Constant False Alarm Rate Technique Research Articles

Peaks Detector Algorithm after CFAR for Multiple Targets Detection

The constant false alarm rate (CFAR) algorithm is a strong technique to detect and track dynamic targets in an environment of an unknown noise floor. Multiple reflections of a pulse from a target and different signal processing techniques applied to the received pulse, make it spread along the range and/or Doppler axis. Spreading of a pulse results in a cluster of targets detection for a single target when the CFAR technique is applied to it. This causes difficulties in calculating those target’s parameters which require only a single maximum peak for a target, such as Radar cross-section (RCS), relative phase, etc. This manuscript proposes a solution, which extracts a single independent peak for a target that had clusters of peaks after CFAR. The novelty of the algorithm is that it works well to extract a single peak for each of all targets in the multiple targets environment, as compared to the conventional global maxima finding techniques which outputs only one target of the maximum amplitude while suppressing the rest of the small targets. The algorithm is basically a local maxima finder algorithm termed as peaks detector algorithm. An attractive feature of this algorithm is that it neither disturbs the Probability of false alarm rate (Pfa) of CFAR nor it affects the probability of detection (Pd) of a target. The algorithm is tested and its performance is evaluated in a multiple targets environment on the output of 1D and 2D CFAR.

A New Model-Data Co-Driven Method for Radar Ship Detection

Target detection played a vital role in radar image interpretation. The early studies usually relied on the model-driven strategy, such as the constant false alarm rate technique. This family of a method aimed to find the statistical anomaly points. The detection performance was, therefore, dependent on the statistical modeling of clutter. They suffered from the model mismatch in realistic scenarios. Recent years have witnessed a resurgence of deep neural networks, by which the high-level representations can be learned. They achieved amazing performance in many fields. However, large amounts of training samples were required to estimate the weights and the bias. For radar sensors, it is costly and difficult to collect much data with label information. To solve the problem, this article presents a model-data co-driven ship detection strategy. The clutter of radar images is first modeled by the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathcal{ G}}$ </tex-math></inline-formula> distribution. The scale and the shape parameters are estimated by the data available. They are used to simulate the sea clutters. Second, the labeled targets available are extended to several different azimuths to construct the target set. Finally, the targets randomly chosen from the target set are embedded into the sea clutter. The synthesized images and the original ones are combined to train the deep architecture. Multiple comparative studies are performed to demonstrate the advantage of the proposed strategy.

Robust variability index CFAR for non‐homogeneous background

Radar signal detection using constant false alarm rate (CFAR) detectors encounters many non-ideal situations making it difficult to characterise the background. These include the presence of multiple targets, clutter edges and their combination in the reference window. Designing an efficient CFAR for these situations is a non-trivial problem. Algorithms based on ordered statistics (OS), outlier rejection using sorting and sample by sample hypothesis testing, variability index (VI), ordered data VI are proposed in the literature. These approaches require expensive sorting or prior information on the depth of censoring. In this study, the authors propose robust VI CFAR (RVI-CFAR) that obviates sorting. RVI-CFAR computes the threshold in multiple stages. The first stage uses VI-CFAR to determine an adaptive threshold. Outlier rejection in the computation of background, mean ratio (MR) and VI is carried out in subsequent stages. The updated MR and VI statistics are used to refine switching decisions at every stage of processing. RVI-CFAR exhibits low CFAR loss in homogeneous and multiple target scenarios, meanwhile achieving superior performance compared to other censored CFAR techniques. The proposed RVI-CFAR is evaluated and shown to be robust for all the cases of non-homogeneity compared to OS CFAR.

Multipath Analysis and Exploitation for MIMO Through-the-Wall Imaging Radar

This paper considers the multipath modeling and exploitation problems for multiple input multiple output through-the-wall imaging radar. A multipath propagation model referring to two parallel walls is established, and the position distribution characteristics of the multipath ghosts in radar image are analyzed by means of geometric approximation. Therein, we find a feature that the locations of the walls can be obtained using the geometric relationship between the true target and its multipath ghosts. After utilizing constant false alarm rate technique and clustering approach, the coordinates of the multipath ghosts and the concealed target are achieved, and the positions of the two walls are obtained. Finally, simulation and experimental results validate the correctness of the multipath model and the validity of the multipath-exploitation-based wall localization method.

