Clutter Model Research Articles

Robust Truncated Statistics Constant False Alarm Rate Detection of UAVs Based on Neural Networks

With the rapid popularity of unmanned aerial vehicles (UAVs), airspace safety is facing tougher challenges, especially for the identification of non-cooperative target UAVs. As a vital approach for non-cooperative target identification, radar signal processing has attracted continuous and extensive attention and research. The constant false alarm rate (CFAR) detector is widely used in most current radar systems. However, the detection performance will sharply deteriorate in complex and dynamical environments. In this paper, a novel truncated statistics- and neural network-based CFAR (TSNN-CFAR) algorithm is developed. Specifically, we adopt a right truncated Rayleigh distribution model combined with the characteristics of pattern recognition using a neural network. In the simulation environments of four different backgrounds, the proposed algorithm does not need guard cells and outperforms the traditional mean level (ML) and ordered statistics (OS) CFAR algorithms. Especially in high-density target and clutter edge environments, since utilizing 19 statistics obtained from the numerical calculation of two reference windows as the input characteristics, the TSNN-CFAR algorithm has the best adaptive decision ability, accurate background clutter modeling, stable false alarm regulation property and superior detection performance.

Effects of Mutual Coupling for Bistatic Airborne Radar with End-fire Array in Clutter Environment

Abstract The end-fire array is widely used in airborne radar systems for its specific physical configuration. However, mutual coupling is usually inevitable in the close-spaced end-fire array, which brings a great challenge for target detection in a clutter environment. In this paper, we investigate the effect of mutual coupling for the bistatic airborne radar with the end-fire array. Firstly, the signal model of the bistatic airborne radar with the end-fire array is presented. Then, the beam pattern and clutter model with mutual coupling are constructed. Furthermore, we analyze the clutter spectra with mutual coupling for different terrain types of the glistening zone. Finally, the simulation results validate the negative impact of the mutual coupling on target detection in clutter environment.

Modelling and Mitigating Wind Turbine Clutter in Space–Air Bistatic Radar

The extensive deployment of wind farms has significantly impacted the detection capabilities of space–air bistatic radar (SABR) systems. Although space–time adaptive processing techniques are available, their performance is significantly degraded, and even unable to suppress clutter. This paper explores the geometric configuration of the SABR system and the selection of detection areas, establishing a space–time clutter model that addresses the effects of wind turbine clutter (WTC). Expressions for spatial and Doppler frequencies have been derived to deeply analyze the characteristics of clutter spreading. Building on this, the paper extends two-dimensional space–time data to three-dimensional azimuth–elevation–Doppler data. It proposes a three-dimensional space–time multi-beam (STMB) strategy incorporating the Ordering Points to Identify the Clustering Structure (OPTICS) clustering algorithm to suppress WTC effectively. This algorithm selects WTC samples and applies OPTICS clustering to the clutter-suppressed data to achieve this effect. Simulation experiments further verify the effectiveness of the algorithm.

Mixture texture model with weighted generalized inverse Gaussian distribution for target detection

With the improvement of radar resolution, the amplitude distribution of sea clutter has begun to exhibit significant heavy-tailed characteristics. Existing models for sea clutter amplitude distribution do not sufficiently solve this problem, which results in a diminished target detection probability in two-step generalized likelihood ratio test (GLRT) based target detectors. To address this issue and to enhance the radar target detection capabilities, we propose a new compound-Gaussian clutter model based on a weighted mixture of generalized inverse Gaussian distributions to model the texture. The CG-WGIG model demonstrates greater flexibility and effectively captures the characteristics of sea clutter amplitude distributions with heavy tails, thus affording a better fit. We combine this model with the two-step GLRT criterion to propose a GLRT detector endowed with a weighted generalized inverse Gaussian texture (GLRT-WGIG), specifically tailored for detecting range-spread targets (RSTs). We have conducted a rigorous mathematical proof to demonstrate that the constant false alarm rate (CFAR) characteristic of the proposed GLRT-WGIG detector is independent of the covariance matrix, thereby ensuring its robustness in various operational scenarios. Using measured data, we demonstrate that the proposed model and detector are flexible and outperform the existing methods in terms of fitting and detection performance, where we obtain a mean 12.02% MSE of fitting performance promotion.

