Adaptive Constant False Alarm Rate Research Articles

The adaptive constant false alarm rate for sonar target detection based on back propagation neural network access

AbstractWith oceanic reverberation and a large amount of data being the main sources of interference for underwater acoustic target detection, it is difficult to obtain a more robust detection performance by relying on the traditional constant false alarm rate (CFAR) detection method. An adaptive sonar CFAR detection method based on a back propagation (BP) neural network is proposed. The method combines the artificial intelligence algorithm and the traditional detection algorithm, and uses the classification ability of the algorithm to select the detection algorithm, which can effectively improve the adaptation ability of the algorithm and the environment and the false alarm control ability. The method combines the artificial intelligence algorithm and the traditional detection algorithm, and uses the classification ability of the algorithm to select the detection algorithm, which can effectively improve the adaptation ability of the algorithm and the environment and the false alarm control ability. This method uses a BP neural network to train the target echo signal to complete the clutter background classification and establish the clutter background recognition classification set. According to the output result of each classification, the best CFAR detector is selected from four CA/SO/GO/OS‐CFAR detectors to detect the target. The simulation results show the detection performance of the proposed method in a uniform environment, a multi‐target environment, and a clutter edge environment. The results show that the environment adaptability is strong for different clutter backgrounds, which further improves the control ability of false alarms under a non‐uniform background.

Adaptive CFAR Method for SAR Ship Detection Using Intensity and Texture Feature Fusion Attention Contrast Mechanism.

Due to the complexity of sea surface environments, such as speckles and side lobes of ships, ship wake, etc., the detection of ship targets in synthetic aperture radar (SAR) images is still confronted with enormous challenges, especially for small ship targets. Aiming at the key problem of ship target detection in the complex environments, the article proposes a constant false alarm rate (CFAR) algorithm for SAR ship target detection based on the attention contrast mechanism of intensity and texture feature fusion. First of all, the local feature attention contrast enhancement is performed based on the intensity dissimilarity and the texture feature difference described by local binary pattern (LBP) between ship targets and sea clutter, so as to realize the target enhancement and background suppression. Furthermore, the adaptive CFAR ship target detection method based on generalized Gamma distribution (GΓD) which can fit the clutter well by the goodness-of-fit analyses is carried out. Finally, the public datasets HRSID and LS-SSDD-v1.0 are used to verify the effectiveness of the proposed detection method. A large number of experimental results show that the proposed method can suppress clutter background and speckle noise and improve the target-to-clutter rate (TCR) significantly, and has the relative high detection rate and low false alarm rate in the complex background and multi-target marine environments.

Experimental Study of Maritime Moving Target Detection Using Hitchhiking Bistatic Radar

Hitchhiking bistatic radar system takes the direct wave signal that is transmitted by the non-cooperative radar emitter as the reference to detect and analyze the target echo signal, so as to realize the positioning and tracking of the target. This radar system has the advantages of low cost and strong survivability. Aiming at the problem of passive radar to covert the detection of maritime targets, this paper develops a hitchhiking bistatic radar system for maritime target detection, which uses the shore-based radar as the non-cooperative radar emitter. By continuously collecting the direct wave and target echo data of the non-cooperative radar, the direct wave reference signal reconstruction, pulse compression, interference suppression and synchronization processing, non-coherent integration, MTI (moving target indication), clutter map processing, and adaptive CFAR (constant false alarm rate) detection are completed to obtain the azimuth, bistatic range, and Doppler frequency of the target, and finally realize the positioning of non-cooperative maritime targets. This paper first introduces and demonstrates the composition principle of the system, introduces the signal processing implementation method of the system in detail, and tests and analyzes the key algorithms. The experimental results show that the system can realize the passive coherent detection of maritime moving targets and locate multiple targets at the same time. The experiment obtains a very clear PPI (plane position indicator) display picture of the hitchhiking bistatic radar system, and the radar detection data of the experimental system is in good agreement with the AIS (automatic identification system) data.

