Clutter Statistics Research Articles

Vehicle Occupancy Detector Based on FMCW mm-Wave Radar at 77 GHz

Millimeter-wave (mm-Wave) radars are a solution in automotive applications, such as advanced driver assistance system (ADASs). This work explores the use of commercially available mm-Wave radar technology for in-cab vehicle applications. A framework for vehicle occupancy detectors based on mm-Wave radar at 77 GHz is presented. A multiple-input–multiple-output (MIMO) frequency-modulated continuous-wave (FMCW) radar from Texas Instruments (AWR1642) is used. The system can detect the presence of people occupying the seats by measuring small movements of the body. To this end, the static clutter is removed; therefore, the point cloud returned by the radar contains information about the body. The radar is installed on the roof of the vehicle to have the maximum field of view of the seats. An algorithm that uses both the density and the dispersion of points around each seat is proposed to determine its degree of occupation. Experimental results have been presented for four- and five-seater vehicles with high accuracy.

Accurate sensing of multiple humans buried under rubble using IR-UWB SISO radar during search and rescue

Upon the collapse of man-made structures like buildings, bridges, etc., the search and rescue personnel must locate and rescue the buried people. Considering the radar’s beam width, victims may be buried under rubble at the same longitudinal range from the radar and fall into blind spots due to missed detections. Here, we will address this problem efficiently. Experimental work has been undertaken by placing subjects under randomized rubble with an impulse-radio ultra-wideband (IR-UWB) radar placed above the debris. Pre-processing the raw data removes stationary and non-stationary clutter, while the novel variance threshold method proposed herein identified the potential targets. The inter-target dynamic range (ITDR) concept is presented to extract the exact number of targets per range bin by considering the difference in the power level of consecutive dominant target breathing frequencies. Consequently, variable mode decomposition (VMD) is used to extract the breathing signal. An average correlation coefficient of 87.9 % was achieved between the target vital signs from the radar and the respiration belt. Finally, a range compensation algorithm is developed to increase detection efficiency to an average of 7.23 % range error. Thus, multiple targets are successfully extracted with reasonably accurate range and vital signs under varying target postures.

Airborne Radar Anti-Jamming Waveform Design Based on Deep Reinforcement Learning.

Airborne radars are susceptible to a large number of clutter, noise and variable jamming signals in the real environment, especially when faced with active main lobe jamming, as the waveform shortcut technology in the traditional regime can no longer meet the actual battlefield radar anti-jamming requirements. Therefore, it is necessary to study anti-main-lobe jamming techniques for airborne radars in complex environments to improve their battlefield survivability. In this paper, we propose an airborne radar waveform design method based on a deep reinforcement learning (DRL) algorithm under clutter and jamming conditions, after previous research on reinforcement-learning (RL)-based airborne radar anti-jamming waveform design methods that have improved the anti-jamming performance of airborne radars. The method uses a Markov decision process (MDP) to describe the complex operating environment of airborne radars, calculates the value of the radar anti-jamming waveform strategy under various jamming states using deep neural networks and designs the optimal anti-jamming waveform strategy for airborne radars based on the duelling double deep Q network (D3QN) algorithm. In addition, the method uses an iterative transformation method (ITM) to generate the time domain signals of the optimal waveform strategy. Simulation results show that the airborne radar waveform designed based on the deep reinforcement learning algorithm proposed in this paper improves the signal-to-jamming plus noise ratio (SJNR) by 2.08 dB and 3.03 dB, and target detection probability by 26.79% and 44.25%, respectively, compared with the waveform designed based on the reinforcement learning algorithm and the conventional linear frequency modulation (LFM) signal at a radar transmit power of 5 W. The airborne radar waveform design method proposed in this paper helps airborne radars to enhance anti-jamming performance in complex environments while further improving target detection performance.