Design and application of a radar target simulator

Abstract To fulfill the need for debugging boundary scenes in radar data processing development, this paper proposes a simulator design method for the editing and simulation of diverse combat scenes as required. The simulator was developed based on the VPX platform, including external control equipment, resource scheduling, beam control, target simulation, and data processing. The motion model is based on linear and circular models, the clutter model algorithm is the zero memory nonlinear transformation method based on K distributions, and the noise model is designed as Gaussian white noise, characterized by a probability distribution in the Gaussian distribution and a constant power spectrum. The generated plots are condensed, and their quality is calculated, for which the rate of plot rejection can reach more than 50% by controlling the feature elements. Finally, the simulated target trajectory is visually verified through Matlab.

Clutter evalution of unmanned surface vehicles for maritime traffic monitoring

A traditional maritime radar system is utilized for ship detection and tracking through onshore transmitters and receivers. However, it faces challenges when it comes to detecting small boats. In contrast, unmanned surface vehicles (USVs) have been designed to monitor maritime traffic. They excel in detecting vessels of various sizes and enhance the capabilities and resolution of maritime radar systems. Nevertheless, just like conventional radar systems, USVs encounter difficulties due to environmental interference and clutter, affecting the accuracy of target signal detection. This research proposes a comprehensive numerical assessment to tackle the clutter issue associated with USVs. This involves gathering clutter signal data, performing numerical analysis, and employing distribution fitting techniques that leverage mathematical distributions to unravel data complexity. The root mean square error (RMSE) is applied in this analysis to validate the efficacy of the distribution model. The results of this study aim to formulate a clutter model that can enhance radar performance in detecting small vessels within cluttered environments.

A bat biomimetic model for scenario recognition using echo Doppler information

The flying bat can detect the difference in Doppler frequency between its echolocation transmission signal and the echoes in its surroundings, enabling it to distinguish between various scenarios effectively. By examining the bio-sonar biomimetic model of a flying bat that uses echo Doppler information for environmental recognition, it may enhance the scene recognition capability of human ultrasound sonar during movement. The paper establishes a three-dimensional clutter model of the flying state of bat bio-sonar for bats emitting constant frequency signals. It proposes a scene recognition method that combines multi-scale time-frequency feature analysis with a convolutional neural network (CNN). The short-time Fourier transform of different scales extract the Doppler and range dimensions, which are then fused to create a multi-scale feature plane containing both Doppler and range information. Combined with CNN’s powerful image classification and recognition capabilities, extract features from multi-scale feature planes of different clutter scenes to achieve environment recognition based on the differences in Doppler and range dimensions of echoes in various directions. Through computer simulations, this study provides a numerical interpretation of the environmental classification and perception capabilities of bats in flight. The algorithm significantly improves scenario classification and recognition performance according to simulation results, with accuracy exceeding 98% in varied clutter scenarios at 30 dB signal noise ratio. Based on computer simulations, an experimental scene was constructed and actual echo signals were collected and analyzed. The experiments demonstrate that utilizing Doppler information enables the classification and recognition of cluttered environments. The effectiveness of the proposed algorithm was also verified. Ultrasonic sonar systems, such as navigation robots and helicopter obstacle avoidance, can apply this biomimetic model and algorithm for environmental recognition during motion.

