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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. [ABSTRACT FROM AUTHOR]

The performance of marine radar target detection is largely affected by the intricate and dynamic space–time variations of sea clutter signals, which cause substantial numbers of false and missed alarms. To improve the target detection performance of rotating scanning marine radar, this study proposes a marine radar image sequence target detection algorithm based on space–time adaptive filtering and the Hough transform algorithm. The algorithm adopts a two-stage approach of coarse detection followed by precise detection. During the coarse detection stage, the sea clutter energy in the 3-D frequency–wavenumber spectrum of the marine radar image sequence is suppressed by a sea clutter suppression algorithm in the space–time domain, space–time clutter suppression (STCS). Subsequently, moving targets are extracted from the image sequence using a target energy extraction method based on the Hough transform algorithm in the 3-D frequency–wavenumber domain. The result is a processed image sequence with sea clutter signal reduction and target signal extraction. The precise detection stage detects the target point in this processed image sequence using a constant false alarm rate method based on a real clutter background distribution model. During verification tests on real X-band marine radar data, the detection probability of the proposed method reaches 99.89% under low sea state, 95.34% under medium sea state, and 94.44% under high sea state. Compared with the WHOS-CFAR and GMOS-CFAR, the average improvement is 10.1% and 16.6%, respectively. Furthermore, compared to the STCS, there is a maximum improvement of 3.7%. The enhancement in detection performance is significant.

Diamond Nitrogen vacancy (NV) color center-based weak magnetic field detection is a popular topic for quantum sensing. The primary method of NV color center magnetic field measurement is the optical detection magnetic resonance (ODMR) method, including three main steps laser polarization, microwave excitation, and fluorescence signal collection. Then the intensities of the fluorescence signals are outputted as an ODMR curve where the magnetic field parameters can be directly observed. However, microwave excitation requires using a large number of frequencies by scanning the frequencies in a specific range, resulting in high complexity and low efficiency. Less than 50% of the microwave excitations are useful, and the rest works barely help to measure. The reason is that only the resonance peaks contain the magnetic field information, so only the microwave frequencies around the resonance peaks need to be excited. Therefore, we consider exciting only a few frequency microwaves. This paper proposes a random frequency microwave excitation method based on the Lorentz function fitting reconstruction, referred to as the under-sampling reconstruction based ODMR (USR-ODMR). It only excites several microwave frequencies randomly to produce the ODMR curve. The main idea of USR-ODMR is to select proper frequencies from random frequencies by employing the constant false alarm rate-based threshold. At last, a diamond NV color center magnetometer is implemented and tested to obtain ODMR curves. Experimental results show that the USR-ODMR can accurately reconstruct the ODMR curves with much lower complexity than the traditional ODMR method. [ABSTRACT FROM AUTHOR]

In cognitive radio networks, Spectrum sensing is most important task for avoiding the unacceptable interference to primary users. The performance of spectrum sensing is based on the threshold value used in the sensing scheme. The existing co-variance based spectrum sensing technique use a fixed threshold which does not ensure sufficient protection to Primary users. Hence, Co-variance based adaptive threshold spectrum detection optimized with Chameleon Swarm optimization for Optimum threshold selection in Cognitive Radio Networks is proposed for appropriate security to the primary user and also reduces the total error probability. In this, the threshold selection is based on constant false alarm rate (CFAR) principles and constant detection rate (CDR) principles. Besides these principles the threshold can be derived by reducing the total probability of decision error. Hence co-variance based adaptive threshold detection (CATD) process is proposed to obtain the adaptive threshold for minimizing the total error probability. But the performance of this process weakens at low Signal to Noise Ratio. To overcome these issues, the optimized selection of threshold is needed. Hence Chameleon swarm optimization algorithm (CSOA) is proposed to enhance the performance of CATD process by selecting an optimal threshold value. The simulation process is executed in the MATLAB platform. The proposed Co-variance based adaptive threshold spectrum detection optimized with Chameleon Swarm optimization (CATD-CSOA) attains low error probability 38.5%, high detection probability 98.6, and high throughput 94.35% when comparing with the existing method such as Optimum threshold selection in Spectrum sensing based Adaptive Covariance-based Detection Algorithm (ACDA) and Optimum threshold selection in Spectrum sensing based CUSUM (Cumulate Sum) Algorithm. Finally, the proposed CATD-CSOA produces high detection probability with limited number of samples as well as threshold is selected in an optimized way. [ABSTRACT FROM AUTHOR]

