Constant False Alarm Rate Research Articles

Off-grid DOA estimation using improved root sparse Bayesian learning for non-uniform linear arrays

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.

A CFAR Detection Algorithm Based on Clutter Knowledge for Cognitive Radar

Under the background of non-homogenous and dynamic time-varying clutter, the processing ability of the traditional constant false alarm rate (CFAR) detection algorithm is significantly reduced, as well as the detection performance. This paper proposes a CFAR detection algorithm based on clutter knowledge (CK-CFAR), as a new CFAR, to improve the detection performance adaptability of the radar in complex clutter background. With the acquired clutter prior knowledge, the algorithm can dynamically select parameters according to the change of background clutter and calculate the threshold. Compared with the detection algorithms such as CA-CFAR, GO-CFAR, SO-CFAR, and OS-CFAR, the simulation results show that CK-CFAR has excellent detection performance in the background of homogenous clutter and edge clutter. This algorithm can help radar adapt to the clutter with different distribution characteristics, effectively enhance radar detection in a complex environment. It is more in line with the development direction of the cognitive radar.

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.

Area-Based CFAR Target Detection for Automotive Millimeter-Wave Radar

Millimeter-wave (mmWave) radar is critical to the emerging automatous driving. As one important step to estimate the range / velocity of unknown targets, constant false-alarm rate (CFAR) techniques should be firstly applied. Most CFAR methods focus on the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">point</i> -based target model (i.e. each target is represented as one point), which may be inadequate for the highly accurate detection scenarios. Owing to the largely improved temporal/spatial resolutions of mmWave radars, each target is now dispersed to many reflection points, covered by a certain area on Range Doppler Map (RDM). In this work, we fully utilize such new information provided by mmWave radars, and develop an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">area</i> -based CFAR framework by fully exploiting the potential diversity gain, with which the detection signal-to-noise ratio (SNR) is substantially improved. Theoretical analysis suggests the achieved SNR gain of our method over traditional algorithms grows as the area size <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$S$</tex-math></inline-formula> of each target on RDM, i.e. <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$ {\mathcal {O}}(S)$</tex-math></inline-formula> . As demonstrated by numerical simulations and real experiments, our method dramatically improves the detection probability of both single-input and single-output (SISO) and multiple-input multiple-output (MIMO) radar systems, also greatly enriching the output point-cloud information for other sophisticated inference tasks. Our method has great potentials in the emerging automotive mmWave radars for highly accurate targets detection.

SA-HPMHT for Maritime Dim Targets Tracking With Sensor Location Uncertainty

To address the problem of tracking low signal-to-clutter ratio (SCR) maritime targets with sensor location uncertainty, an effective track-before-detect (TBD) method, called the state augmentation histogram probabilistic multihypothesis tracking (SA-HPMHT) algorithm, is proposed in this article. In this algorithm, the initialization of new tracks is realized based on the constant false alarm rate (CFAR) detector with a low threshold and the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${K}$ </tex-math></inline-formula> -means clustering algorithm. The target and sensor states are then simultaneously estimated in an iterative manner via a synthetic histogram and state augmentation (SA) method. In each iteration, we update the estimated sensor state and yield more accurate measured images, allowing for improved performance of detection and tracking targets. Finally, an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${M}/{N}$ </tex-math></inline-formula> logic technology is invoked to confirm or terminate the tracks. The results of simulation and real dataset verification show that the SA-HPMHT algorithm has a better performance compared with the histogram probabilistic multihypothesis tracking (H-PMHT) algorithm and the sequential Monte Carlo probability hypothesis density (SMC-PHD) algorithm.

Multitarget Detection and Tracking by Mitigating Spot Jammer Attack in 77-GHz mm-Wave Radars: An Experimental Evaluation

Small form factor radar sensors at millimeter wavelengths find numerous applications in the industrial and automotive sectors. These radar sensors provide improved range resolution, good angular resolution, and enhanced Doppler resolution for short range and ultrashort ranges. However, it is challenging to detect and track the targets accurately when a radar is interfered by another radar. This article proposes an experimental evaluation of a 77-GHz IWR1642 radar sensor in the presence of a second 77-GHz AWR1642 radar sensor acting as a spot jammer. A real-time experiment is carried out by considering five different targets of various cross sections, such as a car, a larger size motorcycle, a smaller size motorcycle, a cyclist, and a pedestrian. The collected real-time data are processed by four different constant false alarm rate detectors, cell averaging (CA)-CFAR, ordered statistics (OS)-CFAR, greatest of CA (GOCA)-CFAR, and smallest of CA (SOCA)-CFAR. Following that, data from these detectors are fed into two different clustering algorithms (density-based spatial clustering of applications with noise (DBSCAN) and K-means), followed by the extended Kalman filter (EKF)-based tracker with global nearest neighbor (GNN) data association, which provide tracks of various targets with and without the presence of a jammer. Furthermore, four different metrics [tracks reported (TR), track segments (TSs), false tracks (FTs), and track loss (TL)] are used to evaluate the performance of various tracks generated for two clustering algorithms with four detection schemes. The experimental results show that the DBSCAN clustering algorithm outperforms the K-means clustering algorithm for many cases.

