Constant False Alarm Rate Algorithm Research Articles

Adaptive radar pulse detection design based on difference of box filter

AbstractPrecise radar pulse detection is the premise of electronic support measures. A constant false alarm rate (CFAR) detection algorithm based on difference of box (DOB) filter is proposed to realise low‐complexity and environment‐adaptive radar pulse detection, and the principle behind the proposed algorithm is revealed. Specifically, the DOB filter is adopted to extract the rising edges and falling edges of radar pulses, and the signal variation law of probability density functions (PDFs) during the process of DOB filter are first theoretically analysed. Based on the derived PDFs, dynamic detection threshold and fixed threshold factor for the proposed CFAR algorithm based on DOB filter are deduced to detect the presence of radar pulses. Furthermore, the closed‐form expression of the detection probability is derived. Simulations and on‐board field programmable gate arrays implementation verify the superiority of the proposed CFAR algorithm based on DOB filter. Simulation results show that the proposed algorithm performs well in detecting various types of pulse signals, the detection probability ≥99% on condition that the signal‐to‐noise ratio ≥3 dB and false alarm probability = 10−8. Moreover, the proposed CFAR algorithm based on DOB filter can deal with both singular pulses and pulse on pulse signals.

Constant false alarm rate detection method in mixed Weibull distribution sea clutter

In maritime surveillance, sea clutter represents a major obstacle to target detection accuracy due to missed or false alarms. Several methods have been proposed to find the most efficient. However, most of these methods have only been tested with simulated data, and their applicability and success in a real environment cannot be guaranteed. This work proposes constant false alarm rate (CFAR) detection techniques tailored to mixed Weibull-distributed sea clutter. The mixed Weibull distribution was used to model a mixture of Gaussian and non-Gaussian data. For CFAR detection in mixed Weibull distribution and Weibull clutter, the log t CFAR (logt-CFAR), Weber Haykin CFAR (WHCFAR), Geometric Mean Order Statistic CFAR (GMOS-CFAR), Trimmed Mean Order Statistic CFAR (TMOS-CFAR), Inclusion/Exclusion CFAR (IE-CFAR), and Weber Haykin Order Statistic CFAR (WHOS-CFAR) algorithms were performed. Both synthetic and actual ice multi-parameter imaging X-band (IPIX) radar data were used for experimental verification of this distribution. Simulation studies demonstrated that the proposed detectors maintain the CFAR property in Weibull-distributed clutter, making them effective in controlled scenarios, and confirming how better is the detection performance compared to other detection methods. This research bridges a critical gap in radar signal processing by significantly improving the detection of targets in challenging clutter environments.

An evaluation research of constant false alarm rate algorithm for the application of underwater vehicles management system based on USBL sonar principles in shallow water

The paper presents an overview of underwater traffic management systems utilizing sonar principles. Based on the evaluation of the current situation both domestically and internationally, the research team has proposed the development of a underwater signal processing algorithm based on an adaptive threshold in the Ultra-short Baseline System (USBL). The theoretical results were implemented on FPGA hardware, not only enhancing signal detection capabilities but also meeting real-time signal processing requirements when deployed in complex underwater conditions in various shallow water regions of Vietnam. The research's future direction indicates substantial potential in the practical application of hydroacoustic signal processing algorithms when executing hardware implementations.

An improved CFAR algorithm for multiple environmental conditions

AbstractThe primary objective of radar digital signal processing is to detect and identify targets in complicated situations, such as those involving clutter or several closely positioned targets. The constant false alarm rate (CFAR) method is more effective for target recognition and has better control over the false alarm rate. Many studies have been conducted on the design of CFAR, but previous CFAR algorithms have not been effective in all or most environmental fields and target scenarios. In this study, an algorithm called Censored Mean Clutter Map CFAR (CM-CM CFAR) has been developed and tested for various environmental conditions. When compared to a fixed false alarm rate, the suggested CFAR algorithm’s Monte Carlo simulation results demonstrated a high detection probability in a variety of environments. This work designs a real-time CM-CM CFAR processor using field-programmable gate array (FPGA) technologies. Xilinx ARTIX 7 FPGA technology is used to develop and map a scalable parallel framework. Consequently, the implementation required 23,741 LUTs and 1825 FF. It is verified that the complexity and operating speed of the suggested CFAR processor are extremely appropriate for real-time implementation when compared to the results of the previously proposed FPGA implementation.

