Constant False Alarm Rate Detector Research Articles

Ship Detection and Direction Finding Based on Time-Frequency Analysis for Compact HF Radar

Ship detection at the sea surface is important for improving human marine activities. Most existing ship detection methods for high-frequency surface wave radar (HFSWR) are based on peak and constant false alarm rate (CFAR) detection and require a coherent integration time (CIT) of several minutes. However, in such a long period, the target may not be stationary. To account for the nonstationary property, a time-frequency analysis (TFA)-based ship detection and direction finding (DF) method is proposed for HFSWR. Target ridges on the TF representation (TFR) of the echo data are detected first. Next, array snapshots are formed by sampling the extracted ridges and are used to estimate the direction of arrival (DOA). The processing results of the radar data collected at Dongshan, Fujian Province, China, show that the proposed method outperforms the CFAR method with both increased detection rates and decreased DF errors, especially under relatively low signal-to-noise ratio (SNR) scenarios.

An Applied Method for Clustering Extended Targets With UHF Radar

In this paper, the application of coherent ultra-high frequency (UHF) Doppler radar for ship target detection over river is investigated. Due to the wide beam and high resolution of UHF radar, ship target echoes are usually significantly extended in both the range and Doppler dimensions of the radar Range-Doppler (R-D) spectrum. The range and radial velocity of the extended target are difficult to be determined using a constant false alarm rate (CFAR) detector, especially for the low-radial-velocity case in which the detection performance of CFAR detector is deteriorated due to strong river clutter. To solve this problem, an applied clustering method is proposed to detect and classify multiple targets and obtain corresponding target centers from the CFAR outputs. The target extension characteristics, which are used for clustering, are modeled and employed in segments for different range. The effectiveness of the proposed method is validated using both simulated and field data and the clustering method can classify extended targets without the need of knowing the number of targets beforehand.

Constant false alarm rate detection based on estimating statistical distribution of non‐homogeneous sea clutter in sky‐wave over‐the‐horizon radar

In sky-wave over-the-horizon radar (OTHR), there are some difficulties to efficiently detect targets in the non-homogeneous sea clutter by using conventional constant false alarm rate detectors. The amplitude of the clutter is supposed to follow the Weibull distribution in OTHR. Moreover, the characteristic of the clutter with a continuous region distribution in the range-Doppler (RD) map suggests that there exist the spatial relationship between neighbouring cells. The authors propose a rigorous algorithm to incorporate these relationships and the regularisation of interferences into the estimation of the shape parameter and scale parameter for every cell under test. This algorithm is mainly based on the consideration that the improvement of detection performance could be carried out by properly utilising the spatial information between neighbouring cells in the non-homogeneous Weibull clutter. Then, the adaptive detection threshold is calculated by the estimated parameters. The validity and effectiveness of the proposed algorithm are verified by simulations and the experiments in the real RD maps collected from an experimental sky-wave OTHR system.

Enhanced Matrix CFAR Detection With Dimensionality Reduction of Riemannian Manifold

This letter proposes an enhanced matrix constant false alarm rate (CFAR) detection method that works on the lower-dimensional Riemannian manifold. Motivated by general matrix CFAR detection method and dimensionality reduction scheme of the Riemannian manifold, this method obtains a mapping by maximizing the geometric test statistic. Dimensionality reduction is formulated as an orthonormal constraint optimization problem on the Grassmann manifold. Moreover, an explicit mapping is obtained by solving the optimization problem via conjugate gradient approach. Performances of the proposed method are evaluated on the lower-dimensional Riemannian manifold. Experiments on simulated data and real sea clutter data demonstrate that our method leads to the robustness to outliers and the improvement of detection performance over classical methods.

An Improved CA-CFAR Method for Ship Target Detection in Strong Clutter Using UHF Radar

In this letter, the application of ultra-high frequency (UHF) radar for ship target detection over river is investigated for the first time. Due to the wide beam width of antenna, the ship detection of the UHF radar suffers from the broaden river clutters scattering from water waves. In addition, due to the high resolution, the ship echoes are seriously extended in both range and Doppler dimensions, which is different from the traditional point target signal. Extended target detection in strong river clutter is applied in this letter. Conventional constant false alarm rate (CFAR) detector is limited in this situation, which is applicable to the point target detection in clutter free background. An improved cell-averaging (CA)-CFAR method is proposed based on a joint estimation of threshold in range, Doppler and time serials of range-Doppler (R-D) spectra. Based on the time stationarity of clutter, this three-dimensional (3D) CA-CFAR combines several R-D spectra collected in continuous time to estimate the clutter threshold. The effectiveness of this improved method is validated using both simulated and field data.

