Constant False Alarm Rate Detector Research Articles

CFAR Target Detector in Synthetic Aperture Radar

Introduction. Constant false alarm rate (CFAR) detectors have found application in synthetic aperture radar (SAR) systems. The operating principle of a classic cell averaging detector (CA-CFAR detector) is based on comparing the decision statistics in the test resolution element with an adaptive threshold, which is calculated from signals in the reference cells. The decision statistic is an estimate of the signal power. Therefore, target signal detection is based on the brightness contrast of the test and reference resolution cells. Such a detector is optimal provided that the noise background is homogeneous. In cases where the background homogeneity is violated, the quality of detection deteriorates. There are several known methods for improving the quality of detection (GO-CFAR, SO-CFAR, OS-CFAR, etc.). However, the precise principle of detection by brightness contrast in such CFAR detectors remains unchanged.Aim. To synthesize a CFAR detector that uses not only the brightness contrast between the test and reference resolution cells, but also the spectral differences of the signals.Materials and methods. The proposed CFAR detector uses estimates of the algebraic moments of the power spectral density of signals in range cells, based on which three decision statistics are calculated containing information about the power, the position of the energy center, and the width of the signal spectrum. The decision about the presence of a target in the test cell is carried out according to the 2/3 rule (2 threshold overshoots out of 3 comparisons).Results. A comparison of the proposed detector with the SO-CFAR detector, performed by computer simulation, showed that, under a signal-to-clutter ratio of -6 dB and a false alarm probability of 10-4, the detection probability of the proposed detector was 0.933 versus 0.708 for the SO-CFAR detector.Conclusion. The article proposes a three-parameter CFAR detector for a synthetic aperture radar system, in which the decision on the presence of a target in the test cell is made via estimation of the first three algebraic moments of the signal spectrum. The synthesized detection algorithm can also be used when detecting moving targets in SAR.

Two-sided automatic censoring CFAR detector based on the interquartile range in a heterogeneous Weibull clutter

As it is well known, CFAR (Constant False Alarm Rate) detectors are intended to regulate the PFA (Probability of False Alarm) in the presence of a clutter background whose mean power is unknown and/or varying. In heterogeneous clutter backgrounds, CFAR detectors false alarm control and detection efficiency rely upon their ability of clutter edge resilience and/or interfering targets rejection. Aiming to improve radar signal detection in a heterogeneous Weibull clutter, the two-sided automatic censoring IQR-CFAR (InterQuartile Range-CFAR) detector is introduced in this paper. This new detector first resorts to a logarithmic amplifier to dynamically achieve a Weibull to Gumbel transformation of the reference samples. Then, working on a cell-by-cell basis, the CFCR (Constant False Censoring Rate) algorithm retains and rejects samples based on the IQR within the reference window. Upon removal of the unwanted samples, the remaining data set is utilized to estimate the unknown biparametric linear detection threshold through the BLU (Best Linear Unbiased) estimates of the Gumbel distribution’s location and scale parameters. Extensive MC (Monte Carlo) simulations with both simulated and real data show that the suggested detector outperforms its challenging automatic censoring CFAR detectors found in the literature.

Adaptive radar pulse detection design based on difference of box filter

AbstractPrecise radar pulse detection is the premise of electronic support measures. A constant false alarm rate (CFAR) detection algorithm based on difference of box (DOB) filter is proposed to realise low‐complexity and environment‐adaptive radar pulse detection, and the principle behind the proposed algorithm is revealed. Specifically, the DOB filter is adopted to extract the rising edges and falling edges of radar pulses, and the signal variation law of probability density functions (PDFs) during the process of DOB filter are first theoretically analysed. Based on the derived PDFs, dynamic detection threshold and fixed threshold factor for the proposed CFAR algorithm based on DOB filter are deduced to detect the presence of radar pulses. Furthermore, the closed‐form expression of the detection probability is derived. Simulations and on‐board field programmable gate arrays implementation verify the superiority of the proposed CFAR algorithm based on DOB filter. Simulation results show that the proposed algorithm performs well in detecting various types of pulse signals, the detection probability ≥99% on condition that the signal‐to‐noise ratio ≥3 dB and false alarm probability = 10−8. Moreover, the proposed CFAR algorithm based on DOB filter can deal with both singular pulses and pulse on pulse signals.

