Clutter Signal Research Articles

Mechanism and Characteristic of Nonlinear Clutter in Harmonic Radar With Frequency-Modulated Waveform

Harmonic radar is a special radar system that has been widely used in insect tracking, search and rescue, and electronic equipment detection in recent years due to its excellent anti-clutter capability. However, in complex close-range scenarios, harmonic radar still suffers from nonlinear clutter interferences, which will cause a large number of clutter false alarms and seriously affect the performance of target detection. This paper analyzes the mechanism and the characteristic of nonlinear clutter through experimental results in complex close-range scenarios. It is found that the nonlinear clutter is generated by the harmonics and intermodulation components of the multipath signals. More importantly, it is the first time for the multipath intermodulation phenomenon to be revealed in harmonic radar with frequency-modulated waveform. A nonlinear clutter signal model is developed to explain this special phenomenon in this paper. The proposed theory is verified by the simulation and measurement results. The work in this paper lays a theoretical foundation for the subsequent work on nonlinear clutter suppression.

Efficient Clutter Cancellation Algorithm Based on a Suppressed Clutter Map for FMCW Radar Systems

This paper proposes an efficient clutter cancellation algorithm based on suppressed clutter map for frequency-modulated continuous wave radar systems. In conventional clutter cancellation algorithms based on clutter maps, range-Doppler maps are employed. However, if the radar cross section (RCS) of the targets is significantly lower than the RCS of the clutter, a problem may occur in that the targets may also be removed in the clutter-removal process. To solve this problem, the proposed algorithm creates a range-Doppler map to which the moving target indicator (MTI) algorithm is applied instead of the general range-Doppler map that does not include the MTI algorithm. The range-Doppler map, used after applying the clutter suppression filter, significantly reduces the clutter removal effect on targets in which RCS is relatively weak by suppressing the effect of relatively dominant clutter signals. The experimental results showed that the proposed algorithm detects weak targets that remain undetected by the existing map-based clutter-removal algorithm.

A Multi-Target Localization and Vital Sign Detection Method Using Ultra-Wide Band Radar.

Life detection technology using ultra-wideband (UWB) radar is a non-contact, active detection technology, which can be used to search for survivors in disaster rescues. The existing multi-target detection method based on UWB radar echo signals has low accuracy and has difficulty extracting breathing and heartbeat information at the same time. Therefore, this paper proposes a new multi-target localization and vital sign detection method using ultra-wide band radar. A target recognition and localization method based on permutation entropy (PE) and K means++ clustering is proposed to determine the number and position of targets in the environment. An adaptive denoising method for vital sign extraction based on ensemble empirical mode decomposition (EEMD) and wavelet analysis (WA) is proposed to reconstruct the breathing and heartbeat signals of human targets. A heartbeat frequency extraction method based on particle swarm optimization (PSO) and stochastic resonance (SR) is proposed to detect the heartbeat frequency of human targets. Experimental results show that the PE-K means++ method can successfully recognize and locate multiple human targets in the environment, and its average relative error is 1.83%. Using the EEMD-WA method can effectively filter the clutter signal, and the average relative error of the reconstructed respiratory signal frequency is 4.27%. The average relative error of heartbeat frequency detected by the PSO-SR method was 6.23%. The multi-target localization and vital sign detection method proposed in this paper can effectively recognize all human targets in the multi-target scene and provide their accurate location and vital signs information. This provides a theoretical basis for the technical system of emergency rescue and technical support for post-disaster rescue.

Performance comparison of compact phased arrays and traditional seismic networks for microseismic monitoring at a CO2 sequestration test site