Clutter map detector for active diver detection sonar

This study presents a temporal approach based on the clutter map for the automatic detection of divers in the active sonar system. The noisy and non-stationary surrounding environment of shallow water makes the statistical parameters estimation of classical constant false alarm rate (CFAR) detectors more difficult. In the authors' system, they take advantage of the fixed positions of the transmitting and receiving arrays in order to improve the statistical parameters estimation over time. The resulting detection algorithm is analysed on simulated data for slow-moving targets and compared with classical spatial CFAR techniques on real data. The authors show that the proposed technique based on the clutter map provides better results than the classical CFAR techniques, particularly due to a reduction in false alerts. They finally analyse the spatial distribution of false alarms for which randomness is important for the performance of the tracking algorithm. They show that this distribution is more random with the clutter map detector than with classical CFAR techniques.

Low-Complexity High-Resolution Parameter Estimation for Automotive MIMO Radars

In this paper, we propose a parameter estimation method for multiple-input-multiple-output (MIMO) automotive radars that consists of two stages. The first stage is a low-complexity three-dimensional (3D) constant false alarm rate (CFAR) detection technique that exploits spatial filtering to extend radar coverage, and it performs low-complexity peak detection. The second stage is an ESPRIT-based direction-of-arrival (DOA) estimation technique that adopts time-frequency resource division to generate high-quality snapshots and it performs DOA estimation of targets without the knowledge of the target number. Computer simulations reveal that the proposed method achieves the performance of the two-dimensional ordered statistic CFAR (2D OS-CFAR) while having much lower computational complexity, and it offers the higher resolution DOA estimation compared to the conventional MIMO radars.

Multiple Sensor Linear Multi-Target Integrated Probabilistic Data Association for Ultra-Wide Band Radar

Ultra-Wide Band (UWB) radar has a number of advantages of resolving multipath, exceptional spatial resolution, and ranging performance. However, several difficulties are confronted for multiple target tracking using UWB radars such as clutter signals which contaminate target signals, and unidentified number and behavior of the targets. Hence, this paper presents to develop a multiple moving target tracking algorithm, consisting of preprocessing and multiple target tracking steps. In the preprocessing step, static clutter reduction and constant false alarm rate (CFAR) detection extract the target candidate range measurements from each UWB radar. Then, two multiple target tracking (MTT) steps are developed: range- based MTT and position-based MTT. The range-based MTT is mainly based on existing linear multi-target integrated probabilistic data association (LM-IPDA) from each UWB radar measurement. Then the outputs of each LM-IPDA are gathered in the positioning center to estimate the position of multiple targets. On the other hands, the position-based MTT is based on multiple sensor LM-IPDA (msLM-IPDA) as an accurate target tracking method for various uncertainties by improving the probabilistic model of LM-IPDA. The tracking performance of two MTT methods is investigated with both numerical simulation and experiments.

Single-Pulse Simultaneous Target Detection and Angle Estimation in a Multichannel Phased Array Radar

This paper is focused on simultaneous target detection and angle estimation with a multichannel phased array radar. Resorting to a linearized expression for the array steering vector around the beam pointing direction, the problem is formulated as a composite binary hypothesis test where the unknowns, under the alternative hypothesis, include the target directional cosines displacements with respect to the array nominal coarse pointing direction. The problem is handled via the Generalized Likelihood Ratio (GLR) criterion (both one-step and two-step) where decision statistics leveraging the Maximum Likelihood Estimates (MLEs) of the parameters are compared with a detection threshold. If crossed, target presence is declared and the MLEs of the aforementioned displacements directly provide target angular position with respect to the pointing direction. From the analytic point of view, ML estimation involves a constrained fractional quadratic optimization problem whose optimal solution can be found via the Dinkelbach's algorithm or approximated through a fast-converging procedure based on a Coordinate Descent (CD) optimization. The performance analysis of the proposed architectures as well as the corresponding discussion is developed in terms of computational complexity, Constant False Alarm Rate (CFAR) behavior, detection performance, and angular estimation accuracy, also in comparison with some counterparts available in the open literature and theoretical benchmark limits.

