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Block diagram of SVM-CFAR.
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In this paper, we propose an intelligent constant false alarm rate detector which uses support vector machine (SVM) techniques to improve radar detection performance in different background environments. The proposed detector uses the variability index statistic as a feature to train a SVM and recognizes the current operational environment based on...
Citations
... The authors in Ref. [9] proposed an intelligent CFAR detector, which uses Support Vector Machine (SVM) techniques to improve the radar detection performance in different background environments. The proposed detector uses the variability index statistic as a feature to train an SVM and recognises the current operational environment based on the classification results. ...
... The proposed detector has the intelligence to select the proper detector threshold adaptive to the current operational environment. The proposed detector provides a low-loss performance in homogeneous backgrounds and also performs robustly in non-homogeneous environments including multiple targets and clutter edges [9]. ...
The presented paper is further focused on the presentation and subsequent assessment of utilising a proposed Neural Network (NN) with simple architecture in the role of a signal preprocessing algorithm for the Constant False Alarm Rate (CFAR) detector and the fixed threshold detector applied on a Range‐Doppler (RD) map with the aim of radar clutter impact reduction and minimisation of processing time. Based on a comparison of all tested algorithm results, it is possible to state that utilising the proposed NN with simple architecture led to reducing the impact of radar clutter when detecting radar targets on RD maps created from provided datasets. Comparing the mean processing time tmean values of all tested algorithms, we can state that employing the proposed NN in combination with the fixed threshold detector led to a significant improvement in the computation time needed for processing one RD map while preserving the suppression of radar clutter and detection of the radar targets. image
... However, these model-based approaches were designed considering a specific measurement model, and their performance may degrade in the case of model mismatch. Alternatively, the data-driven machine learning approaches have been proposed in [12], [13], [14], [15], [19], [20], and [23]. In these approaches, target detection is performed using features extracted from the data. ...
... In particular, Coluccia et al. [12], [14] proposed to obtain a KNN-based decision rule from simulated data and evaluated the proposed methods using the IPIX database [24] of recorded radar echoes that contain correlated heavy-tailed sea clutter. Wang et al. [23] used support vector machine to switch between conventional CFAR methods and perform target detection in an environment containing clutter edges and/or multiple interfering targets under white Gaussian noise. The work in [20] extended the work in [21] to angle dimension and proposed a reinforcement learning based approach to design the beamforming matrix in a cognitive radar setup. ...
... Therefore, the performances of these methods degrade in such scenarios. In addition, the methods in [4], [5], [6], [7], [8], [9], [10], [12], [13], [14], [15], [16], [17], [18], [19], [22], and [23] use the data after range matched-filter processing, which linearly projects each fast-time received pulse to range bins [3]. This linear transformation fails to suppress the clutter echo signals since these are not orthogonal to the projection signals that correspond to each range bin. ...
This work addresses the problem of range–Doppler multiple target detection in a radar system in the presence of
slow-time
correlated and heavy-tailed distributed clutter. Conventional target detection algorithms assume Gaussian-distributed clutter, but their performance is significantly degraded in the presence of correlated heavy-tailed distributed clutter. Derivation of optimal detection algorithms with heavy-tailed distributed clutter is analytically intractable. Furthermore, the clutter distribution is frequently unknown. This work proposes a deep learning-based approach for multiple target detection in the range–Doppler domain. The proposed approach is based on a unified neural network (NN) model to process the time-domain radar signal for a variety of signal-to-clutter-plus-noise ratios (SCNRs) and clutter distributions, simplifying the detector architecture and the NN training procedure. The performance of the proposed approach is evaluated in various experiments using recorded radar echoes, and via simulations, it is shown that the proposed method outperforms the conventional cell-averaging constant false-alarm rate (CFAR), the trimmed-mean CFAR, and the adaptive normalized matched-filter detectors in terms of probability of detection in the majority of tested SCNRs and clutter scenarios.
... The clutter edge environment is caused by changes in weather, jamming, and sudden changes in elevation/reflection [4,5]. The multitarget environment involves cases in which interfering targets exist in the reference window but not in the test cell [6]. For the CA CFAR algorithm, the threshold is calculated using the average of both the leading and lagging windows; therefore, if there is an interfering target, the threshold increases, and the detection probability decreases [7][8][9]. ...