Radar High-Speed Maneuvering Target Detection Based on Three-Dimensional Scaled Transform

This paper presents a novel coherent high-speed maneuvering target detection algorithm, which is based on the three-dimensional (3-D) scaled transform. This algorithm coherently integrates the echo energy into a peak in a 3-D parameter space and estimates the target's radial velocity and acceleration simultaneously by the peak detection technique. Thereafter, compensating off the across range unit and Doppler frequency migration via estimations, this algorithm coherently integrates the echo energy in the range-Doppler space and uses the constant false alarm rate technique to complete the target detection. The cross term of the proposed algorithm is also analyzed and its characteristic indicates the applicability in the scenario of multiple targets. The computational complexity, resolution, peak-to-sidelobe level (PSL), and detection performance are analyzed and compared with several typical algorithms, which leads us to conclude that the proposed algorithm can strike a balance between the computational complexity and detection performance with high resolution and PSL. Finally, experiments with the real measured radar data are conducted to verify the proposed algorithm.

Artificial intelligence techniques for small boats detection in radar clutter. Real data validation

Artificial intelligence techniques were applied for detecting small moving targets in maritime clutter environments. Neural detectors are considered to approximate the Neyman–Pearson (NP) in composite hypothesis testing problems. Sub-optimum approaches based on the Constrained Generalized Likelihood Ratio (CGLR) were analysed, and compared to conventional implementations based on Doppler filtering that are designed to filter clutter and improve the Signal-to-Interference Ratio, and Constant False Alarm Rate techniques. The CGLR performance was significantly better at the expense of a high computational cost. As a solution, neural network training sets were designed for approximating the NP detector. The detection of small boats in Gaussian clutter was the defined case study in order to assume the design hypothesis of the conventional solutions and to study their performance under their most favourable conditions. Detection schemes were evaluated using real radar data. Neural solutions based on Second Order Neural Networks provide the best results, being able to approximate the CGLR with a significantly low computational cost compatible with real-time operations.

Radar high-speed maneuvering target detection based on joint second-order keystone transform and modified integrated cubic phase function

Long-time coherent integration is an effective means to improve the radar detection ability of high-speed maneuvering targets with jerk motion. However, the range migration (RM) and Doppler frequency migration (DFM) have a great impact on the integration performance. To overcome these problems, a unique method, called the second-order keystone transform modified integrated cubic phase function (SKT-MICPF), is proposed. In this method, the velocity compensation and SKT are jointly employed to correct the RM. After the RM correction, the azimuth echoes of a range cell where a target is located can be modeled as a cubic phase signal (CPS), whose chirp rate (CR) and quadratic CR are related to the target’s radial acceleration and jerk, respectively. Thereafter, an effective parameters’ estimation algorithm for CPS, called MICPF, is proposed and applied to compensate the DFM. After that, coherent integration and target detection are accomplished via the fast Fourier transform and constant false alarm rate technique, successively. Compared with the improved axis rotation discrete chirp Fourier transform, the SKT-MICPF achieves close detection performance, but greatly reduces the computational complexity. The results of simulation and real radar data demonstrate the validity of the proposed algorithm.