Strong interference adaptive suppression algorithm for respiratory and heartbeat monitoring

A strong interference adaptive suppression algorithm based on frequency-modulated continuous wave (FMCW) radar is proposed for scenarios with complex static targets. The algorithm removes stationary clutter, stationary fixed targets, and random background noise contained in the echo signal through background resolution, background accumulation, background differencing, and adaptive background update and detects human targets using the cell-averaged selected small constant false alarm rate (SO-CFAR) algorithm. Experiments show that this method improves target detection accuracy in low SNR, reduces the impact of background removal on the target phase, and improves the accuracy of target respiration and heartbeat value estimation.

Ship Detection in Nonhomogeneous Sea Clutter Based on Polarization-Time–Frequency Optimal Using Polarimetric SAR

Synthetic aperture radar (SAR) ship target detection under nonhomogeneous sea conditions is changeable. In this article, according to the characteristics of the target and ocean during SAR imaging, the polarization-time–frequency coherent optimal detector PTFO is constructed, and then the constant false alarm rate method is used to detect ship targets with a stable scattering in SAR images. Four quad-polarimetric RADARSAT-2 data are used to analyze the ship–clutter contrast enhancement capability of PTFO quantitatively, and the appropriate number of time–frequency decompositions is determined to be 3. The proposed method can obtain an FOM of 0.95, which is better than other classical methods to control the detection accuracy and suppress the appearance of false alarm targets.

Application of geometric algebraic projection operator in radar complex signal processing

A variable projection angle is obtained based on the geometric algebra (GA) projection operator using the geometric meaning of complex vectors’ inner product. Considered that the inner product processing is widely used in radar applications, i.e., radar signal detection and matched filtering, a geometric detector, and a geometric matched filter are proposed based on the projection angle in the GA framework. Several simulations, as well as experiments, confirm that the proposed geometric detector overperforms the traditional constant false-alarm rate (CFAR) detector, especially in the uniform background, and in adaptions to the clutter edge and large dynamic change of signals. Moreover, the higher efficiency of the geometric detector is indicated by the analysis comparison of computation complexity, which indicated that the geometric detector possesses higher efficiency than the traditional CFAR detectors. The response characteristics of the geometric matched filter for intermittent sampling and repeater jamming (ISRJ) are also validated through theoretical and simulation analysis. The presented results provide a geometric perspective for solving the design of radar detectors and the suppression of ISRJ.

Infrared Small Marine Target Detection Based on Spatiotemporal Dynamics Analysis

It is a big challenge to detect and track small infrared marine targets in non-stationary and time-varying sea clutter because the signal is too strong to be estimated. Based on the phenomenon that sea clutter spreads not only in the temporal domain but also in the spatial domain, this paper proposes an infrared small marine target detection algorithm based on spatiotemporal dynamics analysis to improve the performances of sea clutter suppression and target detection. The moving sea clutter is modeled as the spatial-temporal phase space, and the dynamical parameters of the sea clutter in the spatiotemporal domain are extracted from the sea clutter image sequence. Afterwards, the temporal dynamics reconstruction function and the spatial dynamics reconstruction function are built based on these extracted dynamical parameters. Furthermore, the space-time coupling coefficient and the spatiotemporal dynamics reconstruction function are estimated by means of a radial basis function (RBF) neural network to reconstruct the propagation regularity of the moving sea clutter. Finally, the sea clutter is suppressed by subtracting the estimated image from the original image, and then the target is detected in the suppressed image using the constant false alarm rate (CFAR) criteria. Some experiments on the small marine target in various fluctuating sea clutter image sequences are induced, and the experimental results show that the proposed algorithm could achieve outstanding performances in sea clutter suppression and small target detection.

