Clutter Suppression Capability Research Articles

Association of renal circulation at acute phase with decongestion level at discharge in patients admitted for acute decompensated heart failure

Abstract Background In patients admitted with acute decompensated heart failure (ADHF), renal hemodynamics may play an important role in the clinical course during hospitalization based on the close cardio-renal relationship. Superb Microvascular Imaging (SMI), distinguished by its advanced clutter suppression capabilities to reveal microflow details, holds promise for evaluating renal circulation. Consequently, our study aims to explore the association between renal circulation as assessed by SMI and the levels of brain natriuretic peptide (BNP) at discharge as an indicator of decongestion status. Methods In a prospective design, 45 ADHF patients were sequentially examined via renal ultrasound with SMI at discharge. The vascular index (VI) by SMI was calculated as the percentage of blood flow signal area in the region of interest. Then, the intra-renal perfusion index (IRPI) as an index of cyclic variation in VI, was calculated as the maximum VI minus the minimum VI within one cardiac cycle in the ROI divided by the Max.VI (Figure A). Results The values of IRPI were as follows: at 12h, 0.70±0.04; at 48h, 0.58±0.05; at 1W, 0.50±0.04; and at discharge, 0.61±0.04. The IRPI measured at 48h showed a stronger correlation with BNP at discharge (r = 0.57, p<0.001) than those at the other three points (Figure B). Patients with higher log BNP at discharge (> 2.31: determined by the median value) had significantly higher IRPI and age, and lower hemoglobin levels at 48h, however, there were no significant differences in estimated glomerular filtration rate, ejection fraction, or systolic pulmonary artery pressure at 48h. The area under the curve of IRPI at 48h for higher log BNP at discharge > 2.31 at discharge was 0.746 (sensitivity 100%, specificity 48%). Conclusion Early assessment of renal circulation by SMI around 48 hours after ADHF admission may predict congestion resolution at discharge. This underscores the potential utility of SMI in guiding more aggressive decongestive treatment strategies earlier in ADHF.

A Novel Fast Iterative STAP Method with a Coprime Sampling Structure.

In space-time adaptive processing (STAP), the coprime sampling structure can obtain better clutter suppression capabilities at a lower hardware cost than the uniform linear sampling structure. However, in practical applications, the performance of the algorithm is often limited by the number of training samples. To solve this problem, this paper proposes a fast iterative coprime STAP algorithm based on truncated kernel norm minimization (TKNM). This method establishes a virtual clutter covariance matrix (CCM), introduces truncated kernel norm regularization technology to ensure the low rank of the CCM, and transforms the non-convex problem into a convex optimization problem. Finally, a fast iterative solution method based on the alternating direction method is presented. The effectiveness and accuracy of the proposed algorithm are verified through simulation experiments.

A STAP Detection Scheme for Low Sample Support Maritime Environments

In airborne radar, reduced rank space-time adaptive processing techniques are employed when non-stationarity and non-homogeneity of the clutter causes insufficient sample support. There have been many approaches proposed to address this problem, including principal components, the cross-spectral metric and the multistage Wiener filter. This latter approach is superior to other reduced rank techniques in terms of computational efficiency, sample support and rank requirements. Regarding operation in heterogeneous environments, the single data set approach for clutter suppression has been proposed and operates solely on the cell under test to obtain a clutter covariance estimate. It is therefore highly effective in environments with limited training data that is homogeneous with the test data. In this paper, a single data set detection approach under the framework of the multistage Wiener filter is proposed and analysed to enhance clutter suppression capabilities. The target detection performance of the filter is evaluated using simulated maritime radar data.

A Novel Knowledge-Aided Training Samples Selection Method for Terrain Clutter Suppression in Hybrid Baseline Radar Systems

For a space-based radar system with hybrid baseline, the problem of clutter angle-Doppler spectral broadening poses a significant challenge to clutter cancellation in the terrain fluctuant observation scene. To improve the robustness of clutter suppression, this paper proposed a homogeneous sample selection method based on a novel concept of Generalized Spatial Spectrum Density Function (GSSDF). This method can be summarized as three crucial steps. First, the GSSDF was constructed by the prior information of DEM data, radar system parameters, and the backscattering model. Then, the angle of deflection (AOD) and the equivalent bandwidth (EBW) of GSSDF were adopted to measure the diffusion of clutter spectral. Subsequently, the sample selection criterion was established by a new threshold detection strategy. But the key here is that an appropriate detection threshold of the AOD and EBW can be determined by the characteristic of filter response. To summarize, this approach ensures that the training samples sharing similar clutter properties can be selected to estimate clutter covariance matrix (CCM); thereby, enhances clutter suppression capability. Finally, the experimental results demonstrated that the proposed method can obtain better clutter suppression performance than other contrast methods.