Target Detection for HFSWR Based on an S3D Algorithm

Because of the influence of clutter and interference, intelligent detection is of great importance for high-frequency surface wave radar (HFSWR) in complex environment. In this paper, we propose a cascade HFSWR target detection strategy based on semi-supervised self-distillation ( $\sf S^{3}D$ ) learning algorithm. First, based on the results of constant false alarm rate (CFAR) detection, the $\sf S^{3}D$ algorithm proposed in this paper further classifies the real and other targets. Moreover, the non-maximum suppression (NMS) is combined to remove redundant target boxes, in order to achieve accurate location. For supervised learning, the classification needs to label all the samples, which is extremely difficult and time consuming, especially for HFSWR data under complex environment. While the $\sf S^{3}D$ algorithm can make use of both labeled and unlabeled samples. For labeled data, labels are directly used to restrict both the deep and shallow layers of the network, so that both layers represent the same semantic categories. For unlabeled data, the pseudo-label is retained only if the deepest layer produces a high-confidence prediction. The deep and shallow layers are then updated to predict the pseudo-label when fed a strongly augmented version of the same unlabeled samples. The above multiple consistency regularization methods can improve the generalization performance of the network effectively. Finally, the simulations conducted across public datasets show that compared with other semi-supervised learning methods, $\sf S^{3}D$ can better improve the performance of network generalization. Besides, the $\sf S^{3}D$ -based HFSWR target detection method can significantly improve the vessel detection performance by using only a minority of sample labels.

Invariance Theory for Adaptive Detection in Non-Gaussian Clutter

This paper studies the problem of detecting range-spread targets in (possibly non-Gaussian) clutter whose joint distribution belongs to a very general family of complex matrix-variate elliptically contoured distributions. Within the family, we explore invariance with respect to both the distributional type and relevant parameters. Several groups are used to describe these invariance mechanisms, and a relationship is revealed between the group invariance and the constant false alarm rate (CFAR) properties in terms of model parameters, the generator function, or both. We then build a maximal invariant framework for the detection problem. This involves deriving the corresponding maximal invariants as well as their statistical characterizations. Using these results, we put forward several maximal invariant detectors, all of which are fully CFAR in that their false alarm rates are completely independent of the underlying clutter distribution. Numerical results show that all the proposed fully CFAR detectors are effective, and for the considered simulation setup, one of them outperforms the others and several existing ones.

A Multifamily GLRT for CFAR Detection of Signals in a Union of Subspaces

We consider the problem of detecting an unknown signal that lies in a union of subspaces (UoS) and that is observed in additive white Gaussian noise with unknown variance. The main contribution of this letter is the derivation of a detector that can accommodate a union made of nested subspaces. This detector includes the generalized likelihood ratio test (GLRT) as a special case when the subspace dimensions are all identical. It relies on the framework of multifamily likelihood ratio tests (MFLRT) and is shown by numerical examples to achieve better performance than existing detectors.

Feature Detection With a Constant FAR in Sparse 3-D Point Cloud Data

The detection of markers or reflectors within point cloud data (PCD) is often used for 3-D scan registration, mapping, and 3-D environmental modeling. However, the reliable detection of such artifacts is diminished when PCD is sparse and corrupted by detection and spatial errors, for example, when the sensing environment is contaminated by high dust levels, such as in mines. In the radar literature, constant false alarm rate (CFAR) processors provide solutions for extracting features within noisy data; however, their direct application to sparse, 3-D PCD is limited due to the difficulty in defining a suitable noise window. Therefore, in this article, CFAR detectors are derived, which are capable of processing a 2-D projected version of the 3-D PCD or which can directly process the 3-D PCD itself. Comparisons of their robustness, with respect to data sparsity, are made with various state-of-the-art feature detection methods, such as the Canny edge detector and random sampling consensus (RANSAC) shape detection methods.