Performance Evaluation of a UWB Positioning System Applied to Static and Mobile Use Cases in Industrial Scenarios

Indoor positioning systems are essential in the industrial domain for optimized production and safe operation of mobile elements, such as mobile robots, especially in the presence of static machinery and human operators. In this paper, we assess the performance of a commercial UWB radio-based positioning system deployed in a realistic industrial scenario, considering both static and mobile use cases. Our goal is to characterize the accuracy of this system in the context of industrial use cases and applications. For the static case, an extensive analysis was presented based on measurements performed at 72 measurement positions at 3 different heights (above, at similar a level to, and below the average clutter level) in different industrial clutter conditions (open and cluttered spaces). The extensive analysis in the mobile case considered several runs of a route covered by an autonomous mobile robot equipped with multiple tags in different positions. The results indicate that a similar degree of accuracy with a median 2D positioning error smaller than 20 cm is possible in both static and mobile conditions with an optimized anchor deployment. The paper provides a complete statistical characterization of the system’s accuracy and addresses the multiple deployment trade-offs and system dynamics observed for the different configurations.

Real-Time Non-Contact Millimeter Wave Radar-Based Vital Sign Detection

In this paper, the extraction of the life activity spectrum based on the millimeter (mm) wave radar is designed to realize the detection of target objects and the threshold trigger module. The maximum likelihood estimation method is selected to complete the design of the average early warning probability trigger function. The threshold trigger module is designed for the echo signal of static objects in the echo signal. It will interfere with the extraction of Doppler frequency shift results. The moving target detection method is selected, and the filter is designed. The static clutter interference is filtered without affecting the phase difference between the detection sequences, and the highlight target signal is improved. The frequency and displacement of thoracic movement are used as the detection data. Through the Fourier transform calculation of the sequence, the spectrum value is extracted within the estimated range of the heartbeat and respiration spectrum, and the heartbeat and respiration signals are picked up. The proposed design uses Modelsim and Quartus for CO-simulation to complete the simulation verification of the function, extract the number of logical units occupied by computing resources, and verify the algorithm with the vital signs experiment. The heartbeat and respiration were detected using the sports bracelet; the relative errors of heartbeat detection were 0–6.3%, the respiration detection was 0–9.5%, and the relative errors of heartbeat detection were overwhelmingly less than 5%.

Non-Contact Detection of Vital Signs Based on Improved Adaptive EEMD Algorithm (July 2022).

Non-contact vital sign detection technology has brought a more comfortable experience to the detection process of human respiratory and heartbeat signals. Ensemble empirical mode decomposition (EEMD) is a noise-assisted adaptive data analysis method which can be used to decompose the echo data of frequency modulated continuous wave (FMCW) radar and extract the heartbeat and respiratory signals. The key of EEMD is to add Gaussian white noise into the signal to overcome the mode aliasing problem caused by original empirical mode decomposition (EMD). Based on the characteristics of clutter and noise distribution in public places, this paper proposed a static clutter filtering method for eliminating ambient clutter and an improved EEMD method based on stable alpha noise distribution. The symmetrical alpha stable distribution is used to replace Gaussian distribution, and the improved EEMD is used for the separation of respiratory and heartbeat signals. The experimental results show that the static clutter filtering technology can effectively filter the surrounding static clutter and highlight the periodic moving targets. Within the detection range of 0.5 m~2.5 m, the improved EEMD method can better distinguish the heartbeat, respiration, and their harmonics, and accurately estimate the heart rate.

A Novel Intelligent Ground Moving Target Indication Using Meta-Heuristic-Based Simplified Fractional Fourier Transform

One of the most significant subfields in “Synthetic Aperture Radar (SAR)” research is considered to be target detection. Numerous studies have been conducted on target identification, with the majority of them favoring filter-oriented methods. The fundamental goal of radar systems is to “detect moving targets on the ground.” Decomposing a complex matrix into a structured sparse matrix and a low-rank matrix is a fundamental mathematics issue. Surveillance and reconnaissance rely heavily on “Ground Moving Target Indication (GMTI),” but it's not a simple task. The SAR ATI was first developed for calculating the radial velocity of ground-moving objects. Yet, overlapping stationary clutter can corrupt the recorded differential phase, resulting in mistakes in position and velocity calculations. The main concept of this paper is to propose a novel “Adaptive Simplified Fractional Fourier Transform (A-SFrFT)” using the intelligent meta-heuristic improvement. This adaptive SFrFT efficiently estimates the “Doppler parameters of the moving targets.” The improved “Harris Hawks Optimization (HHO)” termed Trio Updating HHO (TU-HHO) is used as the meta-heuristic algorithm that enhances the performance of the SFrFT-based target estimation. The mathematical analysis and simulation findings show that the suggested methods recommended strategy is successful.

