use of the standard deviation of phase variations. Measurements show that the method improves the signal-to-clutter ratio (SCR) by 10 dB compared with that when using the conventional approach.

Vital Sign Detection and Radar Self-Motion Cancellation Through Clutter Identification

This article presents a novel technique to remove the radar self-motion effects (RSMs) for an accurate detection of human vital signs. As opposed to the commonly used techniques, the proposed approach does not require any additional sensor, and instead, it extracts the RSM from the signals reflected by stationary clutters. Since the proposed technique requires to accurately identify the clutter range, two procedures for its automatic identification are proposed, aimed to detect both small and large radar motions. Besides allowing precise and reliable vital sign detection, it provides a compact, lightweight, comfortable, and cost-effective solution since potentially intrusive additional sensors are not required. Simulations have been carried out for validating the proposed approach, with an insight on the influence of different clutter radar cross sections on the sensitivity. Moreover, the effectiveness of the RSM cancellation has been experimentally demonstrated, showing its suitability for different applications, e.g., radar on moving platforms, vibrating tools, handheld devices, unmanned aerial vehicles, and cars.