Abstract This paper concerns direction of arrival (DOA) estimation based on a sparse Bayesian learning (SBL) approach. We address two inherent problems of this class of DOA estimation methods: (i) a predefined dictionary can generate off-grid problems to a SBL DOA estimator; (ii) a parametric prior generally enforces the solution to be sparse, but the existence of noise can greatly affect the sparsity of the solution. Both of these issues may have a negative impact on the estimation accuracy. In this paper, we propose an improved root SBL (IRSBL) method for off-grid DOA estimation that adopts a coarse grid to generate an initial dictionary. To reduce the bias caused by dictionary mismatch, we integrate the polynomial rooting approach into the SBL method to refine the spatial angle grid. Then, we integrate a constant false alarm rate rule in the SBL framework to enforce sparsity and improve computational efficiency. Finally, we generalize the IRSBL method to the case of non-uniform linear arrays. Numerical analysis demonstrates that the proposed IRSBL method provides improved performance in terms of both estimation accuracy and computational complexity over the most relevant existing method.

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The goal of this paper is to improve the detection performance and the false alarm regulation of the conventional order statistics Constant False Alarm Rate (OSCFAR) detectors in a non-homogeneous Gaussian environment. To this end, we design and study the New Sorting Weighting (NSW-) and the Modified Sorting Weighting (MSW-) CFAR detectors. We find closed forms of the detection (Pd) and the false alarm (Pfa) probabilities for both detectors. Moreover, we identify the optimum pairs of weights that maximize the Pd and ensure a constant Pfa. Finally, we prove through Monte Carlo simulations that these detectors provide better detection performance and false alarm regulation than the order statistics conventional ones in various clutter situations with the NSW-CFAR detector being the best one. [ABSTRACT FROM AUTHOR]

This paper proposes a system that will guard infrastructures against incoming threats from drones by detecting it with the use of a radar device-based detection scheme. The database acquired, named Real Doppler RAD-DAR (Radar with Digital Array Receiver) is constructed by a Microwave and Radar Group. The radar used uses a Frequency Modulated Continuous Wave (FMCW) on an 8.75 GHz based frequency band with a BWmaxof 500 MHz. The proposed Convolutional Neural Network (CNN), CNN-32DC is varied with different number of filters, combination layers, and number of feature extraction blocks, the preference that will give the most accurate result was selected and compared with different machine learning and classification learning algorithms gained an accuracy that exceeds other networks with less processing time.

In this paper, we consider the adaptive detection problem of range-spread targets embedded in subspace interference plus structured Gaussian clutter. The target signal and interference are assumed to lie in two linearly independent subspaces with unknown coordinates. The clutter component is modeled as a complex Gaussian vector with an unknown persymmetric covariance matrix. We leverage the persymmetric structure to design a two-step detector according to the Rao test criterion. The theoretical results show that the proposed detector possesses the constant false alarm rate property with respect to the clutter covariance matrix. Furthermore, the numerical results show that the proposed detector exhibits better detection performance than the existing unstructured subspace detectors, particularly under a limited training data size. In addition, the proposed detector outperforms the existing persymmetric subspace detectors. [ABSTRACT FROM AUTHOR]

This paper concerns direction of arrival (DOA) estimation based on a sparse Bayesian learning (SBL) approach. We address two inherent problems of this class of DOA estimation methods: (i) a predefined dictionary can generate off-grid problems to a SBL DOA estimator; (ii) a parametric prior generally enforces the solution to be sparse, but the existence of noise can greatly affect the sparsity of the solution. Both of these issues may have a negative impact on the estimation accuracy. In this paper, we propose an improved root SBL (IRSBL) method for off-grid DOA estimation that adopts a coarse grid to generate an initial dictionary. To reduce the bias caused by dictionary mismatch, we integrate the polynomial rooting approach into the SBL method to refine the spatial angle grid. Then, we integrate a constant false alarm rate rule in the SBL framework to enforce sparsity and improve computational efficiency. Finally, we generalize the IRSBL method to the case of non-uniform linear arrays. Numerical analysis demonstrates that the proposed IRSBL method provides improved performance in terms of both estimation accuracy and computational complexity over the most relevant existing method. [ABSTRACT FROM AUTHOR]

