Conventional Constant False Alarm Rate Research Articles

Modified rank sum nonparametric CFAR to combat clutter edge

The classical rank sum (RS) nonparametric constant false alarm rate (CFAR) detector plays an important role in the theoretical study and practical application of radar target detection. In order to improve the ability of the classical RS nonparametric detector to control the false alarm rate at clutter edges, a modified rank sum (MRS) nonparametric CFAR based on the mean ratio of the samples in the leading and lagging windows is proposed. The analytical expressions of the detection probability and false alarm rate of the MRS nonparametric CFAR in homogeneous background and at clutter edges are derived, and a comparison to the performance of the classical RS nonparametric detector along with some conventional parametric CFAR schemes in homogeneous background, multiple targets situation and clutter edges is made. The numerical results show that the detection performance of the MRS nonparametric CFAR in homogeneous background and in a moderate number of interfering targets situation is close to that of the classical RS nonparametric detector, and its ability to control the rise of the false alarm rate at clutter edges is evidently improved.

Multi-target CFAR detector based on compressed sensing radar system

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

Multi-Person Position Estimation Based on Correlation Between Received Signals Using MIMO FMCW Radar

By using Multiple-Input Multiple-Output (MIMO) Frequency Modulated Continuous Wave (FMCW) radar, a range-angle map can be obtained that displays the signal strength in two dimensions with respect to distance and angular directions. In this process, a threshold algorithm such as Constant False Alarm Rate (CFAR) is used to detect positions where the signal strength exceeds a certain threshold value, enabling position estimation of targets. Furthermore, a method has been proposed to use the curve length of the trajectory of the I/Q signal to emphasize the signal of the person’s position on the range angle map. However, the conventional CFAR may cause false alarms when noise and clutter signals are strong. In this paper, we propose a new method for human location estimation using the MIMO FMCW radar. Comparing the reflected signals from people and clutter, we see that I/Q signal variations associated with human motion have little correlation with clutter from stationary objects or noise, and I/Q signal variations associated with clutter often have high correlation with signals at other positions. We focus on the fact that there are many positions where the clutter component signal has a high correlation, compared to the signal caused by a person. Using the correlation map that expresses the correlation between the received I/Q signals on the range-angle plane, we evaluated the characteristics of noise and clutter components from walls on the correlation map by standard deviation. As a result, we can remove strong noise and clutter components caused from near walls, thus improving the accuracy of object location estimation.

Multi-output Convolutional Neural Network Based Distance and Velocity Estimation Technique for Orthogonal Frequency Division Multiplexing Radar Systems

The objective of this work is to propose a new method of estimating velocity and distance based on multi-output convolutional neural network (CNN) for orthogonal frequency division multiplexing (OFDM) radars. The two-dimensional (2D) periodogram is extracted from the received reflected waveforms through radar signal processing of received OFDM symbols. Conventionally, constant false alarm rate (CFAR) algorithm is used to estimate distance and velocity of targets. In contrast, this paper proposes a novel deep-learning based approach for the estimation of the targets in OFDM radar systems. The proposed multi-output CNN-based target detector estimates the distance and velocity of the target simultaneously. The proposed technique is verified through computer simulation. The results show that the proposed multi-output CNN-based method demonstrates more accurate distance and speed estimates than the conventional CFAR. Specifically, the distance and speed estimates of the proposed method are 9.8 and 12.3 times accurate, respectively, than those of the conventional CFAR.

Deep Learning-Based UAV Detection in Pulse-Doppler Radar

With the popularity of unmanned aerial vehicles (UAVs), how to conduct automatic and effective detection to prevent unauthorized flying has become an important issue. The conventional constant false alarm rate (CFAR) detector based on radar signal has shown advantages in moving target detection. However, the CFAR-based detectors are strongly dependent on some manual experience, such as the ambient noise distribution estimation and the detection windows’ size selection, and usually suffered poor performance on small UAV detection due to the weak signal. Inspired by the success of deep learning (DL) on natural scene object detection, this article tries to explore a DL-based method for UAV detection in pulse-Doppler radar. Concretely, we propose a convolutional neural network (CNN) with two heads: one for the classification of the input range-Doppler map patch into target present or target absent and the other for the regression of offset between the target and the patch center. Then, based on the output of the network, a nonmaximum suppression (NMS) mechanism composed of probability-based initial recognition, distribution density-based recognition, and voting-based regression is developed to reduce false alarms as well as control the false alarms. Finally, experiments on both simulated data and real data are carried out, and it is shown that the proposed method can locate the target more accurately and achieve a much lower false alarm rate at a comparable detection rate than CFAR.