As carbon capture, utilization, and sequestration scales toward the gigatonnes level, the need for underground reservoir surveillance is driving efforts in advancing technologies for cost-effective passive seismic monitoring. Quantum Technology Sciences, in cooperation with Carbon Management Canada's Containment and Monitoring Institute (CaMI), installed a network of four permanent compact volumetric phased arrays (seismic and acoustic detection and ranging [SADAR] system) at CaMI's Field Research Station (FRS) to demonstrate the results that can be achieved through passive monitoring of microseismicity using this technology. Configured as a sparse network, the SADAR arrays provide passive, persistent, and permanent data acquisition and analysis for monitoring microseismicity in the earth volume of interest. Data from the phased arrays are processed to take advantage of the spatial coherence of the incident seismic signals to increase signal resolution while suppressing noise and clutter signals and providing signal attributes such as angle of incidence and phase velocity. The CaMI FRS has a network of 28 permanent surface stations that are deployed in an x-shaped geometry centered on the injection well. It has a downhole array of 24 geophones that are permanently deployed in an observation well. This provides a ready and unique opportunity to evaluate the detection and location performance of the different systems for passive seismic monitoring. We analyze observations of five example events selected from the microseismicity detected by the SADAR arrays with moment magnitudes (Mw) down to approximately −2. Signal-to-noise ratio (S/N) and location uncertainties are compared for the events acquired using SADAR arrays versus the surface sensors. The results demonstrate improved performance of networked SADAR arrays compared to traditional surface sensor deployment for detecting and locating microseismicity. Specifically, the results show that coherent processing of SADAR arrays achieves S/N gains up to about 20 dB and location errors down to 10 m.

All-fiber orthogonal-polarized white-noise-modulated laser for short-coherence dynamic interferometry.

An all-fiber orthogonal-polarized white-noise-modulated laser (AOWL) for short-coherence dynamic interferometry is proposed. Short-coherence laser is achieved by current modulating of a laser diode with the band-limited white noise. A pair of orthogonal-polarized lights with adjustable delay for short-coherence dynamic interferometry are output by the all-fiber structure. In the non-common-path interferometry, the AOWL can significantly suppress the interference signal clutter with 73% side lobe suppression ratio, that improves the positioning accuracy of zero optical path difference. The wavefront aberrations of a parallel plate are measured with the AOWL in the common-path dynamic interferometers, avoiding the fringe crosstalk.

Research on Beidou Navigation Interference Monitoring Technology Based on CFAR

Satellite monitoring stations are often affected by strong clutter and interference when monitoring targets. When ground receivers receive target signals, they also receive noise, clutter, and interference signals. These signals have randomness and signal strength changes from time to time. Therefore, constant false alarm detection technology is used to detect interference signals. CFAR detectors construct different system functions in different clutter environments and maintain a constant false alarm rate by adaptively modifying the detection threshold. A comparison of CFAR algorithms shows that CA_ The CFAR algorithm has excellent monitoring performance in uniform environments_ CFAR is suitable for multi target environments, GO_ CFAR performs well in clutter edge environments, SO_ CFAR performs well under various conditions.

Effect of Clutter Filter in High-Frame-Rate Ultrasonic Backscatter Coefficient Analysis.

High-frame-rate imaging with a clutter filter can clearly visualize blood flow signals and provide more efficient discrimination with tissue signals. In vitro studies using clutter-less phantom and high-frequency ultrasound suggested a possibility of evaluating the red blood cell (RBC) aggregation by analyzing the frequency dependence of the backscatter coefficient (BSC). However, in in vivo applications, clutter filtering is required to visualize echoes from the RBC. This study initially evaluated the effect of the clutter filter for ultrasonic BSC analysis for in vitro and preliminary in vivo data to characterize hemorheology. Coherently compounded plane wave imaging at a frame rate of 2 kHz was carried out in high-frame-rate imaging. Two samples of RBCs suspended by saline and autologous plasma for in vitro data were circulated in two types of flow phantoms without or with clutter signals. The singular value decomposition was applied to suppress the clutter signal in the flow phantom. The BSC was calculated using the reference phantom method, and it was parametrized by spectral slope and mid-band fit (MBF) between 4-12 MHz. The velocity distribution was estimated by the block matching method, and the shear rate was estimated by the least squares approximation of the slope near the wall. Consequently, the spectral slope of the saline sample was always around four (Rayleigh scattering), independently of the shear rate, because the RBCs did not aggregate in the solution. Conversely, the spectral slope of the plasma sample was lower than four at low shear rates but approached four by increasing the shear rate, because the aggregations were presumably dissolved by the high shear rate. Moreover, the MBF of the plasma sample decreased from -36 to -49 dB in both flow phantoms with increasing shear rates, from approximately 10 to 100 s-1. The variation in the spectral slope and MBF in the saline sample was comparable to the results of in vivo cases in healthy human jugular veins when the tissue and blood flow signals could be separated.