In this study, a new adaptive detector that can resist multi-target interference was proposed to improve the resistance to multi-target interference when using the constant false alarm rate(CFAR) method. This detector uses reference units as the feature value of the background environment and the TreeBagger algorithm for the construction of the estimator. In the training process, the reference units and TreeBagger algorithm were first used to construct the estimator, which was used to estimate the number of interference targets. In the detection process, the reference units were then used as the inputs of the estimator and the number of interference targets in the current background as the output of the estimator. Furthermore, the estimation results were used as the sequence threshold for the detector. Consequently, the detector was able to eliminate the interference targets and complete detection. The performance of the detector under homogeneous reverberation and multi-target interference backgrounds was then analyzed using the Monte Carlo simulation method, and a comparison of the results with those of existing methods was conducted, which showed that the proposed detector had a better performance at resisting multi-target interference.

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 μ s 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. [ABSTRACT FROM AUTHOR]

In this study, a scheme for leak localization on a cylinder tank bottom using acoustic emission (AE) is proposed. This approach provides a means of early failure detection, thus reducing financial damage and hazards to the environment and users. The scheme starts with the hit detection process using a constant false alarm rate (CFAR) and a fixed thresholding method for a time of arrival (TOA) and an end-time determination. The detected hits are then investigated to group those originating from the same AE source together by enforcing an event definition and a similarity score. Afterwards, these newly grouped hits are processed by a time difference of arrival (TDOA) to find the locations of the events. Since the locations of the events alone do not pinpoint the leak location, a data density analysis using a Voronoi diagram is employed to find the area with the highest possibility of a leak's existence. The proposed method was validated using the Hsu-Nielsen test on a cylinder tank bottom under a one-failed-sensor scenario, which returned a highly accurate result across multiple test locations. [ABSTRACT FROM AUTHOR]

Interference is a key factor in radar return misdetection. Strong interference might make it difficult to detect the signal or targets. When interference occurs in the sidelobes of the antenna pattern, Sidelobe Cancellation (SLC) and Sidelobe Blanking are two unique solutions to solve this problem (SLB). Aside from this approach, the probability of false alert and likelihood of detection are the most essential parameters in radar. The chance of a false alarm for any radar system should be minimal, and as a result, the probability of detection should be high. There are several interference cancellation strategies in the literature that are used to sustain consistent false alarms regardless of the clutter environment. With the necessity for interference cancellation methods and the constant false alarm rate (CFAR), the Maisel SLC algorithm has been modified to create a new algorithm for recognizing targets in the presence of severe interference. The received radar returns and interference are simulated as non-stationary in this approach, and sidelobe interference is cancelled using an adaptive algorithm. By comparing the performance of adaptive algorithms, simulation results are shown. In a severe clutter situation, the simulation results demonstrate a considerable increase in target recognition and signal to noise ratio when compared to the previous technique. [ABSTRACT FROM AUTHOR]

A power transform-based clutter map constant false alarm rate (CM/PT-CFAR) algorithm is proposed to improve the detection performance to the weak target in multiple persisting targets situations. In the CM/PT-CFAR detector, the radar dataset obtained at each scan is normalized and multiplied by a scale factor, and then fed to the power transform operation. The transformed dataset is divided into two parts with a numerical value of 1 as the boundary. The part exceeding 1 is fed to update the scale factor, while the other is used for updating the detection threshold. Because transcendental integrals are produced in the derivation process, an accurate analytical expression of detection probability for CM/PT-CFAR is non-existent. Hence a third-order Taylor expansion operation is introduced to approximate the result. The detection performance of CM/PT-CFAR under various conditions is evaluated and compared with those of other CM/-CFAR detectors. The advantage of CM/PT-CFAR is the detection for weak targets, especially in complex detection situations. The proposed algorithm remains a relatively stable computation load in different cases, which is beneficial for practical application.