Ground Moving Target Detection and Trajectory Reconstruction Methods for Multichannel Airborne Circular SAR

Synthetic aperture radar ground moving target indication (SAR-GMTI) is an attractive mode for radar system to obtain high-resolution regional images and detection of moving targets simultaneously. Moreover, the 360° observation capability of circular SAR (CSAR) makes it possible for moving target trajectory reconstruction. In order to address the problem that slow-moving targets, which are buried in strong stationary ground clutter, are difficult to be detected, a framework of multichannel SAR-GMTI is presented in this article. For ground moving target detection in the presence of stationary clutter, the proposed method first adopts clutter suppression interferometry and relaxation-based cyclic algorithm to suppress clutterand retrieve parameters (e.g., radial velocity and Doppler information). Then, to further reduce the false alarm rate, multiple moving target tracking algorithm is performed in range–Doppler domain. Finally, the moving target trajectory is reconstructed by using the proposed two-stage parameter estimation method based on Doppler characteristic and CSAR geometry, and the mathematical analysis of this method is performed. The proposed method is robust and insensitive to the signal-to-noise ratio. It does not rely on priori road information, so it has wide applicability, especially in military application. In addition, the framework is performed in echo domain and independent of imaging processing. The experimental results on simulated data are presented to evaluate the estimation accuracy of the proposed method, and the real data processing results are provided to demonstrate the validity and feasibility of the proposed method.

Remote Drowsiness Detection Based on the mmWave FMCW Radar

Drowsiness can lead to inefficiency and major disasters; thus it is important to address it in both academia and industry. Despite multiple types of research in this field, a nonintrusive classifier system for detecting drowsiness in real-time under a natural environment without specific stimulation is lacking. This study develops a real-time drowsiness detection system using a 77 GHz millimeter-wave (mmWave) frequency-modulated continuous wave radar. Specifically, firstly, a real-time mmWave processing module is proposed, which can adaptively suppress both stationary and non-stationary clutters. Secondly, a feature extraction module based on a hybrid of handcrafted and machine learning (ML) features is propsoed to obtain a holistic view of mmWave-based vital signals, in which ML features represent linear and temporal changes. Thirdly, a drowsiness classification model is proposed based on feature fusion and extreme gradient boosting algorithms, thus classifying a user’s state into two categories: non-drowsy and drowsy. To confirm the proposed system’s performance, it is validated on a self-collected dataset (n = 28). The experimental results show the following (i) the accuracy of heart rate evaluation is 96.4%, and (ii) based on 10-fold cross-validation, the proposed system gains a detection accuracy of 82.9% and outperforms the state-of-the-art approaches.

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.

Using Virtual Reality to Systematically Examine Impacts of Noise and Visual Clutter on Message Elaboration and Cognitive Capacity.

This study used virtual reality to examine how environmental attributes interact with health communication to influence psychiatric help-seeking behavior, using the example of a subway station. We used a 2 × 2 factorial design crossing two noise conditions (high noise [75 dB] or low noise [30 dB]) and two visual clutter conditions (low clutter [a tidy trash can and orderly construction materials] or high clutter [scattered trash and construction materials]). We found that participants in the high (vs. low) visual clutter condition reported lower cognitive capacity levels, and there was a significant correlation between cognitive capacity and message elaboration. However, we found no effects of noise conditions. Serving as a proof-of-concept study to investigate the contexts in which environmental stressors may influence information processing, this study contributes to the field of health communication environmental design research. Clinical Trial Registration: https://osf.io/rsa48.

MIMO Monopulse Radar for Detecting Human Targets With I/Q Curve-Length Estimations

This letter proposes a method to detect the angular locations of human targets using a multiple-input–multiple-output (MIMO) monopulse radar system. Sum and difference beams used for monopulse processing are simply implemented by digital beamforming of a virtual array generated by collocated MIMO radar. Human targets are readily distinguished from stationary clutter based on the phase variations in their signals, which are detected by estimating the curve lengths (CLs) of the I/Q trajectories. A monopulse processing technique integrated with the CL method is thus proposed. This system resolves two human targets, of which the angular separation is less than the angular resolution of the conventional radar. The system is verified through both simulations and experiments in an indoor environment.