지면 레이다 클러터 신호의 특성 및 생성 방법

Clutter signals are undesired for radar detection. Clutter signals can be categorized as volume, land, and sea clutter. These unwanted signals disturb radar by detecting the target. To increase detection accuracy, the radar must be able to differentiate clutter signals from the target and remove cluttering signals. Therefore, it is important to understand the characteristics of clutter signals. In this paper, we review the characteristics of land clutter signals and the methods used to generate them.

Adaptive noise reduction for power Doppler imaging using SVD filtering in the channel domain and coherence weighting of pixels

Objective. Ultrafast power Doppler (UPD) is an ultrasound method that can image blood flow at several thousands of frames per second. In particular, the high number of data provided by UPD enables the use of singular value decomposition (SVD) as a clutter filter for suppressing tissue signal. Notably, is has been demonstrated in various applications that SVD filtering increases significantly the sensitivity of UPD to microvascular flows. However, UPD is subjected to significant depth-dependent electronic noise and an optimal denoising approach is still being sought. Approach. In this study, we propose a new denoising method for UPD imaging: the Coherence Factor Mask (CFM). This filter is first based on filtering the ultrasound time-delayed data using SVD in the channel domain to remove clutter signal. Then, a spatiotemporal coherence mask that exploits coherence information between channels for identifying noisy pixels is computed. The mask is finally applied to beamformed images to decrease electronic noise before forming the power Doppler image. We describe theoretically how to filter channel data using a single SVD. Then, we evaluate the efficiency of the CFM filter for denoising in vitro and in vivo images and compare its performances with standard UPD and with three existing denoising approaches. Main results. The CFM filter gives gains in signal-to-noise ratio and contrast-to-noise ratio of up to 22 dB and 20 dB, respectively, compared to standard UPD and globally outperforms existing methods for reducing electronic noise. Furthermore, the CFM filter has the advantage over existing approaches of being adaptive and highly efficient while not requiring a cut-off for discriminating noise and blood signals nor for determining an optimal coherence lag. Significance. The CFM filter has the potential to help establish UPD as a powerful modality for imaging microvascular flows.

Dim Moving Point Target Detection in Cloud Clutter Scenes Based on Temporal Profile Learning

This letter presents an approach for detecting dim moving point targets in cloud clutter scenes based on temporal profile learning. The main idea is that a weak transient disturbance will appear in the temporal profiles of target-present pixels, changing the temporal profile’s characteristics. We propose a novel signal-to-signal network to learn the temporal characteristics of the background and the clutter, in which the transient disturbance is extracted by the residual between the input signal and the reconstructed background and clutter signal. The structure of the ConvBlock-1D is designed to enhance the flow and propagation of features in layers. A loss function is proposed to solve the imbalance problem. We provided a comparison to other widely used methods by using simulated datasets and real-world image sequences. The experimental results demonstrate that our method has the best performance in terms of the qualitative and quantitative assessments.

Multi-Person Position Estimation Based on Correlation Between Received Signals Using MIMO FMCW Radar

By using Multiple-Input Multiple-Output (MIMO) Frequency Modulated Continuous Wave (FMCW) radar, a range-angle map can be obtained that displays the signal strength in two dimensions with respect to distance and angular directions. In this process, a threshold algorithm such as Constant False Alarm Rate (CFAR) is used to detect positions where the signal strength exceeds a certain threshold value, enabling position estimation of targets. Furthermore, a method has been proposed to use the curve length of the trajectory of the I/Q signal to emphasize the signal of the person’s position on the range angle map. However, the conventional CFAR may cause false alarms when noise and clutter signals are strong. In this paper, we propose a new method for human location estimation using the MIMO FMCW radar. Comparing the reflected signals from people and clutter, we see that I/Q signal variations associated with human motion have little correlation with clutter from stationary objects or noise, and I/Q signal variations associated with clutter often have high correlation with signals at other positions. We focus on the fact that there are many positions where the clutter component signal has a high correlation, compared to the signal caused by a person. Using the correlation map that expresses the correlation between the received I/Q signals on the range-angle plane, we evaluated the characteristics of noise and clutter components from walls on the correlation map by standard deviation. As a result, we can remove strong noise and clutter components caused from near walls, thus improving the accuracy of object location estimation.