Abstract A new reduced‐rank (RR) space‐time adaptive processing (STAP) algorithm based on multistage selections of angle‐Doppler filters is proposed in the form of a generalised sidelobe canceller. First, two types of the RR auxiliary angle‐Doppler filters are designed based on the discrete Fourier basis functions. Then, a novel multistage method is implemented to select the angle‐Doppler filters by using the cross‐correlation observed at the current stage and the residual output observed at the previous stage. Finally, the asymptotic statistic performance of target detection is evaluated in terms of the conventional framework of STAP for the cases of both known and unknown covariance, which shows that the proposed algorithm is capable of providing the constant false alarm rate. Numerical examples are provided, and they demonstrate that the proposed algorithm is able to offer better performance than the existing sparsity‐based and the conventional reduced‐dimension algorithms under the intrinsic clutter motion and array gain and phase errors.

The constant false alarm rate (CFAR) has been widely used in radar systems to detect target echo signals because of its simplicity. With the recent development of different types of neural networks (NNs), NN architecture-based target detection methods are also being considered. Several studies related to NN-based target detectors have introduced multi-layer perceptron-based and convolutional neural network (CNN)-based structures. In this paper, we propose a CNN-based target detection method in frequency modulation (FM)-band passive coherent location (PCL). We improved the detection performance using a maxpooling layer and a Hadamard division layer, which are parallelly placed with a CNN layer. Moreover, in our method there is no need to determine the specific cell configuration (e.g., cell under test, reference cells, and guard cells) because the proposed method obtains the trained kernels by end-to-end learning. We show that the trained kernels help in the extraction of either signal or noise components. Through the simulations, we also prove that the proposed method can yield an improved receiver operating characteristic compared to that of a cell-averaging CFAR detector for FM-band PCL in a homogeneous environment.

Different from ground-based or airborne early warning radar, space-based radar (SBR) possesses large coverage capability. As a result, several discrete strong scatter points from the antenna side-lobe shares the same feature with the real targets in range-Doppler domain, which leads to false alarms when conducting constant false alarm rate (CFAR) detection process, and the detection performance with regard to SBR deteriorates seriously. In this paper, a discrete side-lobe clutter recognition method based on sliding filter response loss is proposed for space-based radar. Firstly, considering both the echo inhomogeneity and the limited degrees of freedom (DOFs) after dimension-reduced space-time adaptive processing (STAP), the sliding window design strategy is employed to segment range cells for the observation scene. Then, the images related to different range segments are registered after clutter suppression, in this way, the candidate target parameters, including the position information and the amplitude information are counted. On this basis, the reliable recognition scheme between the real target and the discrete side-lobe clutter can be realized by comparing these filter response losses. Compared with recent works, experimental results based on real measured data show that the proposed method significantly improves the fault-tolerant discrimination ability, which possesses high robustness in algorithm performance as well as good prospect in engineering application.

Detection performance evaluation is one of the inevitable problems for marine wireless sensor networks (MWSNs) deployed for target detection. However, it is a very complicated problem since it associates many different aspects, such as emitter power, range, radar cross-section, weather, geography, working mode, and so on. Targeting this problem, this paper incorporates the Poisson point process model into describing the ranges from sensors to targets. The relationship between sensors and a target is built from the perspective of detection probabilities. Then, a new consistent, conservative target detection probability evaluation is derived within a CFAR framework, and the further global detection probability of the whole MWSN on the target is developed. Additionally, the rationality of this modeling approach is demonstrated via simulation results, which is in accord with the actual situation. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of distributed target detection in partially homogeneous Gaussian clutter whose covariance matrix is unknown but persymmetric. It is assumed that primary data and training data share the same clutter covariance matrix structure but different power levels. The target signal is supposed to lie in a multi‐rank subspace with unknown coordinates. A persymmetric subspace detector is designed based on the generalised likelihood ratio test criteria. It is theoretically demonstrated that the proposed detector possesses constant false alarm rate property with respect to the unknown clutter covariance matrix as well as the power level. Experimental results illustrate the performance advantage of the proposed detector over the existing competitors, especially in training‐limited scenarios. [ABSTRACT FROM AUTHOR]