Multi-Chromatic Analysis of SAR Images for Coherent Target Detection

This work investigates the possibility of performing target analysis through the Multi-Chromatic Analysis (MCA), a technique that basically explores the information content of sub-band images obtained by processing portions of the range spectrum of a synthetic aperture radar (SAR) image. According to the behavior of the SAR signal at the different sub-bands, MCA allows target classification. Two strategies have been experimented by processing TerraSAR-X images acquired over the Venice Lagoon, Italy: one exploiting the phase of interferometric sub-band pairs, the other using the spectral coherence derived by computing the coherence between sub-band images of a single SAR acquisition. The first approach introduces the concept of frequency-persistent scatterers (FPS), which is complementary to that of the time-persistent scatterers (PS). FPS and PS populations have been derived and analyzed to evaluate the respective characteristics and the physical nature of the targets. Spectral coherence analysis has been applied to vessel detection, according to the property that, in presence of a random distribution of surface scatterers, as for open sea surfaces, spectral coherence is expected to be proportional to sub-band intersection, while in presence of manmade structures it is preserved anyhow. First results show that spectral coherence is well preserved even for very small vessels, and can be used as a complementary information channel to constrain vessel detection in addition to classical Constant False Alarm Rate techniques based on the sole intensity channel.

Modified log-ratio operator for change detection of synthetic aperture radar targets in forest concealment

Inherent speckle noise greatly limits the performance of change detection in synthetic aperture radar (SAR) images. In order to reduce the influence of such speckle noise, a modified-log-ratio (MLR) operator for change detection of targets in forest concealment is proposed. By performing a logarithm of the linear transform ratio on the pixel amplitude using multilook processing to generate a difference image, the presented MLR operator is theoretically proven to be capable of improved restraining of the negative effect of speckle noise over the conventional log-ratio operator. A Gaussian distribution of the MLR statistics is assumed, which facilitates the adaptive detection of targets using a constant false alarm rate technique. Experimental results based on the public VHF-band CARABAS-II SAR image dataset validate the effectiveness of the proposed operator.

TOA Estimation Technique for IR-UWB Based on Homogeneity Test

This paper deals with the estimation of the time of arrival (TOA) of ultra-wideband signals under IEEE 802.15.4a channel models. The proposed approach is based on a randomness test and consists of determining whether an autoregressive (AR) process modeling an energy frame is random or not by using a distance to measure the randomness. The proposed method uses a threshold that is derived analytically according to a preset false alarm probability. To highlight the effectiveness of the developed approach, simulation setups as well as real data experiments are conducted to assess the performance of the new TOA estimation algorithm. Thereby, the proposed method is compared with the cell averaging constant false alarm rate technique, the threshold comparison algorithm, and the technique based on maximum energy selection with search back. The obtained results are promising, considering both simulations and collected real-life data.

CFAR Detectors for MIMO Radars

CFAR (Constant False-Alarm Rate) processors are useful for detecting radar targets in a background for which the parameters in the statistical distribution are not known. A variety of CFAR techniques such as CA (Cell Averaging), Go (Greatest Of), SO (Smallest Of), OS (Ordered Statistics) and ACMLD (Automatic Censored Mean-Level Detector) processors have been proposed for SISO (Single Input–Single Output) radars in a non-homogeneous background. In this paper, conventional CFAR algorithms including CA, SO, OS and ACMLD processors are generalized for MIMO (Multiple Input–Multiple Output) radars. The exact expressions for false-alarm probabilities of the proposed algorithms in a homogeneous background are presented. In addition, the detection performance of the proposed detectors is studied by means of simulation in the presence of interfering targets and also colored Gaussian clutter. Besides, the proposed CFAR processors are compared, and it is shown that the ACML-based algorithm is superior to the other investigated methods.

Target detection in diagnostic ultrasound: Evaluation of a method based on the CLEAN algorithm

A technique is proposed for the detection of abnormalities (targets) in ultrasound images using little or no a priori information and requiring little operator intervention. The scheme is a combination of the CLEAN algorithm, originally proposed for radio astronomy, and constant false alarm rate (CFAR) processing, as developed for use in radar systems. The CLEAN algorithm identifies areas in the ultrasound image that stand out above a threshold in relation to the background; CFAR techniques allow for an adaptive, semi-automated, selection of the threshold. Neither appears to have been previously used for target detection in ultrasound images and never together in any context. As a first step towards assessing the potential of this method we used a widely used method of simulating B-mode images (Field II). We assumed the use of a 256 element linear array operating at 3.0MHz into a water-like medium containing a density of point scatterers sufficient to simulate a background of fully developed speckle. Spherical targets with diameters ranging from 0.25 to 6.0mm and contrasts ranging from 0 to 12dB relative to the background were used as test objects. Using a contrast-detail analysis, the probability of detection curves indicate these targets can be consistently detected within a speckle background. Our results indicate that the method has considerable promise for the semi-automated detection of abnormalities with diameters greater than a few millimeters, depending on the contrast.