The constant false-alarm rate (CFAR) algorithm is essential for detecting targets during radar signal processing. It has been improved to accurately detect targets, especially in nonhomogeneous environments, such as multitarget or clutter edge environments. For example, there are sort-based and variable index-based algorithms. However, these algorithms require large amounts of computation, making them difficult to apply in radar applications that require real-time target detection. We propose a new CFAR algorithm that determines the environment of a received signal through a new decision criterion and applies the optimal CFAR algorithms such as the modified variable index (MVI) and automatic censored cell averaging-based ordered data variability (ACCA-ODV). The Monte Carlo simulation results of the proposed CFAR algorithm showed a high detection probability of 93.8% in homogeneous and nonhomogeneous environments based on an SNR of 25 dB. In addition, this paper presents the hardware design, field-programmable gate array (FPGA)-based implementation, and verification results for the practical application of the proposed algorithm. We reduced the hardware complexity by time-sharing sum and square operations and by replacing division operations with multiplication operations when calculating decision parameters. We also developed a low-complexity and high-speed sorter architecture that performs sorting for the partial data in leading and lagging windows. As a result, the implementation used 8260 LUTs and 3823 registers and took 0.6 μs to operate. Compared with the previously proposed FPGA implementation results, it is confirmed that the complexity and operation speed of the proposed CFAR processor are very suitable for real-time implementation.
... However, these model-based approaches were designed considering a specific measurement model, and their performance may degrade in the case of model mismatch. Alternatively, data-driven machine learning (ML) approaches have been proposed in [12]- [15], [19], [20], [23]. In these approaches, target detection is performed using features extracted from the data. ...
... In particular, the authors in [12], [14] proposed to obtain a KNNbased decision rule from simulated data, and evaluated the proposed methods using the IPIX database [24] of recorded radar echoes that contain correlated heavy-tailed sea clutter. Authors in [23] used support vector machine to switch between conventional CFAR methods and perform target detection in an environment containing clutter edges and/or multiple interfering targets under white Gaussian noise. The work in [20] extended the work in [21] to angle dimension and proposed a reinforcement learning (RL) based approach to design the beamforming matrix in a cognitive radar (CR) setup. ...
... Therefore, the performances of these methods degrade in such scenarios. In addition, the methods in [4]- [10], [12]- [19], [22], [23] use the data after range matched-filter processing, which linearly projects each fast-time received pulse to range bins [3]. This linear transformation fails to suppress the clutter echo signals, since these are not orthogonal to the projection signals that correspond to each range bin. ...
This work addresses the problem of range-Doppler multiple target detection in a radar system in the presence of slow-time correlated and heavy-tailed distributed clutter. Conventional target detection algorithms assume Gaussian-distributed clutter, but their performance is significantly degraded in the presence of correlated heavy-tailed distributed clutter. Derivation of optimal detection algorithms with heavy-tailed distributed clutter is analytically intractable. Furthermore, the clutter distribution is frequently unknown. This work proposes a deep learning-based approach for multiple target detection in the range-Doppler domain. The proposed approach is based on a unified NN model to process the time-domain radar signal for a variety of signal-to-clutter-plus-noise ratios (SCNRs) and clutter distributions, simplifying the detector architecture and the neural network training procedure. The performance of the proposed approach is evaluated in various experiments using recorded radar echoes, and via simulations, it is shown that the proposed method outperforms the conventional cell-averaging constant false-alarm rate (CA-CFAR), the ordered-statistic CFAR (OS-CFAR), and the adaptive normalized matched-filter (ANMF) detectors in terms of probability of detection in the majority of tested SCNRs and clutter scenarios.
... In recent years, to improve detection performance in multiple target situations and the clutter edge, many CFAR detectors have been studied based on weighted iteration [14], [15], machine learning [16], [17], variability index [18], and outlier rejection based on the Grubbs criterion [19]. These studies have improved detection performances in multiple target situations and the clutter edge. ...
... This limitation is critical for real-time radar systems. In addition, the machine learning method [16], [17] has performance differences depending on the training data. If the detector receives an input that differs from the previously learned data, performance degradation occurs. ...