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 $p$ and the calculation of the OP operator ψx⊥ ${\boldsymbol{\psi }}_{x}^{\perp }$. 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. [ABSTRACT FROM AUTHOR]

Efficient and precise calculation of the probability of false alarms for the K-distributed sea clutter and noise is crucial in radar detection system. We propose efficient and optimal importance sampling algorithms, in which two importance sampling estimators are constructed for both the product form and the compound form of the underlying K distribution. Moreover, we prove the existence and uniqueness of the optimal tilting parameters. Detection accuracy and efficiency are analyzed in simulation studies. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

To address the problem of detecting point-like targets in a partially homogeneous Gaussian cluttered environment, we developed a modified Generalized Likelihood Ratio Test (GLRT) detection method based on a symmetrically spaced linear array that relies on a GLRT design criterion. Considering the target energy spillover during sampling, we use a spillover model of the target energy to decrease spillover loss. To establish the discrete-time signal mode, we use a persymmetric structure of the disturbance covariance matrix to reduce the requirement for auxiliary signals. Lastly, we estimate all of the unknown parameters based on a consideration of both primary and secondary data to derive the persymmetric modified GLRT detector, which has good target detection and range estimation performance. The performance assessment shows that the proposed method not only performs as a constant false-alarm-rate receiver in partially homogeneous environments but also guarantees superior detection performance relative to that of its competitors. In sample-starved environments, compared with other detection methods of the same type, it realizes a detection performance advantage greater than 1 dB.

A power transform-based clutter map constant false alarm rate (CM/PT-CFAR) algorithm is proposed to improve the detection performance to the weak target in multiple persisting targets situations. In the CM/PT-CFAR detector, the radar dataset obtained at each scan is normalized and multiplied by a scale factor, and then fed to the power transform operation. The transformed dataset is divided into two parts with a numerical value of 1 as the boundary. The part exceeding 1 is fed to update the scale factor, while the other is used for updating the detection threshold. Because transcendental integrals are produced in the derivation process, an accurate analytical expression of detection probability for CM/PT-CFAR is non-existent. Hence a third-order Taylor expansion operation is introduced to approximate the result. The detection performance of CM/PT-CFAR under various conditions is evaluated and compared with those of other CM/-CFAR detectors. The advantage of CM/PT-CFAR is the detection for weak targets, especially in complex detection situations. The proposed algorithm remains a relatively stable computation load in different cases, which is beneficial for practical application. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Medical Equipment Journal is the property of Chinese Medical Equipment Journal Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A new reduced‐rank (RR) space‐time adaptive processing (STAP) algorithm based on multistage selections of angle‐Doppler filters is proposed in the form of a generalised sidelobe canceller. First, two types of the RR auxiliary angle‐Doppler filters are designed based on the discrete Fourier basis functions. Then, a novel multistage method is implemented to select the angle‐Doppler filters by using the cross‐correlation observed at the current stage and the residual output observed at the previous stage. Finally, the asymptotic statistic performance of target detection is evaluated in terms of the conventional framework of STAP for the cases of both known and unknown covariance, which shows that the proposed algorithm is capable of providing the constant false alarm rate. Numerical examples are provided, and they demonstrate that the proposed algorithm is able to offer better performance than the existing sparsity‐based and the conventional reduced‐dimension algorithms under the intrinsic clutter motion and array gain and phase errors. [ABSTRACT FROM AUTHOR]

Multichannel adaptive signal detection uses test and training data jointly to form an adaptive detector to determine whether a target exists. The resulting adaptive detectors typically possess constant false alarm rate (CFAR) properties; thus, no additional CFAR processing is required. In addition, a filtering process is also not required because the filtering function is embedded in the adaptive detector. Adaptive detection typically exhibits better detection performance than the filtering-then-CFAR detection technique. It has been approximately 35 years since the first multichannel adaptive detector was proposed by Kelly in 1986. However, there are few overview articles on this topic. Thus, in this study, we present a tutorial overview of multichannel adaptive signal detection with an emphasis on the Gaussian background. We discuss the main design criteria for adaptive detectors, investigate the relationship between adaptive detection and filtering-then-CFAR detection techniques, investigate the relationship between adaptive detectors and adaptive filters, summarize typical adaptive detectors, present numerical examples, provide a comprehensive literature review, and discuss potential future research tracks. [ABSTRACT FROM AUTHOR]