A New CFAR Processor Based on Guard Cells Information

Radar detection procedures involve the comparison of the received signal amplitude to a threshold. In order to obtain a constant false-alarm rate (CFAR), an adaptive threshold must be applied reflecting the local clutter situation. This paper presents an intelligent CFAR technique based on comparing the performance of five existing CFAR processors at different target and clutter situations. The proposed intelligent CFAR processor selects the adaptive threshold which is calculated by the best CFAR processor for certain environmental condition. The selection criterion based on comparing the informationcontained in the guard cells to those contained in test and window cells. This comparison is done to differentiate between single target, multiple targets, and clutter transition situations. Performance comparison through the Receiver Operating Characteristic (ROC) is carried out to validate the superiority of the proposed CFAR technique at different target and clutter situations.

Detection of Ground Moving Targets for Two-Channel Spaceborne SAR-ATI

Many present spaceborne synthetic aperture radar (SAR) systems are constrained to only two channels for ground moving target indication (GMTI). Along-track interferometry (ATI) technique is currently exploited to detect slowly moving targets and measure their radial velocity and azimuth real position. In this paper, based on the joint probability density function (PDF) of interferogram's phase and amplitude and the two hypotheses and clutter plus signal, several constant false alarm rate (CFAR) detection criteria are analyzed for their capabilities and limitations under low signal-to-clutter ratio (SCR) and low clutter-to-noise ratio (CNR) conditions. The CFAR detectors include one-step CFAR detector with interferometric phase, two-step CFAR detectors, and two-dimensional (2D) CFAR detector. The likelihood ratio test (LRT) based on the Neyman-Pearson (NP) criterion is exploited as an upper bound for the performance of the other CFAR detectors. Performance analyses demonstrate the superiority of the 2D CFAR techniques to detect dim slowly moving targets for spaceborne system.

Constant false alarm rate detection of saccadic eye movements in electro-oculography

The analysis of eye movements has proven to be valuable in both clinical work and research as well as in other fields besides medicine. The detection of saccadic eye movements and the extraction of related saccade parameters, such as maximum angular velocity, amplitude, and duration, are usually performed during the analysis of electro-oculographic (EOG) signals. This article considers a saccade detection method that is based on the constant false alarm rate technique, in which the detection sensitivity is continuously adjusted on the basis of the observed signal in order to keep the number of false alarms constant. The method is computationally efficient, it can operate autonomously without user intervention, and it is capable of detecting saccades in a sequential fashion. Therefore, the method finds potential use in applications that require automated analysis of electro-oculographic signals. Because of the constant false alarm rate property, the method can also perform in situations where ideal measurement conditions cannot be guaranteed and noise presents a considerable problem.

Network Intrusion Detection Using CFAR Abrupt-Change Detectors

In this paper, the constant false alarm rate (CFAR) detectors are proposed for network intrusion detection. By using an autoregressive system to model the network traffic, predictor error is shown to closely follow a Gaussian distribution. CFAR detector approaches are then developed on the prediction error distribution. In the present study, we consider the optimal CFAR, the cell-averaging CFAR, and the order statistics CFAR. The use of these CFAR techniques can significantly improve the detection performance. In addition, we propose the use of fusion of these CFAR detectors by using Dempster-Shafer and Bayesian techniques. Computer simulations based on the DARPA traffic data show that the proposed approach achieves higher detection probabilities than the conventional detection method. Even under different types of attacks, the intrusion detection performances based on the proposed CFAR detectors shows consistent improvement.