A novel full-polarization SAR image ship detector based on scattering mechanisms and wave polarization anisotropy

Synthetic aperture radar (SAR) is suitable for earth observation due to its unique advantages. In this study, we constructed an adaptive ship detector using full-polarization SAR images. First, we thoroughly investigated the differences in scattering characteristics between ships, their background, and the wave polarization anisotropy. We then constructed a novel ship detector that incorporates scattering and anisotropy (known as joint scattering–anisotropy [joint-SA]). We found that joint-SA is an effective physical quantity representing the difference between the ship and its background; thus, joint-SA can be used for ship detection using full-polarization image data. Second, we used generalized gamma distribution to characterize joint-SA statistics of sea clutter with a large homogeneity range. Third, an adaptive constant false alarm rate (CFAR) method was implemented based on joint-SA. Finally, RADARSAT-2 and GF-3 data in the C-band and ALOS data in the L-band were used for verification. We tested five datasets, and the experimental results verified the correctness and superiority of the CFAR method based on joint-SA. The results show that the signal–clutter ratio of the proposed ship detector (33.17 dB, 35.98 dB, and 57.25 dB) was higher than that of DBSP (8.92 dB, 3.43 dB, and 25.40 dB) and RsDVH (17.28 dB, 11.17 dB, and 54.55 dB). Furthermore, the proposed detector has a higher detection accuracy and lower false alarm rate than those of the other two other methods.

Adaptive Superpixel-Level CFAR Detector for SAR Inshore Dense Ship Detection

Ship monitoring is an important application of synthetic aperture radar (SAR). The constant false alarm rate (CFAR) methods are commonly used for ship detection. However, CFAR detectors usually face challenges for inshore dense ship detection. Due to the significant mixture of ship candidates and sea clutters within the clutter window, the detection threshold may be overestimated leading to many missed detections. To mitigate this issue, a superpixel-level CFAR detector is proposed. The main contribution contains two aspects. First, a labeling procedure is established for pure clutter superpixels and mixture superpixels discrimination in terms of unsupervised clustering. Second, a nonlocal topology strategy is proposed to adaptively determine a sufficient number of pure clutter superpixels for detection threshold estimation. In this vein, an adaptive superpixel-level CFAR approach is constructed and validated with Radarsat-2, Sentinel-1, and AIRSARShip-1 data sets. Comparison studies demonstrate the superiority of the proposed method. Compared with a traditional CFAR detector and two recent superpixel methods, the proposed method achieves clearly better performance for inshore dense ship regions.

A Robust Acquisition Technique Using CFAR Adaptive Thresholding in Hybrid Spread Spectrum Systems

Acquisition schemes for hybrid spread spectrum (HSS) systems with a fixed threshold cannot provide satisfactory performance, due to the instantaneous changes of channel state information, environmental interferences, and mobility referred to as outliers. In these conditions, the received signal level varies rapidly. The fixed thresholding method (TM) can cause too many false alarms or the reduction of the detection probability for the selected threshold value. In this paper, we consider a robust acquisition method for the HSS systems using an adaptive constant false-alarm rate (CFAR) TM. In our proposed algorithm, the adaptive threshold is calculated based on the statistical analysis and average energy of the adjacent cells. We derive the exact closed-form solution of the acquisition time as a function of the detection probability, false-alarm probability, and the systemic parameters. Another issue is the extraction of the secure frequency-hopping pattern search period, which we examine in this paper. The theoretical analysis and simulation results show that the proposed algorithm can reduce the acquisition time by more than 45% and prevents the performance degradation in the outliers conditions without increasing hardware cost, compared to the conventional fixed TM.

Adaptive CFAR Detectors for Mismatched Signal in Compound Gaussian Sea Clutter With Inverse Gaussian Texture

This letter deals with the adaptive detection problem in compound Gaussian sea clutter with inverse Gaussian texture when the mismatched signal occurs. In order to reject the mismatched signal, we introduce a fictitious signal to the null hypothesis based on the adaptive beamformer orthogonal rejection test (ABORT). We adopt the two-step generalized likelihood ratio test (GLRT) criterion and develop two selective detectors, i.e., the ABORT-like compound Gaussian with inverse Gaussian (A-IGCG) detector and ABORT-like maximum <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a posteriori</i> (MAP) compound Gaussian with inverse Gaussian (AM-IGCG) detector. Both the proposed detectors are proved to be constant false alarm rate (CFAR) detectors. The several experiments that compare the proposed detectors with other selective detectors and compound Gaussian detectors are conducted by using two types of real sea data, i.e., IPIX 1998 and 2019 real sea data. The numerical results indicate that the novel detectors exhibit significant tradeoffs between performances of detection and selectivity property.