Weak Sinusoidal Signal Detection with CSI Model in Chaotic Interference

In small target detection under strong sea clutter or impact signal detection under machinery fault diagnosis, a weak sinusoidal signal with random amplitude is often contaminated by heavier chaotic noise, and the target information is difficult to detect. Traditional solutions, such as neural networks or stochastic resonance, can not effectively extract heteroscedasticity of data, which leads to weak signals not being detected. To overcome these limitations and improve the detection efficiency, an empirical likelihood ratio statistical method for detecting weak sinusoidal signals with random amplitude under strong chaotic interference is proposed. First, based on the reconstruction in the phase space of the 1‐D observed time series signal with embedding dimension and time delay, the presented method obtains a multivariate special temporal series as an input. Subsequently, the chaotic single index (CSI) statistical model is established for single‐step prediction, and it can be estimated by the nonparametric locally linear algorithm for minimizing the mean squares error. Finally, the empirical likelihood ratio statistical method is applied to detect weak sinusoidal signals with random amplitude. Simulated data and real data experiment results show that the proposed CSI model can better capture the weak target signal and detect effectively weak target signal under the chaotic interference.

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.

High-order singular value decomposition of contrast pulsing sequences for improved clutter suppression

Contrast-enhanced ultrasound is a diagnostic tool used to visualize blood flow in the cardiovascular system. The use of ultrasound contrast agent (microbubbles) in combination with contrast pulsing scheme (CPS) improves the sensitivity and specificity of ultrasound flow imaging by enhancing the signal in the blood compartment. The commonly used CPS are pulse inversion (PI), amplitude modulation (AM), and amplitude-modulated pulse inversion (AMPI). Using differences in phase or amplitude of multiple pulses, the linear tissue clutter signal can be suppressed. However, this process can be degraded by motion or non-linear propagation of the ultrasound wave. These effects cause the cancellation of linear clutter signal to be ineffective We propose using higher-order singular value decomposition (HOSVD) with spatial, temporal, and pulsing dimensions as the input to improve clutter suppression under the conditions of motion and non-linear propagation. We performed systematic in-vitro- experiment emulating these conditions, as well as in-vivo cardiac measurements. The results showed that HOSVD increases the clutter suppression of all the 3 CPS compared to the conventional linear processing. The improvement of clutter reduction could be beneficial to various cardiac evaluation like myocardial perfusion or intra ventricular flow assessment.

Adaptive detection based on gradient test and Durbin test in spectrally symmetric interference

In this paper, we consider the problem of detecting a signal in ground clutter dominated environments with a symmetrically structured power spectral density. We take advantage of interference’s spectrum symmetry to develop decision schemes. The gradient test, Durbin test and their two-step (2S) variants are deduced. The 2S-gradient test and the 2S-Durbin test coincide with the 2S generalized likelihood ratio test, and the Durbin test coincide with the Rao test for the same detection problem, while the gradient test is a new detector. Numerical results, based on simulated and real data, show that the gradient test can perform better than the existing ones in sample-starved scenarios, and the detectors exploiting the interference spectrum symmetry outperform traditional detectors without exploiting the interference spectral symmetry.

Research on Ultra-Wideband Radar Echo Signal Processing Method Based on P-Order Extraction and VMD

As a new method to detect vital signs, Ultra-wideband (UWB) radar could continuously monitor human respiratory signs without contact. Aimed at addressing the problem of large interference and weak acquisition signal in radar echo signals from complex scenes, this paper adopts a UWB radar echo signal processing method that combines strong physical sign information extraction at P time and Variational Mode Decomposition (VMD) to carry out theoretical derivation. Using this novel processing scheme, respiration and heartbeat signals can be quickly reconstructed according to the selection of the appropriate intrinsic mode functions (IMFs), and the real-time detection accuracy of human respiratory signs is greatly improved. Based on an experimental platform, the data collected by the UWB radar module were first verified against the measured values obtained at the actual scene. The results of a validation test proved that our UWB radar echo signal processing method effectively eliminated the respiratory clutter signal and realized the accurate measurement of respiratory and heartbeat signals, which would prove the existence of life and further improve the quality of respiration and heartbeat signal and the robustness of detection.