Constant false alarm rate detection method in mixed Weibull distribution sea clutter

In maritime surveillance, sea clutter represents a major obstacle to target detection accuracy due to missed or false alarms. Several methods have been proposed to find the most efficient. However, most of these methods have only been tested with simulated data, and their applicability and success in a real environment cannot be guaranteed. This work proposes constant false alarm rate (CFAR) detection techniques tailored to mixed Weibull-distributed sea clutter. The mixed Weibull distribution was used to model a mixture of Gaussian and non-Gaussian data. For CFAR detection in mixed Weibull distribution and Weibull clutter, the log t CFAR (logt-CFAR), Weber Haykin CFAR (WHCFAR), Geometric Mean Order Statistic CFAR (GMOS-CFAR), Trimmed Mean Order Statistic CFAR (TMOS-CFAR), Inclusion/Exclusion CFAR (IE-CFAR), and Weber Haykin Order Statistic CFAR (WHOS-CFAR) algorithms were performed. Both synthetic and actual ice multi-parameter imaging X-band (IPIX) radar data were used for experimental verification of this distribution. Simulation studies demonstrated that the proposed detectors maintain the CFAR property in Weibull-distributed clutter, making them effective in controlled scenarios, and confirming how better is the detection performance compared to other detection methods. This research bridges a critical gap in radar signal processing by significantly improving the detection of targets in challenging clutter environments.

Object-Enhanced YOLO Networks for Synthetic Aperture Radar Ship Detection

Synthetic aperture radar (SAR) enables precise object localization and imaging, which has propelled the rapid development of algorithms for maritime ship identification and detection. However, most current deep learning-based algorithms tend to increase network depth to improve detection accuracy, which may result in the loss of effective features of the target. In response to this challenge, this paper innovatively proposes an object-enhanced network, OE-YOLO, designed specifically for SAR ship detection. Firstly, we input the original image into an improved CFAR detector, which enhances the network’s ability to localize and perform object extraction by providing more information through an additional channel. Additionally, the Coordinate Attention mechanism (CA) is introduced into the backbone of YOLOv7-tiny to improve the model’s ability to capture spatial and positional information in the image, thereby alleviating the problem of losing the position of small objects. Furthermore, to enhance the model’s detection capability for multi-scale objects, we optimize the neck part of the original model to integrate the Asymptotic Feature Fusion (AFF) network. Finally, the proposed network model is thoroughly tested and evaluated using publicly available SAR image datasets, including the SAR-Ship-Dataset and HRSID dataset. In comparison to the baseline method YOLOv7-tiny, OE-YOLO exhibits superior performance with a lower parameter count. When compared with other commonly used deep learning-based detection methods, OE-YOLO demonstrates optimal performance and more accurate detection results.

Semiparametric constant false alarm rate method for radar and sonar images

AbstractThis work proposed a novel constant false alarm rate detection method based on Gaussian mixture model (GMM). The main contribution contains two aspects. First, the Gabor wavelet is used to determine the GMM component number. Second, the expression of probability of false alarm is derived based on the GMM parameter.