Herein, a robust constant false alarm rate (CFAR) detector with ordered statistic of sub-reference cells (OSS-CFAR) is proposed in multiple target situations. This detector can improve background level estimation and reduce computational complexity using sub-reference cells. The detection performance of the OSS-CFAR detector and of conventional CFAR detectors in multiple target situations are investigated and compared using computer simulations and experimental data with sea clutter. The simulations and experimental results show that the OSS-CFAR detector achieves robust detection performance with low computational complexity, whereas conventional CFAR detectors suffer performance degradation in multiple target situations. At the clutter edge, the OSS-CFAR detector with appropriate parameters achieves an acceptable false alarm rate compared to conventional CFAR detectors.
... The idea of SVMs comes from separating the data by using a line called the hyperplane. It is one of the best algorithms for text classification [5], [6]. The reason for choosing the SVM algorithm as the classifier we use is that after researching and comparing it with other algorithms, we found it is one of the best algorithms for classifying text and the most efficient. ...
The graduation projects (GP) are important because it reflects the academic profile and achievement of the students. For many years’ graduation projects are done by the information technology department students. Most of these projects have great value, and some were published in scientific journals and international conferences. However, these projects are stored in an archive room haphazardly and there is a very small part of it is a set of electronic PDF files stored on hard disk, which wastes time and effort and cannot benefit from it. However, there is no system to classify and store these projects in a good way that can benefit from them. In this paper, we reviewed some of the best machine learning algorithms to classify text “graduation projects”, support vector machine (SVM) algorithm, logistic regression (LR) algorithm, random forest (RF) algorithm, which can deal with an extremely small amount of dataset after comparing these algorithms based on accuracy. We choose the SVM algorithm to classify the projects. Besides, we will mention how to deal with a super small dataset and solve this problem.
... Una de las técnicas ampliamente utilizada en la práctica con el objetivo de adaptarse a la interferencia es el procesamiento con razón de falsa alarma constante (CFAR por sus siglas en inglés) [9]. Aunque existen numerosas variantes [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29], todas comparten dos principios básicos: (i) estiman las características de la interferencia a partir de una ventana deslizante cuyo elemento central es la célula resolutiva sometida a análisis y (ii) varían el umbral de detección en función del estimado anterior para asegurar la probabilidad de falsa alarma deseada. ...
A finales del pasado siglo fue propuesta la detección por radar en el espacio de los momentos estadísticos. Las principales motivaciones para su uso radican en el incremento del poder discriminador de los parámetros y en la disminución de la incertidumbre acerca de su distribución. Sin embargo, la teoría disponible solo considera momentos estadísticamente independientes y carece de procedimientos adaptativos factibles de implementación práctica. La presente investigación contribuye a solucionar las problemáticas anteriores, al incidir en dos aspectos fundamentales: la regla de decisión y el mecanismo de adaptación. Ambos elementos se integran en un novedoso método de detección que consigue mantener la razón de falsa alarma constante cuando los momentos presentan un grado de correlación arbitrario. Mediante experimentos con señales reales se verifica su operación robusta ante escenarios heterogéneos y la mejora en la calidad de la detección respecto a las técnicas con integración no coherente convencionales
... In addition to OS-CFAR, the applicability of those aforementioned methods to indoor scenarios is also investigated in this work. Using modern machine learning techniques, a CFAR detector using Support Vector Machines (SVMs) was proposed in [24] and a neural network-based CFAR was proposed in [25]. Although highly efficient in scenarios well captured by the training data, those methods based on supervised training are not guaranteed to perform well across radically different scenes. ...
... CFAR filters are used to detect targets in unknown background noise [17]. The most traditional CFAR is the CA-CFAR, which computes a local detection threshold for the cell under test (CUT) proportional to the average of the local training cells in a sliding analysis window (see Fig. 1 where S is the CUT and the Z l where scatterers are well isolated, CA-CFAR performs well, but its performance can strongly degrade in non-homogeneous environments where multiple targets and clutter edges are present [24]. The greatest of (GO) CA-CFAR (which chooses the greatest average between the left training cells Z l [x] and the right training cells Z r [x]) performs better compared to CA-CFAR in environments with several clutter edges but suffers from a loss in P D when mulitple targets are present locally. ...