A new scattering similarity based metric for ship detection in polarimetric synthetic aperture radar image

A new paradigm for ship detection in polarimetric synthetic aperture radar (Pol-SAR) image is presented. We firstly utilize the scattering similarity parameters to investigate the differences of scattering mechanism between ships and sea clutter. Based on these differences, we propose a novel ship detection metric, denoted as the scattering similarity based metric (SSM), to conduct ship detection task. The distribution model of SSM metric is investigated and modeled by kernel density estimation (KDE). Based on the statistical distribution, an adaptive constant false alarm rate (CFAR) detection scheme is implemented. We compare the proposed SSM with two classic polarimetric metrics, i.e., the polarimetric cross-entropy (PCE) and the reflection symmetry metric (RSM). The experimental results conducted on C-band RADARSAT-2 Pol-SAR data demonstrate the feasibility and advantage of the proposed SSM metric both in sea clutter modeling and in ship detection.

Neural network-based adaptive selection CFAR for radar target detection in various environments

Constant false alarm rate (CFAR), a target detection method commonly used in the radar systems, has an inconsistent performance against various environments. For improving the radar detectability, this paper proposes a novel scheme of radar target detection using neural network-based adaptive selection CFAR. The proposed method employs cell-averaging, ordered-statistic, greatest-of and smallest-of CFAR thresholds as the basis of references. The pattern of those threshold values combined with the cell under test signal value will be identified and classified by the neural network to compute the raw threshold. Then, the final threshold is selected depending on the nearest value between raw and four referenced CFARs. The performance of the proposed method is examined against three possible cases of the radar systems including homogeneous background, multiple targets and clutter boundary. The result of this research shows that the proposed method outperforms the classical CFARs due to the adaptive selection algorithm can select properly among referenced CFARs against the given cases particularly in the homogeneous and multiple target environments.

Distributed Clutter-Map Constant False Alarm Rate Detection Using Fuzzy Fusion Rules

The performance of distributed adaptive clutter map constant false alarm rate (CMAP-CFAR) detection system using fuzzy fusion rules with homogeneous and non-homogeneous background is considered in this paper. We assume that the sensors are identical and the target is fluctuating according to Swerling I model embedded in a white Gaussian noise with unknown variance. Each detector computes the value of the membership function to the false alarm space from the previous samples of the cell under test and transmits it to the fusion center. These values are combined according to fuzzy fusion rules to produce a global membership function to the false alarm space. The obtained results showed that the best performance was obtained while using the “algebraic product” fuzzy rule and the probability of detection increases significantly with the number of detectors.

Centralized Adaptive CFAR Detection With Registration Errors in Multistatic Radar

The problem of centralized adaptive constant false alarm rate (CFAR) detection with registration errors in multistatic radar is considered. When the observation data from different radar sites are not properly aligned after transformed into a common coordinate system, registration errors occur. This can severely degrade the target detection performance and positioning accuracy of multistatic radar. In this paper, we focus on the design of adaptive detectors that are applicable for centralized target detection with registration errors in multistatic radar. A maximum likelihood estimation-based generalized likelihood ratio test detector and a maximum a posteriori estimation-based generalized likelihood ratio test detector are developed. Both detectors possess the CFAR property with regard to the unknown statistics of the background noise. Accordingly, the performance of the two proposed detectors are analyzed.

Adaptive Ship Detection in Hybrid-Polarimetric SAR Images Based on the Power–Entropy Decomposition

Based on its advantages, compact polarimetric (CP) synthetic aperture radar (SAR) is considered to be a good option for earth observations. This paper proposes an adaptive method of ship detection in CP SAR images operating in the hybrid-polarimetric (HP) mode. First, according to the analysis of scattering between ships and sea background, a novel decomposition approach, named power–entropy (PE) decomposition, is developed. Based on this approach, two components of the scattering power, the high-entropy scattering amplitude (HESA) and low-entropy scattering amplitude, are separated. We demonstrate that the HESA component is an effective physical quantity indicating the difference between the ship and its background and hence can potentially be used for ship detection using HP SAR data. The generalized Gamma distribution ( $\text{G}\Gamma \text{D}$ ) is found suitable for the characterization of HESA statistics of sea clutter with a wide range of homogeneity. As a result, the adaptive constant false-alarm rate (CFAR) technique based on the HESA detector is proposed. Experiments performed using HP measurements emulated from L-band ALOS-PALSAR and C-band RADARSAT-2 full polarimetric data validate the soundness and superiority of the proposed CFAR method based on the HESA detector. Both the theoretical proof and experimental results show that the HESA improves the ship–sea contrast (or the signal–clutter ratio) more than popular detectors, such as the entropy and SPAN. Moreover, the $\text{G}\Gamma \text{D}$ is a versatile model for the description of the statistical behavior of both the HESA and comparable detectors.