Improving Ship Detection Based on Decision Tree Classification for High Frequency Surface Wave Radar

The traditional constant false alarm rate (CFAR) method, with fixed parameter settings and single noise background calculation, is unable to intelligently catch the current detection background. To improve the performance of the CFAR method, this paper proposes a target detection method based on decision tree classification (DTC) for high-frequency surface wave radar (HFSWR). Firstly, the training sample set and labels are obtained by means of a ship automatic identification system (AIS). Then, feature vector of range dimension, Doppler dimension and range-Doppler (RD) dimension is extracted by way of cell averaging, ordered statistics, censored mean and trimmed mean. Finally, DTC is used to recognize “true” and “false” targets in feature space. Experimental results show that, under the same number of detection targets, the DTC method is superior to traditional CFAR methods, and the accuracy of target detection can be increased by more than 5%.

A discrete side-lobe clutter recognition method based on sliding filter response loss for space-based radar

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.

Persistent Homology Approach for Human Presence Detection from 60 GHz OTFS Transmissions.

Orthogonal Time Frequency Space (OTFS) is a new, promising modulation waveform candidate for the next-generation integrated sensing and communication (ISaC) systems, providing environment-awareness capabilities together with high-speed wireless data communications. This paper presents the original results of OTFS-based person monitoring measurements in the 60 GHz millimeter-wave frequency band under realistic conditions, without the assumption of an integer ratio between the actual delays and Doppler shifts of the reflected components and the corresponding resolution of the OTFS grid. As the main contribution of the paper, we propose the use of the persistent homology technique as a method for processing gathered delay-Doppler responses. We highlight the advantages of the persistent homology approach over the standard constant false alarm rate target detector for selected scenarios.

Adaptive filter entropy monitoring method for scalar magnetic detection using optically pumped magnetometers

In this paper, an adaptive filtering minimum entropy detector (AF-MED) method is proposed, which is an improvement on the minimum entropy detector (MED) method. The improvement can be divided into two parts. Firstly, in view of the low detection rate of the MED method in the case of low signal-to-noise ratio (SNR), an adaptive filtering technology is added, which can accurately and dynamically determine the frequency range of the magnetic field according to the moving speed of the magnetic target, and effectively restrain the influence of environmental noise on the magnetic field. Compared with the MED method, the detection rate is increased by 48%. Secondly, using the kernel density estimation method to calculate the probability density value, the advantage is that there is no need to make any predictions or assumptions about the target, which makes the method more convenient to achieve. Furthermore, in this study, numerous simulations and experiments were performed, and the cell averaging constant false alarm rate detector was used to calculate the detection rate. The average value of detection rates of AF-MED, MED and the orthogonal basis function (OBF) are about 69%, 21% and 43% respectively when SNR is low (SNR from −25 dB to −50 dB). The results demonstrated the superiority of the proposed method over the MED and the OBF.

The improved constant false alarm rate detector based on multi‐frame integration for fluctuating target detection in heavy‐tailed clutter

AbstractIn this paper, attention is devoted to the analysis of the detection threshold based on the multi‐frame integration in heavy‐tailed clutter for the radar with high resolution and even smaller grazing angle. The closed‐form expressions of both the probability of the detection and the probability of false alarm for the heavy‐tailed clutter background, which can be used for the theoretical analysis of constant false alarm rate (CFAR) detectors, are derived with the multi‐frame integration technique. Accordingly, an improved CFAR detector is designed to work well with the presence of target‐like outliers in the heavy‐tailed clutter. In addition, the proposed CFAR detector is capable to alleviate the masking‐effect resorting to the additive feedback operation when a target is large enough to cross several cells in multi‐target case. The theoretical analysis and numerical simulations demonstrate that the proposed CFAR detector based on multi‐frame integration can improve the signal‐to‐clutter rate of the targets exhibiting better performance than ones based on single frame in heavy‐tailed clutter background. It is validated from the simulations that the proposed CFAR detector with additive feedback operation can deal with masking‐effect for large target occupying several cells.

Subspace gradient tests for range‐spread target detection in structured interference plus Gaussian clutter

AbstractThis study considers the problem of detecting range‐spread targets embedded in subspace interference plus Gaussian clutter with an unknown covariance matrix. The target and interference signals are modeled in terms of deterministic signals belonging to two known subspaces, respectively. Based on the Gradient test criterion, two adaptive detectors are devised for rejecting subspace interference in homogeneous and partially homogeneous environments, respectively. Both of the proposed detectors theoretically exhibit a desirable property of a constant false alarm rate with respect to the clutter covariance matrix as well as the power level. Furthermore, the numerical results show that, compared with their existing counterparts, the proposed detectors exhibit better detection performance and satisfactory suppression performance for the interference.