Comparison of sEMG Onset Detection Methods for Occupational Exoskeletons on Extensive Close-to-Application Data.

The design of human-machine interfaces of occupational exoskeletons is essential for their successful application, but at the same time demanding. In terms of information gain, biosensoric methods such as surface electromyography (sEMG) can help to achieve intuitive control of the device, for example by reduction of the inherent time latencies of a conventional, non-biosensoric, control scheme. To assess the reliability of sEMG onset detection under close to real-life circumstances, shoulder sEMG of 55 healthy test subjects was recorded during seated free arm lifting movements based on assembly tasks. Known algorithms for sEMG onset detection are reviewed and evaluated regarding application demands. A constant false alarm rate (CFAR) double-threshold detection algorithm was implemented and tested with different features. Feature selection was done by evaluation of signal-to-noise-ratio (SNR), onset sensitivity and precision, as well as timing error and deviation. Results of visual signal inspection by sEMG experts and kinematic signals were used as references. Overall, a CFAR algorithm with Teager-Kaiser-Energy-Operator (TKEO) as feature showed the best results with feature SNR = 14.48 dB, 91% sensitivity, 93% precision. In average, sEMG analysis hinted towards impending movements 215 ms before measurable kinematic changes.

Multi-target CFAR detector based on compressed sensing radar system

ABSTRACT In the multi-target scenarios, we consider the problem of constant false alarm rate (CFAR) target detection. For the conventional CFAR algorithms, the multi-target detection performance is mainly limited by the interference target tolerance within the reference window. In this paper, we propose a local ordered statistic CFAR (OS-CFAR) target detector based on compressed sensing (CS) radar system to address the degradation of detection performance in multi-target scenarios. With the analog-information-converter (AIC), the radar intermediate frequency (IF) signal is compressively sampled into discrete linear measurements. At each detection stage, multiple signal components are generated by correlation tests between the sensing matrix and linear measurements. By support set merge and proxy pruning, the signal components with the largest energy are retained. These components are the echoes reflected from the targets. Since target components are output in decreasing order of correlation, the proposed detector only requires a local OS-CFAR decision in the interval where the least correlated target is located, rather than traversing the entire interval. By continuously updating the detection stage, all targets that satisfy the false alarm rate requirement can be screened out. Finally, the performance of the proposed detector in the multi-target scenarios is confirmed by simulation results.

FPGA Implementation of Efficient CFAR Algorithm for Radar Systems

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.

Estimation of number and position of fish aggregating devices in Indonesia Archipelagic Waters

The research conducted in 2021 aimed to address the limitation on the use of Fish Aggregating Devices (FADs) as one of the management measures in the tuna harvest strategy in Indonesia Archipelagic Waters (IAW), specifically in Indonesia FMA 713, 714, and 715. The lack of data on the number and position of FADs posed challenges for effective management. The research utilized Sentinel-1 satellite image data from July and August 2020 to overcome this issue. These images were analyzed using the Constant False Alarm Rate algorithm and a geospatial approach. The research focused on water areas greater than 12 nautical miles from the coastline of IAW. The results estimated the number of FADs in Indonesia FMA 713, 714, and 715 to be 661, 608, and 291 units. The distance between FADs was generally less than ten nautical miles. These findings provide valuable information for managing and implementing FADs in the designated areas. However, it is important to note that the estimates are based on data from 2020, and the actual number and position of FADs may have changed. The research highlights the need for continued monitoring and registration of FADs to ensure accurate and up-to-date information for effective management measures.

Industrial fluid pipeline leak detection and localization based on a multiscale Mann-Whitney test and acoustic emission event tracking