A New Synthetic Aperture Radar Ship Detector Based on Clutter Intensity Statistics in Complex Environments

In complex environments, the clutter statistical characteristics of synthetic aperture radar (SAR) are inconstant, and the constant detection performance of a false alarm rate (CFAR) detector based on a clutter statistical model is also hard to achieve. As a result, the overestimated threshold leads to a degradation in detection probability. To this end, this paper proposes a SAR ship detector different from CFAR detectors, which is independent of traditional clutter statistical distribution models and the probability of a false alarm (PFA). The proposed detector aims to raise the ship detection probability and alleviate interference from complex environments such as multi-target areas, shores, and breakwaters. It estimates clutter-truncated thresholds based on clutter intensity statistics (CIS). Firstly, three statistical parameters, including the mean, standard deviation, and maximum intensity of background clutter contaminated by outliers, are calculated; secondly, these parameters are utilized to estimate the clutter-truncated threshold using the novel CIS; and finally, the pixel under test is determined according to the CIS detection rule. Compared with CFAR-based algorithms, CIS obtains a high probability of detection in complex environments. As for other aspects, the CIS detector is insensitive to the structure of the detection window, as well as the size. It is also computationally efficient due to its simple calculations. The superiority of the CIS detector is validated on scene-differed SAR images from the DSSDD dataset.

A PointNet-Based CFAR Detection Method for Radar Target Detection in Sea Clutter

A PointNet-Based CFAR Detection Method for Radar Target Detection in Sea Clutter

Waffle

The bathroom has consistently ranked among the most perilous rooms in households, with slip and fall incidents during showers posing a critical threat, particularly to the elders. To address this concern while ensuring privacy and accuracy, the mmWave-based sensing system has emerged as a promising solution. Capable of precisely detecting human activities and promptly triggering alarms in response to critical events, it has proved especially valuable within bathroom environments. However, deploying such a system in bathrooms faces a significant challenge: interference from running water. Similar to the human body, water droplets reflect substantial mmWave signals, presenting a major obstacle to accurate sensing. Through rigorous empirical study, we confirm that the interference caused by running water adheres to a Weibull distribution, offering insight into its behavior. Leveraging this understanding, we propose a customized Constant False Alarm Rate (CFAR) detector, specifically tailored to handle the interference from running water. This innovative detector effectively isolates human-generated signals, thus enabling accurate human detection even in the presence of running water interference. Our implementation of "Waffle" on a commercial off-the-shelf mmWave radar demonstrates exceptional sensing performance. It achieves median errors of 1.8cm and 6.9cm for human height estimation and tracking, respectively, even in the presence of running water. Furthermore, our fall detection system, built upon this technique, achieves remarkable performance (a recall of 97.2% and an accuracy of 97.8%), surpassing the state-of-the-art method.

A comparison of constant false alarm rate object detection algorithms for iceberg identification in L- and C-band SAR imagery of the Labrador Sea

Abstract. In this study, we pursue two objectives: first, we compare six different “constant false alarm rate” (CFAR) algorithms for iceberg detection in SAR images, and second, we investigate the effect of radar frequency by comparing the detection performance at C- and L-band. The SAR images were acquired over the Labrador Sea under melting conditions. In an overlapping optical Sentinel-2 image, 492 icebergs were identified in the area. They were used for an assessment of the algorithms' capabilities to accurately detect them in the SAR images and for the determination of the number of false alarms and missed detections. By testing the detectors at varying probability of false alarm (PFA) levels, the optimum PFA for each detector was found. Additionally, we considered the effect of iceberg sizes in relation to image resolution. The results showed that the overall highest accuracy was achieved by applying a log-normal CFAR detector to the L-band image (F score of 70.4 %), however, only for a narrow range of PFA values. Three of the tested detectors provided high F scores above 60 % over a wider range of PFA values both at L- and C-band. Low F scores were mainly caused by missed detections of small-sized (<60 m) and medium-sized (60–120 m) icebergs, with approximately 20 %–40 % of the medium icebergs and 85 %–90 % of small icebergs being missed by all detectors. The iDPolRAD detector, which is sensitive to volume scattering, is less suitable under melting conditions.