A General Evaluation System for Optimal Selection Performance of Radar Clutter Model

The optimal selection of radar clutter model is the premise of target detection, tracking, recognition, and cognitive waveform design in clutter background. Clutter characterization models are usually derived by mathematical simplification or empirical data fitting. However, the lack of standard model labels is a challenge in the optimal selection process. To solve this problem, a general three-level evaluation system for the model selection performance is proposed, including model selection accuracy index based on simulation data, fit goodness indexs based on the optimally selected model, and evaluation index based on the supporting performance to its third-party. The three-level evaluation system can more comprehensively and accurately describe the selection performance of the radar clutter model in different ways, and can be popularized and applied to the evaluation of other similar characterization model selection.

Assessment of a Monthly Data Structure for Growth and Yield Projections from Early to Harvest Age in Hybrid Eucalypt Stands

Whole-stand Models (WSM) have always been fitted with permanent plot data organised in a sequential age-matched database, i.e., i and i+1, where i = 1, 2, ... N plot measurements. The objectives of this study were (1) to evaluate the statistical efficiency of a monthly distributed data structure by fitting the models of Clutter (1963), Buckman (1962) in the version modified by A. L. da Silva et al. (2006), and deep learning, and (2) to evaluate the possibility of gaining accuracy in yield projections made from an early age to harvest age of eucalypt stands. Three alternatives for organizing the data were analyzed. The first is with data paired in sequential measurement ages, i.e., i and i+1, where i = 1, 2, ... N plot measurements. In the second, all possible measurement intervals for each plot were considered, i.e., ii+1; i, i+2; ...; iN; i+1, i+2; ..., N-1, N. The third has data paired by month (j), always with an interval of one month, i.e., j, j+1; j+1, j+2; j+M-1, M, where M is the stand age of the plot measurement in months. This study shows that the accuracy and consistency of the projections depend on the organization of the monthly distributed data, except for the Clutter model. A better alternative to increasing the statistical assumptions of the forecast from early to harvest age is based on a monthly distributed data structure using a deep learning method.

Sea clutter suppression algorithm in low SCR based on improved fractional Fourier transform

Sea clutter suppression and target detection under complex sea states in low signal-to-clutter ratio (SCR) is the key of current maritime detection research. The target extraction ability of most algorithms is mostly based on accurate modeling of sea clutter, but sea clutter is complex and unstable, which is difficult to accurately model, especially in poor sea states. According to the characteristics of the target echo signal, this paper introduces intrinsic time-scale decomposition (ITD) to preprocess the echo signal to improve the algorithm effect under low SCR. Secondly, the too much cycles in searching for optimal transformation in fractional Fourier transform (FRFT) is improved to reduce the calculation cost and extract the target signal accurately. The effectiveness and reliability of the proposed method are verified by simulation and measured experiments under different SCR and different transform order interval Δp, and the sea clutter suppression ability and target extraction accuracy are measured by parameters such as the detection rate, the gain of SCR and the running time. Compared with FRFT-related algorithms, the results indicates that the proposed algorithm can effectively suppress sea clutter and accurately extract target signal even at SCR = −25 dB in less than half the running time of the comparison algorithms.

Knowledge-Aided Wideband Radar Target Detection in the Heterogeneous Clutter

In this paper, the problem of wideband radar target detection in the heterogeneous environment is addressed. The wideband radar return of target with range migration is characterized as a subband model, and the heterogeneous clutter is described with a hierarchical Bayesian model. Both the prior knowledge of the clutter power and the covariance matrix and the dependence of the primary data and the secondary data are characterized with the inverse gamma and the inverse complex Wishart distribution, respectively. Based on the target and the clutter models, knowledge-aided maximum posterior ratio test (KAMAPRT), knowledge-aided Rao (KARao) test and knowledge-aided Wald (KAWald) test for wideband radar target detection in heterogeneous clutter are proposed. Finally, the performance of the proposed detectors is evaluated by simulations with both the simulated clutter generated by the probability model and the synthesized clutter from a real synthetic aperture radar (SAR) complex image. The results show that the proposed knowledge-aided detectors are effective for wideband radar target detection in the heterogeneous clutter.