This paper proposes a novel technique for leak detection and localization in industrial fluid pipelines. Artificial intelligence-based supervised methods are a popular adoption for pipeline leak identification. However, supervised techniques need prior knowledge about pipeline failure for training purposes. To address this challenge, a new method for pipeline leak detection independent of prior knowledge is proposed. First, acoustic emission signals from the pipeline are acquired. Then, a multiscale Mann-Whitney test is developed, and the test output statistics are used as a pipeline leak state indicator. After detecting the leak, the proposed method localizes the leak by using a newly developed method called acoustic emission event tracking. A major challenge in leak localization is the false alarms that are generated by the poor identification of leak-related acoustic emission events. The acoustic emission event tracking presented in this work precisely determines the leak-related acoustic emission events. The new method first detects acoustic emission hits by using the variability index constant false alarm rate algorithm. Then, short-term energy is calculated in the hit perceived variability index constant false alarm rate windows. The high-energy acoustic emission events are separated into an event bank. Leak-related acoustic emission events are filtered out from the event bank using the theory of wave propagation. The filtered events obtained from the proposed method elucidate leaks and thus reduce the error in leak localization. The results obtained from the proposed method outperformed the reference methods in terms of accuracy for leak detection and localization under variable pressure and leak conditions.

Performance Evaluation of FPGA-Based LSTM Neural Networks for Pulse Signal Detection on Real-Time Radar Warning Receivers

Abstract Radar warning receivers are real-time systems used to detect emitted signals by the enemy targets. The conventional method of detecting the signal is to determine the noise floor and differentiate the signals above the noise floor by setting a threshold value. The common methodology for detecting signals in noisy environment is Constant False Alarm Rate (CFAR) detection. In CFAR methodology, threshold level is determined for a specified probability of false alarm. CFAR dictates the signal power to be detected is higher than the noise floor, i.e. signal-to-noise ratio (SNR) should be positive. To detect radar signals for negative SNR values machine learning techniques can be used. It is possible to detect radar signals for negative SNR values by Long Short-Term Memory (LSTM) Artificial Neural Network (ANN). In this study, we evaluated whether LSTM ANN can replace the CFAR algorithm for signal detection in real-time radar receiver systems. We implemented a Field Programmable Gate Array (FPGA) based LSTM ANN architecture, where pulse signal detection could be performed with 94% success rate at -5 dB SNR level. To the best of our knowledge our study is the first where LSTM ANN is implemented on FPGA for radar warning receiver signal detection.

Distinction of Human and Mechanical Vibrations within Similar Frequency Bands Based on Wavelet Entropy Using Ultrawideband Radar

Construction machinery is necessary in postdisaster emergency rescue missions involving the destruction of ruins. However, their mechanical vibrations can interfere with the detection of human survivors using ultrawideband (UWB) radar. Traditional methods detect and identify humans by determining maximum energy and checking respiratory frequency. However, they lose effectiveness because mechanical vibration is associated with a frequency band which is similar to the human respiration band, but it has higher energy. This study proposes a novel method to distinguish human vibrations from mechanical vibrations. After preprocessing, wavelet entropy decomposition was implemented on the radar data. An improved, censored mean-level detector, constant false-alarm rate algorithm was utilized to automatically identify the position of human and mechanical vibrations. A novel feature is then extracted by calculating the half-height width of the target’s wavelet entropy. Finally, the results of two independent sample t-tests prove that there is a significant statistical difference between the feature values of humans and the mechanical vibrations (p < 1.9 × 10−6), thus proving the effectiveness of the method. We envisage that the proposed method can be used in postdisaster rescue missions to improve the accuracy and speed of identifying human targets. Therefore, more survivors may be rescued.

The combination of singular values decomposition with constant false alarm algorithms to enhance ship detection in a polarimetric SAR application

Ships remote sensing is an essential tool for marine traffic management. It can enhance the monitoring system and ensure the security of sailing ships. We aim in this research paper to identify the best ship detection procedure that combines Singular Values Decomposition (SVD) and Constant False Alarm Rate (CFAR) algorithms. The SVD is now known as a Polarimetric Synthetic Aperture Radar (Pol-SAR) decomposition that can increase the contrast between the ships and the sea in SAR images. The CFAR algorithms are defined as detectors having an adaptive threshold based on well known distributions that model sea clutter. The dataset processed in this research was acquired using RADARSAT-2 satellite over a portuary zone situated in Vancouver. In this paper, it was shown that combining between singular values decomposition and CFAR algorithm based on the exponential distribution outperforms all the combination studied in this paper and existing algorithms present in the CFAR literature, and it attains the best probability of detection (PD) of 93% with a probability of false alarm (PFA) of 0.1%. This result is validated by comparing it to the achievement obtained with the state of the art CFAR algorithms based on distributions such as generalized Gamma where we obtained a PD of 84%, GA0 a PD of 82%, Weibull a PD of 75% and 68% for generalized Pareto distribution.