As radar sensors are being miniaturized,there is a growing interest for using them in indoor sensing applications such as indoor drone obstacle avoidance. In those novel scenarios,radars must perform well in dense scenes with a large number of neighboring scatterers. Central to radar performance is the detection algorithm used to separate targets from the background noise and clutter. Traditionally,most radar systems use conventional CFAR detectors but their performance degrades in indoor scenarios with many reflectors. Inspired by the advances in non-linear target detection,we propose a novel high-performance,yet low-complexity target detector and we experimentally validate our algorithm on a dataset acquired using a radar mounted on a drone. We experimentally show that our proposed algorithm drastically outperforms OS-CFAR (standard detector used in automotive systems) for our specific task of indoor drone navigation with more than 19% higher probability of detection for a given probability of false alarm. We also benchmark our proposed detector against a number of recently proposed multi-target CFAR detectors and show an improvement of 16% in probability of detection compared to CHA-CFAR,with even larger improvements compared to both OR-CFAR and TS-LNCFAR in our particular indoor scenario. To the best of our knowledge,this work improves the state of the art for high-performance yet low-complexity radar detection in critical indoor sensing applications.
... In addition to OS-CFAR, the applicability of those aforementioned methods to indoor scenarios is also investigated in this work. Using modern machine learning techniques, a CFAR detector using Support Vector Machines (SVMs) was proposed in [24] and a neural network-based CFAR was proposed in [25]. Although highly efficient in scenarios well captured by the training data, those methods based on supervised training are not guaranteed to perform well across radically different scenes. ...
... CFAR filters are used to detect targets in unknown background noise [17]. The most traditional CFAR is the CA-CFAR, which computes a local detection threshold for the cell under test (CUT) proportional to the average of the local training cells in a sliding analysis window (see Fig. 1 where S is the CUT and the Z l where scatterers are well isolated, CA-CFAR performs well, but its performance can strongly degrade in non-homogeneous environments where multiple targets and clutter edges are present [24]. The greatest of (GO) CA-CFAR (which chooses the greatest average between the left training cells Z l [x] and the right training cells Z r [x]) performs better compared to CA-CFAR in environments with several clutter edges but suffers from a loss in P D when mulitple targets are present locally. ...
As radar sensors are being miniaturized, there is a growing interest for using them in indoor sensing applications such as indoor drone obstacle avoidance. In those novel scenarios, radars must perform well in dense scenes with a large number of neighboring scatterers. Central to radar performance is the detection algorithm used to separate targets from the background noise and clutter. Traditionally, most radar systems use conventional CFAR detectors but their performance degrades in indoor scenarios with many reflectors. Inspired by the advances in non-linear target detection, we propose a novel high-performance, yet low-complexity target detector and we experimentally validate our algorithm on a dataset acquired using a radar mounted on a drone. We experimentally show that our proposed algorithm drastically outperforms OS-CFAR (standard detector used in automotive systems) for our specific task of indoor drone navigation with more than 19% higher probability of detection for a given probability of false alarm. We also benchmark our proposed detector against a number of recently proposed multi-target CFAR detectors and show an improvement of 16% in probability of detection compared to CHA-CFAR, with even larger improvements compared to both OR-CFAR and TS-LNCFAR in our particular indoor scenario. To the best of our knowledge, this work improves the state of the art for high-performance yet low-complexity radar detection in critical indoor sensing applications.
... One of the techniques widely used in practice for adaptation to interference is the constant false alarm rate (CFAR) processing [1,2]. Although there are numerous variants [2][3][4][5][6][7][8][9][10][11][12][13][14] they all share two basic principles: (i) estimate the interference characteristics from a sliding window with the cell under test as the central element and (ii) vary the detection threshold as a function of the previous estimate to ensure the desired false alarm probability. ...
Radar detection in environments where the power and variability of interference hinders the location of targets is of great importance. Many efforts are directed towards the development of adaptive techniques that process the eco-signals in order to increase the detection quality and to maintain a constant false alarm rate (CFAR). In this context was conceived the radar detection in the moments space, which reduces the uncertainty and enhances the discriminatory ability for environments with low signal-to-interference ratios. The possibilities of this technique are extended by proposing a detector that preserves the constant false alarm rate under the natural changes of the interference. The new detector is called SM-CFAR (Statistical Moments-CFAR) and is based on the square of the Mahalanobis distance. The SM-CFAR will allow the radars to have a technique free of the parameter's distribution and with higher detection capability than those commonly used. Experiments with real signals demonstrate the superior performance of the SM-CFAR when compared to the CA-CFAR with non-coherent integration.