Copyright of Journal of East China University of Science & Technology is the property of Journal of East China University of Science & Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aiming at the problem that the accuracy of traditional ship detection algorithms is not satisfying in complex scene with many false alarm targets,a ship detection optimization method in SAR imagery based on multi-feature weighting was proposed.Firstly,the marker-based watershed algorithm was employed to remove land from SAR amplitude image.Then,the CFAR algorithm based on log-normal distribution was used to obtain candidate targets from no land image.Furthermore,the length to width ratio,the ship area and the contrast ratio of the candidate targets were extracted.Finally,a variance coefficient method was proposed to distribute the weight of the three features,and the confidence levels were calculated by combining the normalized feature vectors of the candidate targets with the feature weight.By determining the best confidence level,false alarm targets among the candidate targets were removed to optimize ship detection results.In order to verify the proposed method,experiments were carried on with the GF-3 SAR images of different complex scenes.The experimental results show that the proposed method is feasible and effective.

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.

In this study, a scheme for leak localization on a cylinder tank bottom using acoustic emission (AE) is proposed. This approach provides a means of early failure detection, thus reducing financial damage and hazards to the environment and users. The scheme starts with the hit detection process using a constant false alarm rate (CFAR) and a fixed thresholding method for a time of arrival (TOA) and an end-time determination. The detected hits are then investigated to group those originating from the same AE source together by enforcing an event definition and a similarity score. Afterwards, these newly grouped hits are processed by a time difference of arrival (TDOA) to find the locations of the events. Since the locations of the events alone do not pinpoint the leak location, a data density analysis using a Voronoi diagram is employed to find the area with the highest possibility of a leak’s existence. The proposed method was validated using the Hsu-Nielsen test on a cylinder tank bottom under a one-failed-sensor scenario, which returned a highly accurate result across multiple test locations.

We consider a modern radar system which has a general antenna array configuration consisting of primary channels with high-gain beams and reference channels with low-gain beams. A generalized likelihood ratio test (GLRT) was proposed in this context for target detection and direction of arrival estimation. However, the analytical performance of the GLRT has not been provided yet. In this paper, we derive finite-sum expressions for the probabilities of false alarm and detection. It is proven that the GLRT exhibits a constant false alarm rate against the overall interference disturbance covariance matrix. Numerical examples show that the detection performance of the GLRT may not necessarily improve when the number of sensors increases [ABSTRACT FROM AUTHOR]

In this paper, we develop a new elegant systematic framework relying on the Kullback-Leibler Information Criterion to approach the design of one-stage adaptive detection architectures for multiple hypothesis testing problems in radar. Specifically, at the design stage, we assume that one out of several alternative hypotheses may be in force and that only one null hypothesis exists. Then, starting from the case where all the parameters are known and proceeding towards the adaptivity with respect to the entire parameter set, we come up with decision schemes for multiple alternative hypotheses consisting of the sum between the compressed log-likelihood ratio based upon the available data and a penalty term accounting for the number of unknown parameters. Such a term arises from suitable approximations of the Kullback-Leibler Divergence between the true and a candidate probability density function. Interestingly, under specific constraints, the proposed decision schemes can share the constant false alarm rate property by virtue of the Invariance Principle. Finally, we also show that the new architectures can be viewed as the result of a suitable regularization of the log-likelihood. [ABSTRACT FROM AUTHOR]