Autoencoder-Based Target Detection in Automotive MIMO FMCW Radar System.

In general, a constant false alarm rate algorithm (CFAR) is widely used to automatically detect targets in an automotive frequency-modulated continuous wave (FMCW) radar system. However, if the number of guard cells, the number of training cells, and the probability of false alarm are set improperly in the conventional CFAR algorithm, the target detection performance is severely degraded. Therefore, we propose a method using a convolutional neural network-based autoencoder (AE) to replace the CFAR algorithm in the multiple-input and multiple-output FMCW radar system. In the AE, the entire detection result is compressed at the encoder side, and only significant signal components are recovered on the decoder side. In this work, by changing the number of hidden layers and the number of filters in each layer, the structure of the AE showing a high signal-to-noise ratio in the target detection result is determined. To evaluate the performance of the proposed method, the AE-based target detection result is compared with the target detection results of conventional CFAR algorithms. As a result of calculating the correlation coefficient with the data marked with the actual target position, the proposed AE-based target detection shows the highest similarity with a correlation of 0.73 or higher.

An Image-Based Radar Detector Approaching Optimal Likelihood Ratio Detector

This paper presents an image-based radar detector, named neighborhood difference order statistics (NDOS) detector. Different from the classic likelihood ratio detector, the proposed detector treats the echo spectrum as an image and determines the existence of a target by comparing the difference between the test cell and its adjacent cells with a threshold. The closed-form expressions of probabilities of detection and false alarm are derived under Gaussian noise background and Swerling I target model. It is proved that the detection performance of the proposed detector approaches the optimal likelihood ratio detector when the homogenous noise power is known. When the noise power is unknown, we modify the detector into cell-averaging (CA) NDOS detector by estimating the noise power. Analytical derivations show that the CA-NDOS detector holds the constant false alarm rate (CFAR) identity. Moreover, CA-NDOS detector possesses a better detection performance compared with two typical CFAR algorithms under the condition of typical reference window size.

Orthogonal projection constant false alarm rate algorithm in fractional domain for small surface targets

In this article, a constant false alarm rate (CFAR) detection algorithm based on the fractional Fourier transform (FRFT) and orthogonal projection (OP) is proposed. With a low signal-to-clutter ratio (SCR), the radar returns of sea-surface small targets are often submerged by sea clutter. The FRFT can accumulate target energy in the optimal order. Meanwhile, the OP suppresses the sea clutter in a projection space in the FRFT domain, thus enhancing the output SCR. The FRFT and the OP are in conjunction with the CFAR to realise target detection, which is referred to as the FRFT-OP-CFAR. For the FRFT-OP-CFAR detector, the most important is the selection of the optimal order p and the calculation of the OP operator ψ x ⊥ . The experimental results show that the proposed FRFT-OP-CFAR has a better clutter suppression and detection performance than some detectors in the authors’ comparison.

A CFAR Algorithm Based on Monte Carlo Method for Millimeter-Wave Radar Road Traffic Target Detection

The development of Intelligent Transportation Systems (ITS) puts forward higher requirements for millimeter-wave radar surveillance in the traffic environment, such as lower time delay, higher sensitivity, and better multi-target detection capability. The Constant False Alarm Rate (CFAR) detector plays a vital role in the adaptive target detection of the radar. Still, traditional CFAR detection algorithms use a sliding window to find the target limit radar detection speed and efficiency. In such cases, we propose and discuss a CFAR detection method, which transforms the Monte Carlo simulation principle into randomly sampling instantaneous Range–Doppler Matrix (RDM) data, to improve the detection ability of radar for moving targets such as pedestrians and vehicles in the traffic environment. Compared with conventional methods, simulation and real experiments show that the method breaks through the reference window limitation and has higher detection sensitivity, higher detection accuracy, and lower detection delay. We hope to promote the detection application of millimeter-wave radar in road traffic scenes.