Robust CFAR Detection for Multiple Targets in K-Distributed Sea Clutter Based on Machine Learning

For K-distributed sea clutter, a constant false alarm rate (CFAR) is crucial as a desired property for automatic target detection in an unknown and non-stationary background. In multiple-target scenarios, the target masking effect reduces the detection performance of CFAR detectors evidently. A machine learning based processor, associating the artificial neural network (ANN) and a clustering algorithm of density-based spatial clustering of applications with noise (DBSCAN), namely, DBSCAN-CFAR, is proposed herein to address this issue. ANN is trained with a symmetrical structure to estimate the shape parameter of background clutter, whereas DBSCAN is devoted to excluding interference targets and sea spikes as outliers in the leading and lagging windows that are symmetrical about the cell under test (CUT). Simulation results verified that the ANN-based method provides the optimal parameter estimation results in the range of 0.1 to 30, which facilitates the control of actual false alarm probability. The effectiveness and robustness of DBSCAN-CFAR are also confirmed by the comparisons of conventional CFAR processors in different clutter conditions, comprised of varying target numbers, shape parameters, and false alarm probabilities. Although the proposed ANN-based DBSCAN-CFAR processor incurs more elapsed time, it achieves superior CFAR performance without a prior knowledge on the number and distribution of interference targets.

Ship Detection Using Deep Convolutional Neural Networks for PolSAR Images

Ship detection plays an important role in many remote sensing applications. However, the performance of the PolSAR ship detection may be degraded by the complicated scattering mechanism, multi-scale size of targets, and random speckle noise, etc. In this paper, we propose a ship detection method for PolSAR images based on modified faster region-based convolutional neural network (Faster R-CNN). The main improvements include proposal generation by adopting multi-level features produced by the convolution layers, which fits ships with different sizes, and the addition of a Deep Convolutional Neural Network (DCNN)-based classifier for training sample generation and coast mitigation. The proposed method has been validated by four measured datasets of NASA/JPL airborne synthetic aperture radar (AIRSAR) and uninhabited aerial vehicle synthetic aperture radar (UAVSAR). Performance comparison with the modified constant false alarm rate (CFAR) detector and the Faster R-CNN has demonstrated that the proposed method can improve the detection probability while reducing the false alarm rate and missed detections.

Multi-Scale Rotation-Invariant Haar-Like Feature Integrated CNN-Based Ship Detection Algorithm of Multiple-Target Environment in SAR Imagery

This paper proposes a multi-scale rotation-invariant haar-like (MSRI-HL) feature integrated convolutional neural network (MSRIHL-CNN)-based ship detection algorithm of the multiple-target environment in synthetic aperture radar (SAR) imagery. Usually, ship detection includes preprocessing, prescreening, discrimination, and classification. Among them, prescreening and discrimination are the most two important stages so that they catch great intention. Based on our previous work, we propose a truncated-clutter-statistics-based joint, constant false alarm rate (CFAR) detector (TCS-JCFAR) for ship target prescreening in the multiple-target environment. TCS-JCFAR greatly enhances the prescreening rate in the multiple-target environment while achieving a low observed FAR. In the discrimination stage, conventional CNN extracts the deep features (high-level features); however, it will lose the local texture and edge information (low-level features) which are of great significance for target discrimination. Hence, the MSRI-HL features are used to represent the multi-scale, rotation-invariant texture, and edge information that conventional CNN fails to capture. The extracted low-level MSRI-HL features and the high-level deep features are optimally fused to a multi-layered feature vector. Finally, the multi-layered feature vector is fed into a typical support vector machine (SVM) classifier for ship target discrimination. The proposed MSRIHL-CNN combines the low-level texture and edge features and the high-level deep features; moreover, they are optimally fused to fully represent the ship targets. Undoubtedly, MSRIHL-CNN has better discrimination performance. The superiority of the proposed TCS-JCFAR-based prescreener and MSRIHL-CNN-based discriminator is validated on the Chinese Gaofen-3 SAR imagery.