Robust Joint Design of Transmit Waveform and Receive Filter for MIMO-STAP Radar Under Target and Clutter Uncertainties

This paper deals with the joint design problem of transmit waveform and receive filter for the robust detection of ground moving target with multiple-input multiple-output Space time adaptive processing (MIMO- STAP) radar in the presence of target and clutter uncertainties. With the prior knowledge of target and clutter statistics, the averaged signal-to-interference-plus-noise ratio (SINR) is formulated as a figure of merit to maximize. The robust joint design problems for the continuous and discrete phase cases, respectively, are formulated with the constant-modulus and similarity constraints. Then, an efficient iterative algorithm is developed for improving the SINR. Specifically, each iteration of the proposed algorithm involves the generalized eigenvalue decomposition to optimize the receive filter, while a nested iterative procedure involving Dinkelbach’s framework and alternating direction penalty method (ADPM) algorithm to design transmit waveform. The proposed algorithm can achieve higher SINR with reduced computational load compared to the recently developed ones. Numerical examples demonstrate the effectiveness of the proposed method for the robust detection of moving target in MIMO-STAP radar.

Mitigation of Leakage and Stationary Clutters in Short-Range FMCW Radar With Hybrid Analog and Digital Compensation Technique

In short-range sensing, the frequency-modulated continuous-wave (FMCW) radar is subject to the leakage between the transmitter (Tx) and the receiver (Rx) and the stationary clutter reflections, which are difficult to deal with due to the proximity to the target. The proximity ghost image may not only interfere with the target but also pose limitations on the radar performance, such as the degradation of the signal-to-noise ratio (SNR). This article proposes a novel hybrid analog and digital compensation techniques, which mitigates the leakage and the stationary clutters by compensating with the prestored anti-interference signals in both analog and digital domains at the intermediate-frequency (IF) stage. A novel modulation signal-based synchronization (MBS) technique is developed to align the real-time IF signal with the anti-interference signal during the compensation process. By significantly suppressing both the Tx-Rx leakage and the stationary clutters, the proposed technique greatly improves the signal-to-interference ratio (SIR) of the IF beat signals and makes possible the detection of targets with weak reflections. Moreover, the analog compensation enables the full use of the dynamic range of the analog-to-digital converter (ADC), which will reduce the impact of quantization noise and, thus, improve the SNR. Experiments have been carried out to validate the performance of the proposed technique in leakage compensation, weak target detection, stationary clutter compensation, and gesture sensing. The experimental results show that the SIR and the SNR have been improved by around 38 and 10 dB, respectively.

Land Clutter Statistics From an Airborne Passive Bistatic Radar

Passive operation is an important operational concept for future airborne radar platforms. Successful target detection requires an understanding of the underlying clutter statistics in order to set the detection threshold accurately. In addition, clutter statistics are important for modeling of passive radar performance and simulation of passive bistatic radar to aid in the development of new target detection algorithms. In this article, the clutter statistics of data from an airborne platform are studied over a range of bistatic geometries and receive polarizations. The illuminator is a digital video broadcast-terrestrial (DVB-T) station and the collected data spans both ground and maritime regions. The key contributions include a study of amplitude statistics using a number of common distributions and understanding the impact of the reference signal quality in terms of the clutter statistics.

A Stationary Clutter Suppression Method for 3-D Micromotion Measurements Based on Wideband Radar Amplitude and Phase Information

The micromotion features of a target contain unique structural information and motion information about the target, which can be used as an important basis for target classification and recognition. To achieve high-accuracy three-dimensional micromotion measurements in the clutter environment, a stationary clutter suppression method for three-dimensional micromotion measurements based on wideband radar amplitude and phase information is proposed in this paper. This method is capable of accurately measuring the trajectory of small amplitude micromotion with the high-accuracy phase-derived angle measurement (PDAM) and phase-derived range measurement (PDRM). However, the measurement accuracy of three-dimensional micromotion deteriorates sharply when clutter exists. Thus, the main challenge that we overcome is achieving high-accuracy clutter estimation based on wideband radar measurements. The clutter suppression method is proposed by utilizing wideband radar amplitude and phase of multiple range cells jointly. Finally, simulation and experiment are presented to validate the feasibility and effectiveness of the proposed method under stationary clutter conditions.