PMBM Filter for Multiple Extended Targets With Unknown Clutter Rate and Detection Probability

In a multi-target tracking (MTT) scenario, the prior knowledge of parameters such as clutter rate and detection probability are usually uncertain or estimated offline from training data. However, significant parameters mismatch in the clutter and detection model will result in biased estimates. In such cases, the ability to adaptively online estimate clutter rate and detection probability is critical in MTT under the random finite sets (RFS) framework. In this paper, we propose a robust Poisson multi-Bernoulli mixture (PMBM) filter that can accommodate model mismatch in clutter rate and detection probability for multiple extended target tracking problems. Moreover, the closed-form solution to the proposed method is derived by the use of Beta and Gamma Gaussian inverse-Wishart (GGIW) distribution, where Beta is used to describe unknown detection probability and GGIW is used to model extended target extension as an ellipse. Simulation results are presented to verify the effectiveness of the proposed method.

Sea Clutter Feature Prediction and Parameters Inversion Using Deep Learning Model

The characteristics of sea clutter in real marine environments in different sea areas play a vital role in military industry such as radar detection, remote sensing, SAR imaging, and situational awareness. In this paper, a deep neural network sea clutter model is proposed based on the sea clutter big data under the real marine environment to study the characteristics of sea clutter and parameter inversion. Based on the ERA-Interim reanalysis (2015-2017), a database of marine environmental elements in China's offshore waters was established, and a spatiotemporal prediction model for marine environmental elements was proposed to improve missing values. Considering the scattering mechanism of sea surface at different scales comprehensively, a large database of sea clutter time series of multi-scale real surface is established and comparison with the experimental data. Aiming at the coastal waters of China, the LSTM model and deep neural network (DNN) are used to establish the correlation model between marine environmental elements and sea clutter characteristics, and the prediction and parameter inversion of sea clutter characteristics based on sea clutter big data are studied. The results shown that the coefficients of determination of the predicted fitted curves for the HH and VV polarization amplitudes reach 0.9249 and 0.8872, respectively. The inversion of wave heights in different sea areas is the lowest in the South China Sea (accuracy rate of 78%) and the highest in the East China Sea (accuracy rate is 90%). The results of this paper can help improve the ability of sea surface remote sensing and sea clutter suppression.

Jitter-Caused Clutter and Drift-Caused Clutter of Staring Infrared Sensor in Geostationary Orbit.

For staring infrared sensors in geostationary orbit, the clutter caused by the high-frequency jitter and low-frequency drift of the sensor line-of-sight (LOS) is the impact of background features, sensor parameters, LOS motion characteristics, and background suppression algorithms. In this paper, the spectra of LOS jitter caused by cryocoolers and momentum wheels are analyzed, and the time-related factors such as the jitter spectrum, the detector integration time, the frame period, and the temporal differencing background suppression algorithm are considered comprehensively; they are combined into a background-independent jitter-equivalent angle model. A jitter-caused clutter model in the form of multiplying the background radiation intensity gradient statistics by the jitter-equivalent angle is established. This model has good versatility and high efficiency and is suitable for the quantitative evaluation of clutter and the iterative optimization of sensor design. Based on satellite ground vibration experiments and on-orbit measured image sequences, the jitter-caused clutter and drift-caused clutter models are verified. The relative deviation between the model calculation and the actual measurement results is less than 20%.

Bayesian Detectors for Wideband Radar Target With Range Walking in Compound Gaussian Clutter

In this paper, the problem of the wideband radar target detection with the range walking in compound Gaussian clutter without the secondary data is addressed. The wideband radar target return with the range walking is represented as a canonical linear model in the multi-rank subspace, and the clutter is modeled as a two-dimensional random process with separable complex inverse Wishart distributed random covariance matrices in the pulse and range-frequency domains. The generalized likelihood ratio test (GLRT), the Wald test and the Rao test based on the above target and clutter models are designed under the Bayesian framework. The Laplace approximation is adopted to compute the second-kind confluent hypergeometric function of matrix argument (SKCHFMA) in the proposed detectors. Finally, the performance of the detectors is validated by the simulations.