DNN-Based Peak Sequence Classification CFAR Detection Algorithm for High-Resolution FMCW Radar

Multitarget detection is very challenging, especially when targets are densely distributed. In conventional constant false alarm rate (CFAR) detection algorithms, the detection threshold is determined based on a pre-estimated background level (BL). However, interfering targets inevitably lead to inaccurate BL estimation, resulting in degraded detection performance. In our previous work, a new solution was developed and verified to be effective; in that solution, compressed sensing (CS)-based detector performs target detection without relying on BL estimation, and a subsequent CFAR regulation processor achieves the desired false alarm rate based on the statistical information of the reduced samples obtained by removing the detected targets and adjacent guard cells from the original samples. However, for scenes with a high target density, the CS-based detector suffers from performance degradation while acquiring the correlation between linear measurements of the signal and the sensing matrix due to the high local signal sparsity. To address this shortcoming, this article proposes a deep neural network (DNN)-based detector that further improves the detection performance by converting the target detection into a problem of peak sequence classification of frequency intensity (FI) measurements from radar. A DNN detector trained on augmented simulated data shows excellent generalization ability for deployment in real scenes. In addition, to achieve better computational efficiency, a Taylor-series-based approximate maximum likelihood estimator with an explicit expression is applied in the CFAR regulation process for the first time. Both simulation and field tests are performed to verify the superiority of the proposed algorithm over conventional algorithms.

Robust CFAR Detector With Ordered Statistic of Sub-Reference Cells in Multiple Target Situations

Herein, a robust constant false alarm rate (CFAR) detector with ordered statistic of sub-reference cells (OSS-CFAR) is proposed in multiple target situations. This detector can improve background level estimation and reduce computational complexity using sub-reference cells. The detection performance of the OSS-CFAR detector and of conventional CFAR detectors in multiple target situations are investigated and compared using computer simulations and experimental data with sea clutter. The simulations and experimental results show that the OSS-CFAR detector achieves robust detection performance with low computational complexity, whereas conventional CFAR detectors suffer performance degradation in multiple target situations. At the clutter edge, the OSS-CFAR detector with appropriate parameters achieves an acceptable false alarm rate compared to conventional CFAR detectors.

Improving Ship Detection in Clutter-Edge and Multi-Target Scenarios for High-Frequency Radar

As one of the main sensors for continuous maritime measurements of sea state parameters, high-frequency surface wave radar (HFSWR) also plays an important role in ship detection and tracking. Compact HFSWR often suffers from missing targets, especially when the target appears near the Doppler region with heavy sea clutter or near another target in a multi-target scenario. To address this problem, an automatic ship detection method based on time–frequency (TF) analysis is presented in this paper. The TF target ridge areas are extracted in the TF image via the eigenvalues of the Hessian matrix, image edge detection, and local maximum search. Then, whether ship signals exist in the TF ridges or not is decided by a decision threshold that is calculated by fitting the probability distribution function (PDF) of sea clutter in the TF domain. The proposed TF method can separate TF ridges of similar Doppler frequency and performs constant false alarm rate (CFAR) detection for TF targets, which facilitates detecting these targets that are masked by sea clutter and other large targets. Experimental results show that the number of detected ships that match with the automatic identification system (AIS) records is four times more than that obtained by the conventional constant false alarm rate (CFAR) detectors and 1.3 times more than that by the state-of-the-art TF method in consideration of approximately the same number of detected targets.