Development of FMCW Radar Signal Processing for High-Speed Railway Collision Avoidance

Collision is the main issue in safe transportation, including in the railway system. Sensor systems have been developed to detect obstacles to prevent a collision, such as using cameras. One disadvantage of the camera systems is that performance detection decreases in a not clean environment, like the target position behind the fogs. This paper discusses the development of frequency modulated continuous wave (FMCW) radar signal processing for high-speed railway collision avoidance. The development of radar signal processing combines a two-dimensional constant false alarm rate (2D-CFAR) and robust principal component analysis (RPCA) to detect moving targets under clutter. Cell average (CA) and Greatest of CA (GOCA) CFAR are evaluated under a cluttered wall environment along the railway track. From the experiment, the development of FMCW radar can detect stationary or moving obstacles around 675 meters in front of the locomotive. Combining 2D-CFAR and RPCA algorithm outperforms average background subtraction in extracting moving targets from strong clutter signals along the railway track.

Accurate Clutter Synthesis for Heterogeneous Textures and Dynamic Radar Environments

This article proposes an accurate clutter synthesis method for a dynamic radar under various environmental conditions. Previous clutter synthesis methods mostly rely on empirical clutter models fitting the measurement results obtained from the limited conditions of observations. Inherently, these models have difficulties synthesizing clutter for unexplored environments and operational conditions. The method presented in this article overcomes this limitation by creating and summing the clutter patch by patch. The clutters of individual patches are synthesized by the clutter model corresponding to each patch’s specific conditions, such as the grazing angle, ground texture, and wind velocity. It enables one to synthesize clutter signals under various observation scenarios accurately. For example, this work demonstrates the synthesis of clutter signals from heterogeneous textures for an aircraft radar on a mission. Also, the clutter signals for a monopulse radar are synthesized and included in the radar simulation to evaluate the performance of a detection algorithm. Potentially, this method can be utilized to design a robust radar algorithm against the clutter environment of interests. For a given condition of measurement, it is shown that the synthesized clutters agree well with the actual measured data in their statistical characteristics. The program is freely available through the open code.

기상 클러터 효과를 고려한 표면 클러터 반사신호 모노펄스 레이다 공대지 신호 생성

An airborne radar echo signal includes signals returned from clutter and a target; thus, simulating the signal from the clutter may be crucial for predicting and analyzing accurate radar performance. Because it can be extremely difficult and expensive to obtain real measured signal data for various scenarios, an accurate signal generation method is required. Existing weather clutter and ground clutter simulators for an X-band airborne radar are combined to simulate the echo signal when two clutters exist simultaneously. The weather clutter effect becomes more significant with increasing frequencies, such as in the X-band. The proposed simulator can generate time-domain signals by considering the random characteristics of the clutter signal and the Doppler effect caused by the movement of aircraft and clutter. In addition, a simple scheme is proposed for generating signals from artificial structures. Finally, the finite bandwidth of the transmitted chirp signal is calculated to improve the accuracy of the generated signal.

Multichannel Clutter Modeling, Analysis, and Suppression for Missile-Borne Radar Systems

When a missile-borne radar system works in downward-looking surveillance mode, the broadened ground clutter signal in virtue of platform high-speed motion will be received by the radar receiver, which will cause difficulty in moving target detection and attacking. Unlike airborne and spaceborne platforms, a missile-borne platform exhibits some unique motion characteristics, such as diving, spinning, and coning, causing the clutter space–time distribution property significantly different from those of airborne and spaceborne radar platforms. In addition, the forward target striking requirements make the missile-borne clutter space–time spectrum further exhibit the severe range-dependent property. To deal with these issues, accurate motion modeling of a missile-borne radar platform is first carried out in this article, where the complex platform motions including forward-looking diving, spinning, and coning are considered. Then, the autocorrelation processing combined with iterative adaptive approach is applied to estimate the clutter angle-Doppler center frequencies, so as to effectively realize the clutter nonstationary compensation along spatial and temporal directions. Finally, a time-domain sliding window-based subspace projection method is proposed to achieve the robust clutter suppression. Both simulation and real-measured radar data processing results are presented to validate the effectiveness and feasibility of the proposed algorithm.