Siamese Cross-Domain Tracker Design for Seamless Tracking of Targets in RGB and Thermal Videos

Multimodal (RGB and thermal) applications are swiftly gaining importance in the computer vision community with advancements in self-driving cars, robotics, Internet of Things, and surveillance applications. Both the modalities have complementary performance depending on illumination constraints. Hence, a judicious combination of both modalities will result in robust RGBT systems capable of all-day all-weather applications. Several studies have been proposed in the literature for integrating the multimodal sensor data for object tracking applications. Most of the proposed networks try to delineate the information into modality-specific and modality shared features and attempt to exploit the modality shared features in enhancing the modality specific information. In this work, we propose a novel perspective to this problem using a Siamese inspired network architecture. We design a custom Siamese cross-domain tracker architecture and fuse it with a mean shift tracker to drastically reduce the computational complexity. We also propose a constant false alarm rate inspired coasting architecture to cater for real-time track loss scenarios. The proposed method presents a complete and robust solution for object tracking across domains with seamless track handover for all-day all-weather operation. The algorithm is successfully implemented on a Jetson-Nano, the smallest graphics processing unit (GPU) board offered by NVIDIA Corporation.

Multi-radar data fusion for maritime moving target detection based on three-dimensional sliding window

ABSTRACT With the continuous improvement of radar resolution, distributed characteristics are presented with regard to maritime moving targets, which occupy multi-resolution cells in Synthetic Aperture Radar (SAR) image domain. However, the current constant false alarm rate (CFAR) detection algorithms rarely consider the impact of distributed characteristics on target detection performance, and thus the corresponding performance evaluation methods could not be applicable to this issue. In this paper, a multi-station fusion detection method for maritime moving target (MMT) is presented based on three-dimensional (3D) sliding window. Firstly, the multi-station echoes in Cartesian-Doppler frequency rate (DFR) domain are obtained under the simple-transmitting and multiple-receiving operation configuration, and the 3D sliding window is designed to achieve the optimal matching for a specified target with any moving direction in terms of its prior information, i.e. Radar Cross Section (RCS), radar resolution and target size. Then, the target cells within the sliding window are directly processed by means of the M/N criterion, and thus to avoid target detection performance loss caused by the artificial construction of extended detection statistics. Finally, a novel quantitative evaluation method in regards to a high-resolution radar is designed by mining the relationship between the target detection performance and the occupied cell number, which could greatly improve detection probability of distributed targets on the premise of a constant false alarm rate. The proposed algorithm does not need to design complex extended detection statistics, which provides a feasible way for robust detection and performance evaluation for a high-resolution radar. Simulation results verify the effectiveness of this research.

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.

Robust CFAR Detector Based on KLQ Estimator for Multiple-Target Scenario

The order statistic (OS) constant false alarm rate (CFAR) detector is designed from a perspective of quantile estimation and uses the sample quantile (SQ) estimator to estimate background level. Following this idea, we find a more efficient quantile estimator, i.e. Kaigh-Lachenbruch quantile (KLQ) estimator, to construct a robust CFAR detector, which is referred to as KLQ-CFAR. We prove that the KLQ estimator is an asymptotic unbiased estimator to the quantile function and derive the asymptotic variance of KLQ estimator in Exponential case. It is shown that the KLQ estimator has more efficient estimation efficiency than SQ estimator. Subsequently, we explore the performance of KLQ-CFAR in Exponential clutter. In homogeneous background, the KLQ-CFAR conquers the shortcoming of large detection loss of OS-CFAR, and exhibits comparable performance with trimmed mean (TM) CFAR. The superiority of KLQ-CFAR is also reflected in multiple-target scenario, especially in the case where the number of interferences exceeds the preset parameters of KLQ- and TM-CFAR. In this case, the detection performance and false alarm control ability of KLQ-CFAR have significant improvement compared with TM-CFAR. Then, we extend the KLQ-CFAR from Exponential background to non-Gaussian clutter via transformation approach, and obtain the transformed KLQ (TKLQ) CFAR. We take Weibull clutter as a specific example. It is found that the behavior of TKLQ-CFAR in Weibull background is very similar to that of the KLQ-CFAR in Exponential clutter, but the difference lies in that the performance of TKLQ-CFAR is related to Weibull shape parameter. The TKLQ-CFAR tends to have better detection performance with the increase of Weibull shape parameter. Finally, the validity of TKLQ-CFAR is verified in skywave over-the-horizon radar data.