Waveguide Invariant-based mitigation of reverberation and interference in active and passive sonar

Multipath propagation poses many challenges for broadband sonar signal processing in underwater acoustic channels. The waveguide invariant (WI) provides a useful relation between the temporal and frequency fading characteristics in shallow-water channels that can been exploited to improve broadband target detection in both active and passive sonars. In active sonar, the WI motivates an alternative to conventional constant-false-alarm rate (CFAR) normalization of the background by averaging over observed time-frequency striations to estimate the reverberation level. A WI-based generalized likelihood ratio test is reviewed and shown to outperform conventional cell-averaged and frequency-invariant CFAR detectors in both simulation and with real Mediterranean data. In passive towed-array sonar, targets near endfire are often masked by angularly spread broadband multipath interference whose statistics must be estimated with limited training data for effective suppression. A WI relationship characterizing the frequency-dependence of wavenumber differences permits averaging of narrowband array snapshots to form a low-rank broadband “focused” covariance matrix. Simulation experiments in a realistic ocean environment are reviewed showing that adaptive beamforming using WI focused covariance matrices can provide a significant array gain improvement over conventional adaptive methods with limited observation time. Finally, the potential for waveguide invariant-based blind source separation is discussed. [Work supported by ONR.]

A Low-Complexity Radar Detector Outperforming OS-CFAR for Indoor Drone Obstacle Avoidance

As radar sensors are being miniaturized, there is a growing interest for using them in indoor sensing applications such as indoor drone obstacle avoidance. In those novel scenarios, radars must perform well in dense scenes with a large number of neighboring scatterers. Central to radar performance is the detection algorithm used to separate targets from the background noise and clutter. Traditionally, most radar systems use conventional constant false alarm rate (CFAR) detectors but their performance degrades in indoor scenarios with many reflectors. Inspired by the advances in nonlinear target detection, In this article, we propose a novel high performance, yet low-complexity target detector and we experimentally validate our algorithm on a dataset acquired using a radar mounted on a drone. We experimentally show that our proposed algorithm drastically outperforms ordered statistics CFAR (OS-CFAR) (standard detector used in automotive systems) for our specific task of indoor drone navigation with more than 19% higher probability of detection for a given probability of false alarm. We also benchmark our proposed detector against a number of recently proposed multitarget CFAR detectors and show an improvement of 16% in probability of detection compared to censored harmonic averaging CFAR, with even larger improvements compared to both outlier-robust CFAR and truncated statistics log-normal CFAR in our particular indoor scenario. To the best of authors’ knowledge, this article improves the state-of-the-art for high-performance yet low-complexity radar detection in critical indoor sensing applications.

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.

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.

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.

SAR Target Detection in Complex Scene Based on 2-D Singularity Power Spectrum Analysis

The synthetic aperture radar (SAR) target detection method in the background of a complex scene and extremely low signal-to-noise ratio (EL-SNR) is studied in this paper. With theoretical derivation and analysis, the singularity power spectrum (SPS) is developed into two-dimensional SPS (2D-SPS). Developed from the 2-D Holder exponent and the SPS, the 2D-SPS can be applied for the singularity power analysis of images. Furthermore, a novel SAR target detection method based on 2D-SPS is proposed. The proposed methodology is tested on sea clutters, both with and without SAR ship target, from the OpenSAR data sets. The experimental results indicate that the SAR target detection based on 2D-SPS performs better than conventional multifractal spectrum (MFS) and constant false alarm rate (CFAR) methods, and can achieve more than 95% and 98% detection probability, respectively, under EL-SNR with SNR = −30 dB, SNR = −10 dB, and $P_{f} = 10^{-2}$ , and almost 100% detection probability for weak and multiple targets within sea clutters. The proposed method can be applied to target detection under the background of fractal noise and provides a reference for target detection in related fields.

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.

Predicting False Alarm Rates for High-Resolution Antisubmarine Warfare Sonars in a Cluttering Environment Prone to False Alarm Rate Inflation