Clutter Reduction by Estimation of Echoes Direction of Arrival in Distributed Radar Sounders in Formation Flying

Spaceborne radar sounders are High Frequency (HF) / Very High Frequency (VHF) nadir-looking sensors devoted to subsurface investigations. Their data interpretation can be severely hindered by off-nadir surface clutter. Recent literature showed that the clutter suppression capabilities of this class of systems can be greatly enhanced by deploying an array of orbiting sensors in formation flight synthesizing a narrow radar antenna beam. In this paper, we assess the capability of distributed radar sounding to discriminate clutter from subsurface returns by exploiting Direction of Arrival (DOA) estimation techniques. This is achieved by first outlining an approach for designing and evaluating the distributed radar sounder DOA estimation performance as function of the radar system parameters (e.g, inter-sensor distance) and external noise factors such as ionospheric scintillations. Then, the theory is complemented by radar simulations of several acquisitions over Greenland assuming a variety of subsurface geometries. The simulations confirm that clutter discrimination through DOA estimation is a viable approach to further improve the array capability in disambiguation of subsurface echoes from surface ones.

A General Framework for Slow and Weak Range-Spread Ground Moving Target Indication Using Airborne Multichannel High-Resolution Radar

Airborne multichannel high-resolution radar (HRR)-ground moving target indication (GMTI) is of great significance to wide-area surveillance, traffic monitoring and target recognition. The Aerospace Information Research Institute, Chinese Academy of Sciences, produced an advanced airborne digital array radar with high resolution and conducted an experiment with slow and weak cooperative moving targets in 2021. In this paper, an overall processing framework for target detection, parameter estimation and target tracking using this system is introduced. For HRR detection, the echo energy of targets is spread into multiple range units, so-called range-spread targets; thus, using detectors designed for point-like targets will severely degrade the detection performance, especially for slow and weak targets. To address the adaptive detection of such range-spread targets embedded in Gaussian clutter with an unknown covariance matrix, a novel two-step generalized space-time adaptive processing (GSTAP) algorithm is proposed, which offers an enhanced clutter suppression capability compared with natural competitors. Moreover, a tracking method is implemented in the range-Doppler domain to avoid track loss caused by azimuth relocation error and reject discrete false alarms. Both simulation and experimental results are presented to demonstrate the effectiveness of the proposed method and provide a paradigm for further research.

Transmit–Receive Design for Airborne Radar With Nonuniform Pulse Repetition Intervals

This article aims to the joint design of transmit waveform and receive filter to enhance the clutter suppression capability for airborne radar with nonuniform pulse repetition interval (NUPRI). Specifically, a multipulse echo model with NUPRI incorporating a moving point-like target and signal-dependent clutter is first established. Then the 2-D range-Doppler plane is acquired by performing pulse compression and the windowed pulse Doppler processing on the echo. Further, the signal to interference plus noise ratio (SINR) of the interested area in the range-Doppler plane is formulated as a figure of merit to maximize along with constant modulus constraint on the waveform. To solve the resultant nonconvex problem, the sequential greedy optimization algorithm through alternately updating the transmit waveform and receive filter is proposed. Each iteration of the developed algorithm invokes coordinate descent framework and a Lagrangian-based algorithm to obtain the transmit waveform and receive filter, respectively. The analytical guarantee of the monotonic SINR improvement is proved. Finally, the performance of the proposed algorithm is assessed through numerical simulations showing its capability to suppress signal-dependent clutter.

Infrared Small Target Detection Method with Trajectory Correction Fuze Based on Infrared Image Sensor.