Single-Channel Circular SAR Ground Moving Target Detection Based on LRSD and Adaptive Threshold Detector

Due to the advantages of long-time and multi-angle observation, ground moving target detection with circular synthetic aperture radar (SAR) has recently attracted lots of interest from researchers. Our team has previously proposed the logarithm background subtraction algorithm for moving target detection in single-channel circular SAR. Its principle is that the background image (static clutter) is obtained by median filtering of the image sequence, and the foreground image (moving target) is obtained by subtraction. To further improve the performance of background and foreground separation, we introduce the low-rank sparse decomposition (LRSD) method into the previous framework. A new algorithm based on LRSD and adaptive threshold detector (ATD) is proposed in this letter. First, this letter introduces entropy metric to optimize parameters in LRSD to obtain better background and foreground separation. Second, since the statistical distribution of the foreground image is unknown, the constant false alarm rate (CFAR) detector cannot be applied to the foreground image. Therefore, an adaptive threshold detector (ATD) built on Otsu is presented in this letter, which is independent of image statistical properties. The final detection result is obtained via clustering using the modified density-based spatial clustering of applications with noise (DBSCAN) method. The effectiveness of the proposed algorithm is verified by the experimental results on the airborne X-band Gotcha Volumetric SAR Data.

Spatial Coherence Adaptive Clutter Filtering in Color Flow Imaging-Part I: Simulation Studies.

The appropriate selection of a clutter filter is critical for ensuring the accuracy of velocity estimates in ultrasound color flow imaging. Given the complex spatio-temporal dynamics of flow signal and clutter, however, the manual selection of filters can be a significant challenge, increasing the risk for bias and variance introduced by the removal of flow signal and/or poor clutter suppression. We propose a novel framework to adaptively select clutter filter settings based on color flow image quality feedback derived from the spatial coherence of ultrasonic backscatter. This framework seeks to relax assumptions of clutter magnitude and velocity that are traditionally required in existing adaptive filtering methods to generalize clutter filtering to a wider range of clinically-relevant color flow imaging conditions. In this study, the relationship between color flow velocity estimation error and the spatial coherence of clutter filtered channel signals was investigated in Field II simulations for a wide range of flow and clutter conditions. This relationship was leveraged in a basic implementation of coherence-adaptive clutter filtering (CACF) designed to dynamically adapt clutter filters at each imaging pixel and frame based on local measurements of spatial coherence. In simulation studies with known scatterer and clutter motion, CACF was demonstrated to reduce velocity estimation bias while maintaining variance on par with conventional filtering.

Statistical Comparison of Melting Iceberg Backscatter Embedded in Sea Ice and Open Water Using RADARSAT-2 Images of the Newfoundland Sea

This article investigates and compares polarimetric signatures of icebergs embedded in sea ice and icebergs in open water. The main objective is to study on the backscatter properties of melting iceberg and to check on whether there is any distinguishable property in them in the case of different background clutter conditions (i.e. sea ice and open water). This study results will improve the potential of iceberg detection using radar polarimetry. RADARSAT-2 images have been used for the analysis acquired over locations near the coastline (approximately 3–35 km) of the island of Newfoundland. For analysis, polarimetry parameters, such as co-(HH) and cross-(HV) polarization and several popular decomposition techniques, specifically Pauli, Freeman–Durden, Yamaguchi, Cloud–Pottier, and van Zyl, have been used to determine the polarimetric signatures of icebergs and sea ice. The statistical hypothesis T-test has been applied to achieve a precise comparison among backscatters from different icebergs groups. Statistical results tend to show a dominant surface scattering mechanism for icebergs in all types of clutter conditions. Moreover, icebergs in open water produce larger volume scatter than icebergs in sea ice, whereas icebergs in sea ice produce larger surface scatter than icebergs in open water.

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.