An improved radar clutter suppression by simple neural network

AbstractThe presented paper is further focused on the presentation and subsequent assessment of utilising a proposed Neural Network (NN) with simple architecture in the role of a signal preprocessing algorithm for the Constant False Alarm Rate detector and the fixed threshold detector applied on a Range‐Doppler (RD) map with the aim of radar clutter impact reduction and minimisation of processing time. Based on a comparison of all tested algorithm results, it is possible to state that utilising the proposed NN with simple architecture led to reducing the impact of radar clutter when detecting radar targets on RD maps created from provided datasets. Comparing the mean processing time tmean values of all tested algorithms, the authors can state that employing the proposed NN in combination with the fixed threshold detector led to a significant improvement in the computation time needed for processing one RD map while preserving the suppression of radar clutter and detection of the radar targets.

Experimental study of underwater operation scene with target perception framework

This paper presents a target perception framework aimed at enhancing diver safety and facilitating underwater operations by extracting critical information from underwater scenes. The framework employs a layered processing approach, which encompasses water column imaging, constant false alarm rate detection, and local feature analysis. To simulate the diver's underwater environment, we conducted experiments with three distinct fields of view: fixed down-looking, fixed front-looking, and mobile side-looking perspectives. Our experimental findings demonstrate the framework's ability to accurately differentiate between false targets, stationary targets, and moving targets within the underwater scenes, as well as to capture the motion trajectories of dynamic targets. Furthermore, the application of 3D reconstruction techniques to underwater scene data enables the generation of approximate stereoscopic representations of divers and bubble groups.

Spaceborne SAR Time-Series Images Change Detection Based on SAR-SIFT-Logarithm Background Subtraction

Synthetic Aperture Radar (SAR) image change detection aims to detect changes with images of the same area acquired at different times. It has wide applications in environmental monitoring, urban planning and resource management. Traditional change detection methods for spaceborne SAR time-series images typically adopt a pairwise comparison strategy to obtain multi-temporal change information. However, this kind of method has the problem of losing the overall change information, which is time consuming. To address this problem, this paper proposes a new change detection algorithm for spaceborne SAR time-series data based on SAR-SIFT-Logarithm Background Subtraction. This algorithm combines the SAR-SIFT image registration technology with Logarithm Background Subtraction. The method first preprocesses the input time-series data with steps like noise reducing and radiometric calibration. Then, the images will be coregistered by the SAR-SIFT step to avoid mismatches-induced detection performance degradation. Next, the parts that remained unchanged throughout the time period are modeled with a median filter to obtain the static background. The change information is then obtained via the subtraction of background and CFAR detection and clustering. The proposed algorithm is validated using the Sentinel-1 GRD and PAZ-1 time-series dataset. Experimental results demonstrate that the proposed method effectively detects the overall change information and reduces processing time compared to traditional pairwise comparison methods.

Sea clutter radar target detector based on autoregressive sieve bootstrap

The autoregressive sieve (ARS) bootstrap method can capture the correlation of time series and expand the original data set to the required volume. In this study, we introduce the ARS bootstrap into the matrix constant false alarm rate (CFAR) method to counter the inability of the Monte Carlo methods to extract the detection threshold due to the limited number of pulses under a certain false alarm probability. We assume the echo data of each range cell in a coherent processing interval (CPI) to be a stationary time series, and regard target detection as a two-sample hypothesis testing problem. We propose a statistic to test the equivalence of the autocovariance corresponding to two potential time series by using the correlation between pulses in a short pulse dataset, and analyze the validity of the ARS bootstrap for this statistic. The statistic distribution is obtained according to the bootstrap procedure and the threshold is obtained under fixed false alarm probabilities. Numerical experiments on the simulated and real sea clutter data were conducted to compare the detection results of the partial matrix CFAR detectors for the short and long pulse cases. With the introduction of the bootstrap method, we do not need to model the clutter amplitude distribution and the related statistical characteristics during target detection. To improve the detection probability with low signal-to-clutter ratio (SCR), we propose the two-sample autocovariance vector (ACV) detector based on the proposed statistic and use the correlation properties between pulses. We observed a 28% increase in detection probability when the SCR was 0 dB compared to other matrix CFAR methods, such as detectors based on Logarithmic Euclidean (LE) distance, Kernel Function-based Symmetric Kullback-Leibler (KSKL) divergence, and Total Bregman divergence (TBD) for the short pulse case.