Due to the complexity of background and diversity of small targets, robust detection of infrared small targets for the trajectory correction fuze has become a challenge. To solve this problem, different from the traditional method, a state-of-the-art detection method based on density-distance space is proposed to apply to the trajectory correction fuze. First, parameters of the infrared image sensor on the fuze are calculated to set the boundary limitations for the target detection method. Second, the density-distance space method is proposed to detect the candidate targets. Finally, the adaptive pixel growth (APG) algorithm is used to suppress the clutter so as to detect the real targets. Three experiments, including equivalent detection, simulation and hardware-in-loop, were implemented to verify the effectiveness of this method. Results illustrated that the infrared image sensor on the fuze has a stable field of view under rotation of the projectile, and could clearly observe the infrared small target. The proposed method has superior anti-noise, different size target detection, multi-target detection and various clutter suppression capability. Compared with six novel algorithms, our algorithm shows a perfect detection performance and acceptable time consumption.

Passive Radar STAP Detection and DoA Estimation Under Antenna Calibration Errors

This article addresses the problem of clutter cancelation for slowly moving target detection and localization in multichannel passive radar onboard mobile platforms. A post-Doppler space-time adaptive processing (STAP) approach is exploited in the case of an angle-dependent imbalance affecting the receiving channels. While the clutter suppression capability is ensured by the adaptivity of space-time filtering, different solutions are compared, aimed at recovering the detection performance losses associated with channel calibration errors. A space-time generalized-likelihood ratio test (GLRT) scheme is considered, where the steering vector is not specified in the spatial domain, resulting in a noncoherent integration of target echoes across the receiving channels. This is compared with a fully coherent GLRT scheme where echoes from the stationary scene are exploited for the proper calibration of spatial steering vector mismatch. The first scheme proves to be a simple solution for target detection in the passive radar case, offering comparable clutter cancelation capability. The second scheme, at the expense of an additional stage, offers slightly better detection performance and preserves target direction-of-arrival (DoA) estimation capability. Finally, the STAP scheme is employed for the maximum-likelihood estimation of target DoA, evaluating the role of the steering vector calibration against the negative impact of channel imbalance. The effectiveness of the proposed approaches is tested against both simulated and experimental data from a DVB-T-based mobile passive radar.

Ambiguity Function of MIMO Radar Based on Quasi-Orthogonal Ultrawideband-Throb Signals

Multiple-input-multiple-output (MIMO)-radar systems employing ultrawideband (UWB) signals promise enhanced imaging performance. In this article, the generalized ambiguity function (GAF) for an MIMO-radar system is derived by using a set of quasi-orthogonal step-frequency UWB-throb signals. The effect of the step-frequency increment on the orthogonality property of the step-frequency UWB-throb signals is first analyzed. The properties of the UWB-GAF are also described. Computer simulation plots are generated for the delay-Doppler resolution function, delay-angle resolution function, Doppler-angle resolution function, delay profile, Doppler profile, and power beampattern for various design parameters of the MIMO-radar system and the employed UWB-throb signals. For the purpose of comparison, the same resolution functions and profiles are also generated for UWB linear-frequency-modulated (LFM) chirp signals. Performance analysis is conducted to demonstrate the resolution and the clutter-suppression capabilities of the GAF of an MIMO-radar system using UWB-throb signals in comparison with that of an MIMO-radar system using UWB-LFM-chirp signals. In principle, the UWB-GAF of an MIMO-radar system offers many degrees of freedom and allows for tradeoffs between the various signal-and-system design parameters. Such tradeoffs are of importance in practice for reducing the system complexity, enhancing the performance, and achieving the cost-effectiveness.

Structure-Adaptive Clutter Suppression for Infrared Small Target Detection: Chain-Growth Filtering

Robust detection of infrared small target is an important and challenging task in many photoelectric detection systems. Using the difference of a specific feature between the target and the background, various detection methods were proposed in recent decades. However, most methods extract the feature in a region with fixed shape, especially in a rectangular region, which causes a problem: when faced with complex-shape clutters, the rectangular region involves the pixels inside and outside the clutters, and the significant grey-level difference among these pixels leads to a relatively large feature in the clutter area, interfering with the target detection. In this paper, we propose a structure-adaptive clutter suppression method, called chain-growth filtering, for robust infrared small target detection. The well-designed filtering model can adjust its shape to fit various clutter structures such as lines, curves and irregular edges, and thus has a more robust clutter suppression capability than the fixed-shape feature extraction strategy. In addition, the proposed method achieves a considerable anti-noise ability by employing guided filter as a preprocessing approach and enjoys the capability of multi-scale target detection without complex parameter tuning. In the experiment, we evaluate the performance of the detection method through 12 typical infrared scenes which contain different types of clutters. Compared with seven state-of-the-art methods, the proposed method shows the superior clutter-suppression effects for various types of clutters and the excellent detection performance for various scenes.