Deep Learning Approach for Fixed and Rotary-Wing Target Detection and Classification in Radars

Recent advancements demonstrate the application of machine learning techniques to radar signal processing, particularly the use of neural networks in one-dimensional (1-D) radar data to assist constant false alarm rate (CFAR) based target detection. However, the efficacy of using neural networks alone on 2-D data for target extraction remains to be explored. This research aims to develop a novel approach of designing a convolution neural network (CNN) based target detection system for real radar data. We propose a dual neural network detection scheme in which the first neural net filters out noisy images. Thereafter, we employ a second CNN-based target detector that extracts information from both radar data axes (fast-time and slow-time), thus utilizing additional information for better detection of targets even with low SNR. Moreover, the same CNN, in conjunction with a region-based convolutional network, exploits micro-Doppler features of rotary-wing aircraft in radar returns and uses them to further classify detected targets as fixed-wing or rotary-wing aircraft. To the best of our knowledge, no generalized methodology exists to address these two issues together. The proposed solution replaces the critical CFAR and peak detection algorithms from the radar signal processing chain. This article also introduces a labeled range-Doppler images dataset obtained from real-world air traffic control radar data. Experimental results demonstrate the superior performance of the proposed technique over 1-D and 2-D CFAR and peak detection algorithms.

Achievement of Small Target Detection for Sea Ship Based on CFAR-DBN

With the rapid development of marine exploration and marine transportation, the activities of marine ships are becoming more and more frequent. Accurate and rapid detection of the position of marine ships has very important practical and strategic significance. SAR has the characteristics of all-weather detection. It is an important means of ship detection. Aiming at the problem of fuzzy interference in sea surface ship SAR image detection, a small target image detection algorithm based on constant false alarm rate (CFAR) and depth belief network (DBN) is proposed. Firstly, according to the traditional CFAR detection principle, the whole image to be detected is detected by CFAR globally, and the index matrix is obtained, so as to improve the ship detection speed. Secondly, the output data of the hidden layer of the last layer of DBN are used as the input data of SVM, and the trained DBN model is applied to local detection, so as to improve the accuracy and robustness of ship detection. Finally, the algorithm combining CFAR and DBN is trained and applied in ship detection. Experimental results show that the accuracy of the proposed algorithm under fuzzy interference is better than that of traditional CFAR, BPNN, Fast R-CNN, and SSD512 algorithms, which proves that the robustness of the combined algorithm is significantly improved.

An Anti-2D Deceptive Jamming Method for Multi-Baseline Interferometric SAR Based on Co-Localization Jammer

Aimed at the problem that range-azimuth joint jamming can effectively generate high fidelity false targets in synthetic aperture radar (SAR) images, an anti-2D deceptive jamming method for multi-baseline interferometric SAR is proposed in this paper. The method consists of three steps: identification, classification and suppression. Firstly, the target is detected in the sea clutter background using constant false alarm rate algorithm, and then the false target is identified base on the difference of the spatial position of the target in different SAR images. Secondly, through the analysis of the generating principle of deceptive jamming, the false target is classified by multi-baseline interferometric SAR according to partial prior knowledge, which is designed based on the differences between the interferometric phase of the true and false target. Finally, the position of the active jammer is co-located underlying the phase equalization characteristics between multiple baselines and the geometric configuration of the SAR, and the adaptive beamforming method is supplied to suppress the jamming signal. The proposed method has a significantly improved ability to suppress jamming relative to interferometric phase cancellation. The effectiveness of the proposed method is verified by simulation.

G-Wishart Distribution in Multilook Polarimetric Whitening Filter and its Application

The polarimetric whitening filter (PWF) is widely used in constant false alarm rate (CFAR) ship detection in polarimetric synthetic aperture radar (PolSAR) imagery. The detection threshold plays a key role in the CFAR detection, which is generally determined by the statistical model of clutter. Many product models with different distributed textures are considered to fit the PWF output for an accurate threshold. Unfortunately, the product model with the general inverse Gaussian (<i>G</i>) texture, which can be named <i>G</i>-Wishart model according to the polarimetric covariance matrix, has not been well studied for its effective calculation. In this letter, the probability density function (PDF), the probability of false alarm (PFA), and the threshold in the CFAR algorithm based on the PWF are all obtained corresponding to the <i>G</i> distribution. The closed forms for the PDF and PFA are obtained with the Fox H function and its multivariate version, respectively. The threshold is derived by the bisection method when the false alarm rate (FAR) is constant. Finally, experimental results using both simulated and real data demonstrate that the different statistical models with the same log-cumulants can achieve almost the same CFAR loss.