Fast Double Selectivity Index-CFAR Detection Method for the Multi-Beam Echo Sounder

Seafloor terrain and water column target detection is of great significance in marine surveys. However, the complex clutter environment can dramatically affect the detection performance of a multi-beam echo sounder. To simultaneously and robustly detect the seafloor terrains and water column targets, this paper proposes a fast two-dimensional double selectivity index-constant false alarm rate (DSI-CFAR) detection method. A two-dimensional cross sliding window, which includes reference cells and guard cells, is used in this paper. The proposed method improves the detection performance by using the appropriate clutter power level estimation strategies in different directions. An exponential model is accurately built by fitting various distributions, so the DSI-CFAR detection performance is greatly ameliorated. To reduce the computational load of the method, this paper uses a fast algorithm based on the global threshold. The region of interest (ROI) is selected by the preset global threshold, and the DSI-CFAR detector is only performed in the region of interest, which significantly reduces the number of pixels that must be calculated. In this paper, the measured results of different sliding windows and CFAR detection methods validate the basic performance of the proposed method.

Optimal Predictive Inference and Noncoherent CFAR Detectors

A Bayesian predictive inference approach, for the development of classical sliding window detection processes, is the focus of this article. It will be demonstrated that this methodology can produce such detectors with the constant false alarm rate (CFAR) property for clutter modeled by statistics, which are invariant with respect to a group of scale and power transformations. For such a clutter model, it will be shown that the derived detector achieves optimality in the sense of strong consistency. This implies that the Bayesian CFAR detector will have the best performance, in the class of CFAR detectors, for this specific clutter distribution. Although these Bayesian detectors are produced under the assumption of homogeneous clutter, they nonetheless provide the practical engineer with a benchmark on potential CFAR performance. To illustrate the results, the optimal CFAR detector for the Weibull-distributed clutter is derived. Although the Pareto Type I clutter model fits into the class of scale- and power-invariant distributions, there is complexity in the derivation of the Bayesian decision rule. Hence, this special case is also examined, and it will be shown that the Bayesian CFAR corresponds to a well-known detector for this clutter environment.

Spectral Norm Based Mean Matrix Estimation and Its Application to Radar Target CFAR Detection

The estimation of mean matrix for a set of Hermitian positive definite (HPD) matrices can be considered based on a geometric framework. This kind of estimator is closely related to geometric measures, which can be established to measure the dissimilarity between two matrices on the matrix manifold. In this paper, the matrix spectral norm is exploited to derive a new geometric measure on the matrix manifold consisting of all HPD matrices. Correspondingly, the mean matrix associated with the new geometric measure on the matrix manifold is considered via two effective solution techniques. More specifically, the first method transforms the mean matrix estimation into a semidefinite programming problem solved by the interior point method; the second method exploits joint approximate diagonalization to diagonalize the given covariance matrices and then recasts the mean matrix estimation into an optimization problem with linear inequality constraints. Furthermore, two matrix detectors employing the proposed mean matrix estimators are designed for target detection using pulse Doppler radar with a small bunch of pulses. At the analysis stage, we show that the proposed matrix detector can guarantee the bounded constant false alarm rate property in terms of the clutter covariance matrix. Finally, numerical experiments based on simulated clutter data and real sea clutter data are performed to verify the effectiveness and robustness of the proposed matrix detectors.

Aircraft Detection for HR SAR Images in Non‐homogeneous Background Using GGMD‐Based Modeling

In the problem of aircraft detection for High resolution (HR) Synthetic aperture radar (SAR) images, the background areas commonly contain multiple land cover types, such as runways and grassland. The conventional Constant false alarm rate (CFAR) detection in these non-homogeneous backgrounds with homogeneous assumption leads to unreliable detection results. This paper constructs a one-stage detection method based on the Generalized gamma mixture distribution (GGMD), which is regarded as a competitive and applicable model for combining the advantages of the Generalized gamma distribution (GGD) and the Finite mixture model (FMM). In order to evaluate the availability of the proposed algorithm, HR SAR images for aircraft detection from different product types and with various resolutions are examined. Compared with the CFAR algorithms based on the Gamma distribution, the GGD, and the gamma mixture distribution, the proposed algorithm demonstrates its availability and effectiveness for aircraft detection in HR SAR images in non-homogeneous background.