A Spatial Coherence Beamformer Design for Power Doppler Imaging.

Acoustic clutter is a primary source of image degradation in ultrasound imaging. In the context of flow imaging, tissue and acoustic clutter signals are often much larger in magnitude than the blood signal, which limits the sensitivity of conventional power Doppler in SNR-limited environments. This has motivated the development of coherence-based beamformers, including Coherent Flow Power Doppler (CFPD), which have demonstrated efficacy in mitigating sources of diffuse clutter. However, CFPD uses a measure of normalized coherence, which incurs a non-linear relationship between image intensity and the magnitude of the blood echo. As a result, CFPD is not a robust approach to study gradation of blood signal energy, which depicts the fractional moving blood volume. We propose the application of mutual intensity, rather than normalized coherence, to retain the clutter suppression capability inherent in coherence beamforming, while preserving the underlying signal energy. Feasibility of this approach was shown via Field II simulations, phantoms, and in vivo human liver data. In addition, we derive an adaptive statistical threshold for the suppression of residual noise signals. Overall, this beamformer design shows promise as an alternative technique to depict flow volume gradation in cluttered imaging environments.

Anti‐jamming method for STAP based on a bi‐phase random‐coded signal

The scattered-wave sweeping frequency jamming occupies many space-time adaptive processing (STAP) degrees of freedom, which results in a serious decline in STAP performance. To solve this problem, an anti-jamming method of STAP based on a bi-phase random-coded signal is proposed. This method utilises the phase-coded signal's ability of waveform agile and anti-jamming. Also, the phase coding is designed as a random sequence, which distinguishes and suppresses the interference by white noise characteristic. Simulation results show that, compared with the STAP method based on a chirp signal, the proposed method improves beamforming robustness and clutter suppression capability under an interfering background.

Application of Adaptive Digital Beamforming to Osaka University Phased Array Weather Radar

The X-band phased array weather radar (PAWR) at Osaka University has a rapid scanning rate and is capable of high-density observations in elevation; it produces approximately 100 plan position indicator radar images with a 60-km range, at different elevation angles, in less than 30 s. The PAWR uses a fan-shaped beam with a narrow beamwidth (1.2°) in azimuth and a wider beamwidth (from 5° to 10°) in elevation. With digital beamforming (DBF), the elevation beamwidth can be reduced to 1.2°, using 128 antenna elements arranged in tandem. Although the fan-shaped beam is useful for rapid scanning, the received signals tend to be affected by ground clutter. In this paper, we investigate the clutter suppression capability of common DBF methods: Fourier, Capon, and minimum mean-square error (MMSE) beamforming. Furthermore, to improve performance when the PAWR data contain errors—such as lacking data caused by mechanical problems—a correction method is proposed. The effect of clutter suppression using MMSE is shown to be greatly improved if used together with the proposed correction method. The resulting method is shown to sufficiently suppress clutter in all elevation angles above a few degrees, even in the presence of strong clutter; its clutter reduction performance is compared and found to be superior to the ones of the analyzed conventional DBF methods.

A User Parameter-Free Diagonal-Loading Scheme for Clutter Rejection on Radar Wind Profilers

AbstractThis paper presents a novel method for the automatic determination of the diagonal-loading level for robust adaptive beamforming on radar wind profilers. This method balances the degradation of the signal-to-interference ratio with that of the signal-to-noise ratio to maximize the detectability of the backscattered signals. Because radial wind velocities are usually estimated from the first moment of the spectrum of backscattered echoes, both the residual ground clutter and any increase in noise level degrade the detectability of atmospheric echoes. The proposed algorithm evaluates the power spectral density of the residual clutter and increased noise to determine the optimal diagonal-loading level by balancing these two factors. The results of numerical simulation show that, without the need to specify any user parameters, the proposed algorithm is stable and more effective at maximizing the signal-to-interference ratio than the conventional norm-constrained diagonal-loading approach. The stability and clutter suppression capability of the proposed algorithm are examined using data from the Program of the Antarctic Syowa Mesosphere–Stratosphere–Troposphere/Incoherent Scatter Radar.