CFAR detection of weak target in clutter using chaos synchronization

AbstractIn this paper, a novel constant false alarm rate (CFAR) approach for detecting weak targets in sea clutter spectrum based on chaos synchronization is proposed. The weak target signal is detected when the synchronization between two identical chaotic systems is realized, even if the target spectrum lies inside the clutter spectrum. The threshold for the proposed CFAR detection is derived theoretically. The proposed chaos‐synchronization‐based CFAR technique is shown to be able to enhance the detectability of the target when the signal‐to‐clutter ratio and signal‐to‐noise ratio are low. Numerical experiments based on real radar sea clutter data confirm the effectiveness of the proposed chaos‐synchronization‐based CFAR detection method. The performance is superior to those of the standard autoregressive estimation‐based and the cell‐averaging CFAR detectors. Copyright © 2007 John Wiley &amp; Sons, Ltd.

Exact performance analysis of OS modified versions with noncoherent integration in nonideal situations

The radar signals returning from the targets being illuminated are usually accompanied by thermal noise and clutter. Constant false alarm rate (CFAR) processors are useful for detecting these targets in a background for which the parameters of the statistical distribution are not known and may be nonstationary. The ordered-statistics (OS) CFAR technique has been proven to work satisfactorily in both multiple-target and nonuniform clutter cases. Unfortunately, the large processing time taken by this scheme limits its practical uses. The modified versions of the OS processor have been proposed to replace it in these applications. They can reduce the processing time of the single-window OS detector in half without changing its useful properties. Our goal in this paper is to provide a complete detection analysis for the OS processor along with ordered-statistic greatest-of (OSGO) and ordered-statistic smallest-of (OSSO) modified versions, for M postdetection integrated pulses when the operating environment is nonideal. Analytical results of performance are presented in both multiple-target situations and in regions of clutter power transitions. The primary and the secondary interfering targets are assumed to be fluctuating in accordance with the Swerling II target fluctuation model. As the number of noncoherently integrated pulses increases, lower threshold values and consequently better detection performances are obtained in both homogeneous and multiple-target background models. However, the false alarm rate performance of OSSO-CFAR scheme at clutter edges worsens with increasing the postdetection integrated pulses. As predicted, the OSGO-CFAR detector accommodates the presence of spurious targets in the reference window, given that their number is within its allowable range in each local window, and controls the rate of false alarm when the contents of the reference cells have clutter boundaries. The OSSO-CFAR scheme is useful in the situation where there is a cluster of radar targets amongst the estimation cells.

The impact of false alarm mitigation on surface landmine detection in MWIR imagery

Surface landmine and minefield detection from airborne imagery is a difficult problem. As part of the minefield detection process, anomaly detection is performed to identify potential landmines in individual airborne images. Post-processing is performed on the initial landmines identified to reduce the number of false alarms, referred to as false alarm mitigation. In this research, a circular harmonics transform image processing approach (the CHT method) and a constant false alarm rate technique (the RX approach) are investigated for surface landmine detection and false alarm mitigation in medium wave infrared (MWIR) image data. The false alarm mitigation approach integrates the CHT and RX methods to identify candidate landmine locations with one technique at a given false alarm rate and applies the other technique to confirm landmine locations and eliminate potential false alarms. Individual detector and false alarm mitigation experimental results are presented for 31 daytime and 43 nighttime MWIR images containing 76 and 142 landmines, respectively. At a 0.9 desired probability of landmine detection, experimental results show that false alarm mitigation reduces the false alarm rate by as much as 84.3% and 13.7% for daytime and nighttime images, respectively, maintaining the probability of detection at 0.85 and 0.90, respectively.