Operation of high-resolution, broadband, antisubmarine warfare sonars in littoral waters is challenging, since the presence of sea mounts, underwater ridges, and other topographic features causes increased false alarm rates. Two important contributors to the raised false alarm rate are the signal-processing induced phenomenon called false alarm rate inflation (FARI) and the presence of sonar clutter, also referred to as non-Rayleigh distributed matched filter envelope in literature. Conventional constant false alarm rate (CFAR) algorithms fail to achieve a constant false alarm rate in all ranges and bearing in the presence of such effects. Given sufficient information on the bathymetry and the bottom properties, the occurrence of FARI may be estimated through the use of an acoustic model. This allows for more accurate estimates of the false alarm rate. Through measurements, the scatterer statistics for a given sonar in a given area may be estimated. Combining a FARI predicting scheme with knowledge on the scatterer statistics allows the estimation of the probability of false alarm in the presence of both FARI and sonar clutter. Here, we propose a new detection scheme that employs this approach to estimate a range- and bearing-dependent threshold that can be applied on normalized sonar data to achieve a CFAR even in the presence of FARI and clutter. The performance of the method is assessed through the use of receiver operating characteristic curves and is shown to outperform conventional CFAR algorithms, such as the cell averaging, greater of, and ordered statistics CFAR processors. The method is tested on both recorded and synthetic data. The robustness of the method is tested using synthetic data by introducing errors in the topography, sound speed, and scatterer statistics when estimating the probability of false alarm. The performance of the method decreases when introducing these errors, but it still outperforms the conventional CFAR processors.

Research on a New Comprehensive CFAR (Comp-CFAR) Processing Method

Since the clutter statistics of marine radar are non-stationary and difficult to ascertain, the constant false-alarm rate (CFAR) processor based on some clutter statistical characteristics is hard to obtain the CFAR performance. Especially, for clutter with long smearing effect characteristics, such as lognormal distribution, Pareto distribution, and K distribution, it is difficult to obtain CFAR characteristics using conventional CFAR processing techniques. The main consideration of this paper is to improve the robustness of CFAR, and the Comp-CFAR method is proposed according to the central limit theorem and the logarithmic compression principle of the signal. This method mainly includes clutter two-parameter logarithmic compression processing and accumulation of the magnitudes' average comprehensive CFAR processing. The experimental verification of CFAR characteristics and target detection performance with CFAR in four typical clutter environments shows that this method has better detection ability compared with the NCI-CFAR.

Detection and Discrimination of Ship Targets in Complex Background From Spaceborne ALOS-2 SAR Images

This paper proposes a novel method for ship detection and discrimination in complex background from synthetic aperture radar (SAR) images. It first implements a pixel-level land–sea segmentation with the aid of a global 250-m water mask. Then, an efficient multiscale constant false alarm rate (CFAR) detector with generalized Gamma distribution clutter model is designed to detect candidate targets in the sea. At last, eigenellipse discrimination and maximum-likelihood (ML) discrimination are designed to further exclude false alarm nonship objects in nearshore and harbor area. The proposed land–sea segmentation method is compared with multilevel Otsu method. The proposed multiscale ship detector is compared with conventional CFAR detectors. These contrast experiments show the good performance of our method. Finally, experiments undertaken on actual ALOS-2 SAR data show the efficacy of the proposed approach in detecting nearshore ship targets in a complex coastal environment.

Data-Dependent Clustering-CFAR Detector in Heterogeneous Environment

This paper devises a new constant false alarm rate (CFAR) detection scheme to deal with the problem of radar target detection in heterogeneous environment. The proposed scheme, called “clustering-CFAR detector,” is data dependent and composed of three stages: an adaptive clustering procedure that, exploiting the recorded measurements of the clutter environment, divides the detection area into different classes to provide auxiliary information, a dynamic reference cell selector that chooses appropriate secondary data according to the classes, and a conventional CFAR processor to make the final decision about the target presence. The performance of “clustering-CFAR detector” is analyzed by computer simulation and public radar measured data (IPIX data and MSTAR data), and compared with existing CFAR detectors. The results show that the new detector achieves a better performance in the aspects of terrain classification, control of false alarm points, and probability of detection.

A novel design of algorithm and framework for decentralized CFAR signal detection without prior information

In this paper, a fusion detection algorithm that focuses on decentralized CFAR (Constant False Alarm Rate) signal detection problem without prior information is proposed. In the algorithm, the threshold and test statistic of the detection fusion algorithm derive from the conventional CFAR detection method. At last a framework for decentralized CFAR signal detection is designed corresponding to the fusion algorithm. Simulation results illustrate that an almost optimal detection performance is obtained by the proposed algorithm.