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 autonomous underwater vehicle (AUV) can operate automatically with a small number of humans and requires few resources when compared to a remotely operated vehicle (ROV). The pipeline inspection with the use of an AUV can be significantly cheaper than those of an ROV. Successful deployment of AUVs relies on accurate pipeline detection and extraction that can automatically locate and track a pipeline direction in real time. We proposed to use forward-looking sonar images to locate and extract pipeline paths since sonar can easily image through turbulence and small particles, commonly found in underwater environments. However, sonar images are prone to disturbance from the environment. Thus, as a superior alternative, we developed a pipeline detection algorithm by considering that a pipeline has a piecewise linear shape. Next, the self-organizing map (SOM) is employed to join the labeled segments together to form a pipeline track to extract the pipeline path for the navigation of an AUV since SOM can map a 2-D image of pipeline into a 1-D line. In our experiment, data obtained from the Gulf of Thailand are used for the performance analysis, and we are able to extract and describe a pipeline direction with accuracies between 90% and 97%. [ABSTRACT FROM AUTHOR]

In this article, a target detection problem in homogeneous Gaussian noise with unknown covariance matrix is examined using multiple observations, which may be collected from multiple range cells, bands, and/or coherent processing intervals. In order to take into consideration mismatches of the target steering vector, we adopt a subspace model where the target steering vector is assumed to lie in a subspace spanned by the column vectors of a known matrix with unknown target coordinates. By exploiting persymmetric structures, we propose several adaptive detectors according to ad hoc modifications of generalized likelihood ratio test (GLRT), Rao test, and Wald test. It is found that the Rao test does not exist, whereas the two-step Wald test shares the same form as the two-step GLRT. Numerical examples show that the robustness of the proposed detectors is better than that of their counterparts in general. In particular, the proposed one-step GLRT is the most robust in most cases, and the proposed two-step GLRT and one-step Wald test can be more robust than the one-step GLRT in the case where the number of training data is small. [ABSTRACT FROM AUTHOR]

This paper studies adaptive radar detection of a subspace signal embedded in two disturbance sources. The former is thermal noise with known power. The latter is a Gaussian subspace interference with zero mean and unknown covariance matrix (CM). It is assumed that the signal and interference subspaces are known and partially related. As customary, several secondary data containing only interference and thermal noise are used to estimate this interference CM. This paper derives the generalized likelihood ratio test (GLRT), and theoretically deduces the probabilities of false alarm (PFA) and detection of the new detector. This PFA shows that the new detector has the constant false alarm rate (CFAR) property against the interference CM. Several numerical experiments are performed to evaluate the detection performance of the new detector. The results show that the performance of the new detector is better than the natural counterparts in some scenarios. [ABSTRACT FROM AUTHOR]

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With the rapid growth of information and communication technologies, the number of security threats in computer networks is substantially increasing; thus, the development of more proactive security warning measures is required. In this work, we propose a new anomaly detection method that operates by decomposing TCP traffic into control and data planes, which exhibit similar behaviors in the absence of attacks. The proposed method exploits the statistics of the cross-correlation function of the two planes traffic and the constant false alarm rate (CFAR) scheme for detecting anomalies of the underlying network traffic. Both the fixed and adaptive thresholding schemes are implemented. The adaptive thresholding is setup by adjusting the value of the threshold in accordance with the local statistics of the cross-correlation function of the two planes traffic. We evaluate the performance of the proposed method by analyzing the real traffic captured from a deployed network and traffic obtained from other publicly available datasets; we focus on TCP traffic with three different aggregated count features: packet count, IP address count, and port count sequences. Although both the fixed and adaptive thresholding schemes perform well and detect the presence of a distributed denial-of-service efficiently. The adaptive thresholding scheme is more reliable because it detects anomalies as they start. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel detection architecture in a through-wall environment using impulse radio ultra-wideband(IR-UWB) radar. In the proposed architecture, multiple moving target indicators and constant false alarm rate(CFAR) detectors are combined to detect both stationary and moving targets. Each detector is designed for the state of targets; in particular, the modified CFAR method is applied to detect fast-moving targets precisely. The outputs of the CFAR detectors are combined through a logical operation to achieve high detection rates for the targets in different movement. This fusion can be used not only to achieve a high detection rate but also increase the false alarm rate. The characteristics of the signals detected on the CFAR are analyzed to reduce the high false alarm rate by classifying the actual target and false target. Experiments were performed for different scenarios of multiple targets using various movements. It is verified that the proposed algorithm has improved performance in terms of both detection rate and false alarm rate compared with conventional approaches, such as cell averaging CFAR and ordered statistics CFAR. [ABSTRACT FROM AUTHOR]