연근해 소형 어선의 레이더 정보 수록 및 해석 시스템 개발 - CFAR에 의한 레이더 잡음 억제 -

연근해 소형 어선에 널리 탑재되어 있는 소형 레이더 장치에 radar target extractor를 인터페이스하여 레이더 선호를 수록, 처리 및 해석하기 위한 PC based radar system 을 구축하고, cell averaging CFAR 처리장치를 통해 실제의 레이더 echo 신호를 처리하여 오경보 확률의 설정치 변화에 따른 echo영상의 변화패턴을 레이더 스코프상에서 직접 비교, 분석한 결과 및 레이더 영상신호의 음영구역의 발생대역폭을 추정한 결과를 요약하면 다음과 같다. 1. 레이더 선호의 해석을 통해 추출된 표적의 운동벡터 및 방위, 거리, 속력, CPA, TCPA 등과 같은 ARPA 정보를 실시간으로 이동 궤적과 함께 PC 모니터상에 구현하고 있기 때문에 소형 레이더 시스템에 이 장치를 부착하면 저가의 비용으로써 ARPA 정보의 취득이 기능하다. 2. ideal threshold 에 의한 표적검출성능을 개선시키기 위해 cell averaging CFAR processor 의 CUT전후에 각각 3 개의 guard cell과 이 cell의 좌<TEX>$.$</TEX>우측에 각각 20개씩, 총 40개의의 reference cell를 설치하여 레이더의 video 신호를 입벽한 후, 오경보 확률 10<TEX>$\^$</TEX>-0.25/∼10<TEX>$\^$</TEX>-1.0/의 범위에 대하여 설정치를 점차증가시켜 갈 때, 레이더 영상신호는 10<TEX>$\^$</TEX>-0.75/ 의 설정치에서 가장 양호한 clutter 제거효과를 나타내었다. 3. 레이더 스코프상에서 영상신호를 관찰하면서 cell averaging CFAR 의 오경보 확률을 적정하게 제어하면 지금까지의 ideal threshold level 에 의한 잡음억제기법에서 나타나는 선박영상의 과도한 레벨약화현상을 보완할 수 있을 것으로 판단한다. 4. 부산 용호만에 정박중인 예인선의 레이더 신호를 해석하여 영상의 음영패턴과 음영 대역폭을 추정한 결과, 예인선의 유효높이는 약 1.2 m 이었고, 이들 음영효과의 정량적 해석을 통한 해상표적의 형상정보는 향후 3차원 레이더 영상을 구현하는 데 그 기초자료가 될 것으로 판단된다. This paper describes on the suppression of sea clutter on marine radar display using a cell-averaging CFAR(constant false alarm rate) technique, and on the analysis of radar echo signal data in relation to the estimation of ARPA functions and the detection of the shadow effect in clutter returns. The echo signal was measured using a X -band radar, that is located on the Pukyong National University, with a horizontal beamwidth of <TEX>$$3.9^{\circ}$$</TEX>, a vertical beamwidth of <TEX>$20^{\circ}$</TEX>, pulsewidth of <TEX>$0.8 {\mu}s$</TEX> and a transmitted peak power of 4 ㎾ The suppression performance of sea clutter was investigated for the probability of false alarm between <TEX>$l0-^0.25;and; 10^-1.0$</TEX>. Also the performance of cell averaging CFAR was compared with that of ideal fixed threshold. The motion vectors and trajectory of ships was extracted and the shadow effect in clutter returns was analyzed. The results obtained are summarized as follows；1. The ARPA plotting results and motion vectors for acquired targets extracted by analyzing the echo signal data were displayed on the PC based radar system and the continuous trajectory of ships was tracked in real time. 2. To suppress the sea clutter under noisy environment, a cell averaging CFAR processor having total CFAR window of 47 samples(20+20 reference cells, 3+3 guard cells and the cell under test) was designed. On a particular data set acquired at Suyong Man, Busan, Korea, when the probability of false alarm applied to the designed cell averaging CFAR processor was 10<TEX>$^{-0}$</TEX>.75/ the suppression performance of radar clutter was significantly improved. The results obtained suggest that the designed cell averaging CFAR processor was very effective in uniform clutter environments. 3. It is concluded that the cell averaging CF AR may be able to give a considerable improvement in suppression performance of uniform sea clutter compared to the ideal fixed threshold. 4. The effective height of target, that was estimated by analyzing the shadow effect in clutter returns for a number of range bins behind the target as seen from the radar antenna, was approximately 1.2 m and the information for this height can be used to extract the shape parameter of tracked target..