Adaptive optimization technology of segmented reconstruction signal based on genetic algorithm for enhancing radar jamming effect

Given the improved capabilities of radar systems, addressing unfamiliar signals presents a challenge for radar jamming technology. To tackle this issue, this study proposes an adaptive technique for optimizing jamming waveforms to suppress multiple false targets in escort jamming scenarios. The objective is to minimize the detectability of false targets by fine-tuning phase modulation and individual waveform parameters. The optimization model adjusts the energy and delay of jamming waveform segments using intercepted radar signal phase modulation and direct forwarding. Real-time adaptation is achieved through the utilization of a genetic algorithm and radar constant false alarm rate detection based on received emissions. The key findings highlight the advantages of adaptivity in effectively suppressing false targets under diverse conditions. The technique successfully learns efficient waveforms through feedback, even without specific knowledge of the radar system. The optimized waveforms maintain consistent jamming impact across different constant false alarm rate settings, surpassing the limitations associated with fixed assumptions. The introduction of phase modulation enhances the resilience of false targets by creating noise-like characteristics. Remarkably, robust jamming is achieved with only 12 false targets, reducing complexity. The unified waveform design is particularly suitable for single platform jamming, eliminating the need for multiple jammers. Furthermore, the optimized waveforms demonstrate improved coverage of real targets under position errors. As a result, the approach exhibits versatility across various signals, processing methods, and scenarios. This study suggests that increased adaptability and the incorporation of machine learning techniques contribute to the advancement of radar jamming capabilities. By optimizing jamming waveforms, the adaptive approach presented in this study may enhance the effectiveness of countering advanced radar systems.

Accurate multi-target vital signs detection method for FMCW radar

The frequency modulated continuous wave (FMCW) radar has gained increasing attention in the field of noncontact vital signs detection. However, the detection of the target chest’s range bin is easily influenced by various clutters and interferences, posing numerous challenges to vital signs detection, particularly in scenarios involving multiple targets This paper proposes an anti-clutter and interference method that combinates moving target indication (MTI), constant false alarm rate (CFAR) detection, K-means clustering, and local search with moving average filtering to accurately detect multi-target vital signs. Moreover, it employs the extended differentiate and cross multiply algorithm for phase demodulation to overcome phase discontinuity. The proposed method is evaluated using a 77 GHz FMCW radar in an office environment. Experimental results demonstrate that the proposed method can effectively detect multiple targets amidst various clutters and small random body movement, yielding mean accuracies of 94.2 % and 95.1 % for breathing rate and heartbeat rate, respectively.

VIHCEMOS-CFAR Detector Based on Improved VI-CFAR

A variability index heterogeneous clutter estimate modified ordered statistics constant false alarm rate (VIHCEMOS-CFAR) detector based on improved VI-CFAR is proposed. First calculate the VI and MR values and then judge the clutter background: If the background is uniform, cell averaging (CA) target detection strategy is adopted; HCE detection strategy is adopted if it is clutter edge background; If it is a complex background composed of multiple targets or strong clutter, the OS detection strategy is adopted. The method of judging the interfering targets and strong clutter in the reference window by using the phase characteristics is used to calculate the accurate k. The Modified-OS algorithm with corrected k is proposed as the detection strategy in the multi-target environment to improve the target detection performance in practical projects. Through detailed experiments, it is proved that VIHCEMOS has adaptive clutter background capability and better detection performance than all other CFAR detection algorithms in multi-target environment.

Advancing Stepped-Waveform Radar Jamming Techniques for Robust False-Target Generation against LFM-CFAR Systems.