Clutter Suppression Algorithm with Joint Intrinsic Clutter Motion Errors Calibration and Off-Grid Effects Mitigation in Airborne Passive Radars

In an airborne passive radar, multipath (MP) clutter, which is caused by MP signals contained in the contaminated reference signal, degrades the space-time adaptive processing (STAP) performance. The MP clutter suppression algorithm before STAP can mitigate the influence of impure reference signals. However, the performances of the existing MP clutter suppression methods deteriorate when the intrinsic clutter motion (ICM) exists because the sparse model of MP clutter is disturbed. To eliminate the impacts of ICM on MP clutter suppression, a joint optimization algorithm is developed for airborne passive radar. Firstly, the sparse model of MP clutter is modified by taking ICM fluctuation into account. Subsequently, the joint optimization function of the ICM fluctuation and MP clutter profile is derived. Finally, based on the local search technique, MP clutter is suppressed with ICM error calibration and off-grid effects mitigation. A range of simulations verify the reliability and superiority of the proposed method.

Seamount Clutter Modeling for Long Range Seabed Reverberation in Deep Sea

Reverberation clutter induced by discrete strong scatterers is an important reason which will increase the false alarm probability of active detection, which seriously affects the performance of active sonar detection. In this paper, taking seamount as an example, the theoretical modeling and simulation of the clutter in the deep-sea induced by seamount are carried out by using the ray-mode theory. Through ray-mode approximation, the acoustic rays in surface and reverse channels are ignored, a theoretical model is proposed to calculate the seabed reverberation clutter induced by seamounts, and the physical mechanism of how the seabed reverberation clutter is induced by seamounts is analyzed. The results show that reverberation intensity will increase in the location of seamount and induce discrete reverberation clutter. The greater the fluctuation of seamount, the greater the reverberation clutter intensity.

A Weak Target Detection Algorithm IAR-STFT Based on Correlated K-distribution Sea Clutter Model

A Weak Target Detection Algorithm IAR-STFT Based on Correlated K-distribution Sea Clutter Model

Dimensionality reduction bistatic radar clutter suppression technique based on three-dimensional cross-beam

Aiming at the problem that the non-stationary of airborne bistatic radar clutter reduces the performance of traditional clutter suppression algorithm, this paper first formulates airborne bistatic radar clutter model, and then analyzes the bistatic radar clutter characteristics in four typical combat scenarios. In addition, in order to reduce the computation burden of the STAP algorithm, a three-dimensional cross-beam clutter suppression algorithm is proposed, which uses the transformation matrix to convert the space-time two-dimensional data into azimuth-elevation-Doppler three-dimensional data. By eliminating auxiliary beam clutter data, only the main beam clutter data which plays a major role in dual-basis clutter suppression is retained to form a three-dimensional cross-beam for local adaptive clutter suppression. The proposed algorithm can greatly reduce the amount of calculation and the demand of training samples, while trying to ensure that the clutter suppression performance does not degrade. The effectiveness of the proposed algorithm is verified by analyzing the clutter suppression effect and computation amount of various algorithms in typical combat scenarios. Therefore, the algorithm has great potential in the applications of airborne bistatic and non-stationary clutter suppression.

Suzuki Distributed Monostatic and Bistatic S-Band Radar Sea Clutter

Low grazing angle S-band radar sea clutter data, collected with the NetRAD multistatic radar system, are analyzed. The Suzuki distribution, a compound Gaussian model with log normal texture, is found to be a good model for both monostatic and bistatic clutter. Clutter textures from simultaneously recorded monostatic and bistatic data are interpreted using hydrodynamics, providing estimates of the water depth and wave direction.