Detection Capability and CFAR Loss Under Fluctuating Targets of Different Swerling Model for Various Gamma Parameters in RADAR

Target detection of RADAR deals with different and manifold problems over few decades. The detection capability is one of the most significant factors in RADAR system. The main aim of detection is to increase probability of detection while decreasing rate of false alarm. The threshold of detection is modified as a function of the receiver noise level to keep a fixed rate of false alarm. Constant False Alarm Rate (CFAR) processors are used to maintain the amount of false alarm under supervision in a diverse background of interference. In Signal to Noise Ratio (SNR) level, a loss can be occurred due to CFAR processor. Gamma function is used to determine the probability of false alarm. It is assumed in adaptive CFAR that the interference distribution is familiar here. This type of CFAR also approximates the unknown parameters connected with various interference distributions. CFAR loss depends on gamma function. Incomplete gamma function plays an important role in maintaining threshold voltage as well as probability of detection. Changing the value of gamma function can improve the probability of detection for various Swerling Models which are proposed here. This paper has also proposed a technique to compare various losses due to CFAR in terms of different gamma function in presence of different number of pulses for four Swerling Models.

Classification of Radar Environment Using Ensemble Neural Network with Variation of Hidden Neuron Number

Target detection is a mandatory task of radar system so that the radar system performance is mainly determined by its detection rate. Constant False Alarm Rate (CFAR) is a detection algorithm commonly used in radar systems. This method is divided into several approaches which have different performance in the different environments. Therefore, this paper proposes an ensemble neural network based classifier with a variation of hidden neuron number for classifying the radar environments. The result of this research will support the improvement of the performance of the target detection on the radar systems by developing such an adaptive CFAR. Multi-layer perceptron network (MLPN) with a single hidden layer is employed as the structure of base classifiers. The first step of this research is the evaluation of the hidden neuron number giving the highest accuracy of classification and the simplicity of computation. According to the result of this step, the three best structures are selected to build an ensemble classifier. On the ensemble structure, all of those three MLPN outputs then be collected and voted for getting the majority result in order to decide the final classification. The three possible radar environments investigated are homogeneous, multiple-targets and clutter boundary. According to the simulation results, the ensemble MLPN provides a higher detection rate than the conventional single MLPNs. Moreover, in the multiple-target and clutter boundary environments, the proposed method is able to show its highest performance.

Vibration detection method for optical fibre pre‐warning system

The measurement of optical fibre vibration is a key part of optic fibre pre-warning system, which has gradually focused on phase-sensitive optical time-domain reflectometer. However, for this instrument, false alarm rate is very high and some unstable intrusion signals cannot be detected by using its fixed threshold method in the actual application. It needs to develop new vibration detection method to overcome the above defect. The vibration signals normally consist of three parts, that is, noise, interference and intrusion signals. After a large number of data analysis, the authors find that the system noise is time varying and follows the Rayleigh distribution. Hence, the authors innovatively use the constant false alarm rate (CFAR) method to detect this type of intrusion. Considering interference is also time varying and diverse, a good detection performance cannot be obtained only by using the conventional CFAR. For this reason, a background homogeneity adaptive CFAR (BHA-CFAR) method is further proposed to detect the vibration signals in this study. The BHA-CFAR consists of two detectors, cell averaging CFAR (CA-CFAR) detector and greatest-of/smallest-of CFAR (GO/SO-CFAR) detector. A parameter, homogeneity of background, is estimated first to classify the surrounding. Then CA-CFAR and GO/SO-CFAR are optionally used according to the surrounding is homogeneous or heterogeneous, respectively. This new detection method can adapt to any background surrounding and has a good detection performance. In order to check the feasibility and validity of the BHA-CFAR method, several experiments were carried out in Da Gang oilfield. The detection results show that the proposed method can provide a good tradeoff between the detection performance and computation time.