This study investigates the utilization of a stepped wave frequency modulation jamming technique in radar systems. The objective is to enhance the effectiveness and robustness of false target jamming in the presence of linear frequency modulation (LFM) radars employing constant false alarm rate (CFAR) detection. The proposed method combines stepped frequency modulation with full pulse delay/sum repeat jamming to enhance resilience against uncertainties in target parameters. Theoretical analysis and simulation experiments are conducted to establish relationships between key jammer parameters, such as frequency slope and power compensation, and performance metrics, like false target distribution and CFAR masking. The results demonstrate that the proposed technique effectively maintains a dense distribution of false targets surrounding the protected target, even in the presence of uncertainties in position and signal-to-noise ratio. In comparison to existing methods, the utilization of stepped-waveform modulation enables improved control over target distribution and CFAR masking. Adaptive power allocation compensates for parameter errors, thereby enhancing robustness. Simulation results reveal that the proposed approach significantly reduces the probability of detecting the true target by over 95% under uncertain conditions, while previous methods experienced degradation. The integration of stepped waveforms optimizes false target jamming, thereby advancing electronic warfare capabilities in countering advanced radar threats. This study establishes design principles for resilient jamming architectures and supports enhanced survivability against radars employing pulse compression and CFAR detection. Moreover, the concepts proposed in this study have the potential for extension to emerging radar waveforms.

Constant false alarm rate detection of pipeline leakage based on acoustic sensors

During the transportation of oil and gas pipelines, there are many potential factors that can lead to pipeline leakage with serious consequences, making automatic and real-time pipeline leakage detection urgent. In response to the inconvenience of manual detection, constant false alarm rate (CFAR) detection technique in radar target detection theory is introduced for pipeline leakage detection based on acoustic signals. In this paper, an automatic pipeline leakage detection algorithm based on an improved CFAR detector is proposed. The improved CFAR detection is executed after pre-processing the acoustic signals so as to adaptively set the detection threshold to achieve the purpose of automatic detection of pipeline leakage incidents. A simulated leakage test of a real pipeline is used for validation, and the proposed method achieves detection accuracies of 84.6%, 97.7%, and 98% for different leakage diameter settings, i.e., 5 mm, 7 mm, and 10 mm leak hole diameters, respectively, with an overall accuracy of 94.1%, while the false alarm rates are 3.3%, 0.7%, and 0, respectively, as well as an overall of 1.2%. The results of experimental data based on real scenarios demonstrate the effectiveness of the proposed method.

Clutter and Interference Cancellation in River Surface Velocity Measurement with a Coherent S-Band Radar

Using a Doppler radar to measure river surface velocity is a safe and effective technique. However, the measurement would be severely affected by undesired targets that enter the illuminated area of radar. The issue is worsened when measuring the surface velocities of wide rivers because undesired targets such as boats and ships are more likely to be present. The buoy boats fixed on the river surface and cargo ships sailing on the river would generate ground clutter and moving target interference, respectively. The clutter and interference can mask the signal produced by the Bragg scattering and seriously bias the extraction result of river surface velocity. This paper proposes two effective methods to remove ground clutter and moving target interference, respectively. One is an improved phase-based method that eliminates ground clutter after obtaining its boundaries through the phase in the frequency domain, and another is an improved constant false alarm rate (CFAR) detector that combines smallest-of selection logic and a multi-step deletion scheme to detect and remove interference in the time-Doppler spectrum. The experimental data measuring the surface velocity of the Yangtze River with a coherent S-band radar in July 2022 are used to verify the proposed methods. The results show that the proposed methods can effectively remove ground clutter and moving target interference, respectively. After clutter and interference cancellation, a more reasonable result of river surface velocity distribution can be extracted. Therefore, the methods proposed in this paper can be used to remove clutter and interference when extracting the surface velocity of rivers with numerous undesired targets.