Ultra-wide bandwidth backscatter modulation: processing schemes and performance

Future advanced radio-frequency identification (RFID) systems are expected to provide both identification and high-definition localization of objects with improved reliability and security while maintaining low power consumption and cost. Ultrawide bandwidth (UWB) technology is a promising solution for next generation RFID systems to overcome most of the limitations of current narrow bandwidth RFID technology, such as reduced area coverage, insufficient ranging resolution for accurate localization, sensitivity to interference, and scarce multiple access capability. In this article, the UWB technology is applied to passive RFID relying on backscatter modulation. A signaling structure with clutter and interference suppression capability is proposed and analyzed. The potential performance is investigated in terms of range/data rate trade-off, clutter suppression, and multiple access capability using experimental data obtained in both the controlled and realistic environments.

Signal Processing System for the CASA Integrated Project I Radars

Abstract This paper describes the waveform design space and signal processing system for dual-polarization Doppler weather radar operating at X band. The performance of the waveforms is presented with ground clutter suppression capability and mitigation of range–velocity ambiguity. The operational waveform is designed based on operational requirements and system/hardware requirements. A dual–Pulse Repetition Frequency (PRF) waveform was developed and implemented for the first generation X-band radars deployed by the Center for Collaborative Adaptive Sensing of the Atmosphere (CASA). This paper presents an evaluation of the performance of the waveforms based on simulations and data collected by the first-generation CASA radars during operations.

TOA Estimator for UWB Backscattering RFID System with Clutter Suppression Capability

Time of arrival (TOA) estimation in multipath dense environment for UWB backscattering radio frequency identification (RFID) system is challenging due to the presence of strong clutter. In addition, the backscattering RFID system has peculiar signal transmission and modulation characteristics, which are considerably different from conventional communication and localization systems. The existing TOA estimators proposed for conventional UWB systems are inappropriate for the backscattering RFID system since they lack the required clutter suppression capability and do not account for the peculiar characteristics of backscattering system. In this paper, we derive a nondata-aided (NDA) least square (LS) TOA estimator for UWB backscattering RFID system. We show that the proposed estimator is inherently immune to clutter and is robust in under-sampling operation. The effects of various parameter settings on the TOA estimation accuracy are also studied via simulations.

Two-dimensional pulse-to-pulse canceller of ground clutter in airborne radar

It is well known that in the airborne radar, the location of the ground clutter spectrum in the angle-Doppler space is dependent mainly on the platform velocity and radar parameters. The authors propose a two-dimensional pulse-to-pulse canceller (TDPC) that can make full use of such prior information. The more detailed formulations of the ground clutter model and the signal model are given in a matrix-vector form. The least-squares-typical cost function associated with the filter coefficient matrix of the TDPC is established on the basis of the ground clutter model and the signal model. Like the classical displaced phase centre antenna (DPCA) processing, the proposed TDPC is also a spatial-temporal suppressor of ground clutter and can decrease the ground clutter signals, even though the DPCA condition is not satisfied. The proposed TDPC can also be used as an efficient pre-filtering tool before the conventional moving target indication (MTI) processing and the classical adaptive processing. Moreover, if only the TDPC plus the conventional MTI is used, it takes less computational time than the adaptive canceller. Experimental results show that the proposed TDPC has the satisfactory ground clutter suppression capability by using both simulated data and measured data.

Experimental studies on circular SAR imaging in clutter using angular correlation function technique

This paper describes a novel approach for three-dimensional (3-D) imaging in a heavy-clutter environment. The proposed approach consists of two techniques. The first calls for the use of a synthetic aperture radar (SAR) moving in a circular orbit, whereas the second involves using angular correlation function (ACF) measurement for achieving clutter suppression. To demonstrate the imaging and clutter suppression capabilities of this combined approach (circular-correlation SAR), microwave imaging experiments are conducted at X-band frequencies (7-13 GHz). It is found that in heavy-clutter environments, this combined SAR approach outperforms conventional SAR, resulting in clearer images with enhanced target-to-clutter ratio.