Statistical Modeling of PMA Detector for Ship Detection in High-Resolution Dual-Polarization SAR Images

The product of multilook amplitudes (PMA) detector has been used to detect ships in high-resolution dual-polarization synthetic-aperture-radar images. However, the adaptive constant false-alarm rate (CFAR) technique of the PMA detector is desirable for practical applications, wherein a crucial problem is to find an appropriate model to describe the PMA statistics for varied sea surfaces. First, we consider a new probability density function to characterize the PMA statistics of homogeneous sea surfaces. Second, by using the new density and multiplicative model, the PMA detector's statistical model for nonhomogeneous sea surfaces is specified and demonstrated to be the $\mathcal{G}^{0}$ distribution. Then, a theoretical analysis of the relationship between the performance of the standard CFAR detection and the parameters in the $\mathcal{G}^{0}$ distribution is conducted. Experiments performed on the measured RADARSAT-2 and NASA/JPL AIRSAR images verify the effectiveness and appropriateness of the $\mathcal{G}^{0}$ model for describing the statistical behavior of the PMA of sea clutter, as well as the usefulness of the model for practical ship-detection applications.

Performance Analysis of an Environmental Adaptive CFAR Detector

The constant false alarm rate (CFAR) is a detection algorithm that is generally used in radar or sonar systems, but its performance depends greatly on the environment. This means that the detection performance cannot be satisfied with only a single CFAR detector. This paper evaluates mathematically a proposed environmental adaptive (EA) CFAR detector. The proposed CFAR detector selects an optimal CFAR detector depending on the environment. Computer simulations validate the mathematical analysis and robustness of the detector in homogeneous and nonhomogeneous backgrounds.

Fast Two-Dimensional CFAR Procedure

An FMCW radar system, which transmits a sequence of rapid chirp signals, is considered to measure target range and radial velocity simultaneously even in multitarget situations. The main outcome of the coherent processing procedure applied to the received echo signal is the two-dimensional range-Doppler-matrix (RDM), which is the basis for an adaptive constant false alarm rate (CFAR) target detection technique. The well-known one-dimensional cell averaging (CA) CFAR procedure suffers from masking effects in all multitarget situations and is additionally very limited in the proper choice of the reference window length. In contrast the ordered statistic (OS) CFAR is very robust in multitarget situations but requires a high computation power. Therefore two-dimensional CFAR procedure based on a combination of OS and CA CFAR is proposed. In this case any masking effects in multitarget situations can be avoided without increasing the computation complexity. In addition the size of the reference window can be adjusted in order to optimize the detection performance.

UWB RADAR based Modified Adaptive CFAR Algorithm for improved safety of Personal Rapid Transit

Personal Rapid Transit(PRT) is a new unmanned transportation system using electricity. The purpose of the PRT is relieving the congestion of city traffic and connecting between inner city and airport, high-speed railroad. PRT requires to develop devices for the guarantee of safety and reliability. PRT as the mean of rail transportaion must be equipped with control system for front rail sensing. Ultra Wide Band(UWB) radar system is suitable for PRT’s detection because it has the advantage of low power consumption, low interference and high resolution. In this paper, an improved adaptive Constant False Alarm Rate(CFAR) algorithm is proposed and studied in various noise environments. The proposed algorithm improves performance in various noise environments compared to the Mean Level CFAR algorithms and other adaptive CFAR algorithms.

A HOMOGENOUS REFERENCE CELLS SELECTOR FOR CFAR DETECTOR IN HIGHLY HETEROGENEOUS ENVIRONMENT

This paper considers the radar scenes which contain numerous rapidly changing terrains, i.e., there are more than one clutter-edge in the environment. This kind of radar scenes incurs sharply degradation in the performance of the present adaptive constant false alarm rate (CFAR) detectors as the statistical characteristic of reference cells is highly heterogeneous. To solve this problem, we propose a homogenous reference cells selector to improve the performance of CFAR detector in highly heterogeneous environment. The selector is comprised of an M-N clutter-edge detector cascading a terrain classifler. The M-N clutter-edge detector is used to obtain multiple clutter-edges in heterogeneous environment. With the detected clutter-edges, the terrain classifler is derived to obtain identical distributed range cells. Based on the selector, a modifled Log-t-CFAR detector is suggested. Finally, the performance of the proposed selector and CFAR detector is evaluated by measured data and computer simulation.