Computationally efficient CFAR detector for 𝒦-distributed SAR clutter

The paper proposes a novel constant false alarm rate (CFAR) detector using Markov chain models, an innovative new technique that will utilize the finer resolution of RADARSAT-2 to yield improved detection performance for higher-resolution objects. The Markov chain based CFAR detector extends traditional PDF based CFAR detection to first-order Markov chain model by considering both correlation between neighboring pixels and PDF information in CFAR detection. With the additional correlation information, the proposed approach results in advancing the performance of conventional CFAR detectors. Our both analytical and experimental results both show that the new Markov chain CFAR detector can improve the conventional PDF CFAR detector about 30% in terms of detection probability gain and about 2.84 dB in terms of signal-to-clutter ratio gain.

This paper proposes a new Markov-chain-based constant false alarm rate (CFAR) detector for polarimetric data using low-level data fusion and high-level decision fusion. The Markov-chain-based CFAR detector extends traditional probability density function (pdf) based CFAR detection to first-order Markov chain model by considering both correlation between neighboring pixels and pdf information in CFAR detection. With the additional correlation information, the proposed approach results in advancing the performance of conventional CFAR detectors. Moreover, to take advantage of full polarizations of polarimetric data, various data fusion methods are considered to improve detection performance, including polarimetric transformation, principal component analysis, and decision fusion. Our experimental results confirm the superiority of the new Markov chain polarimetric CFAR detector over conventional pdf-based CFAR detectors.

Constant false alarm rate (CFAR) detection has been widely used in through-the-wall radar. However, there is a low probability of detection for transverse motion when using the CFAR detection in the slow time dimension; while it is easy to misjudge fixed targets with strong reflection to moving targets when CFAR detection used in the fast time dimension, due to the presence of residual clutter suppression. Aimed at the above problems, this paper proposes a kind of one dimension CFAR detection algorithm combined with fast and slow time. Experiments turn out that the algorithm can well solve the problems of low detection probability of traverse movement in the slow time CFAR detection and the false alarm of strong reflection targets in the fast time CFAR detection. The algorithm has the merits of small amount of computation, simple operation and strong environment adaptability.

A fuzzy statistical normalization for target detection in active sensing data is proposed in this paper. The first stage of the fuzzy statistical normalization is the Fuzzification of the active sensing data. Then the fuzzy inference and defuzzification operation based on statistical method and alpha-cut approach are performed, which not only attenuate the heavier tailed clutter data values, but also enlarge the lower shadow area noise data values. The constant false alarm rate (CFAR) detector based on fuzzy statistical normalization firstly estimates the background level with the normalized data, and then detects the target signal with the original active sensing data based on the estimated background level. Performance comparison between the proposed CFAR detector with outlier rejection based on fuzzy statistical normalization and the conventional CFAR detectors is carried out to validate the superiority of the proposed fuzzy statistical normalization in CFAR detection. The results show that the CFAR detector with fuzzy statistical normalization is robust.

In this paper, a constant false alarm rate (CFAR) target detection algorithm is presented. Here, lognormal and Weibull distributions are considered for modeling clutter amplitude of synthetic aperture radar(SAR) images. Then Kullback-Leibler (KL) goodness-of-fit test is employed to evaluate the histogram matching accuracy of estimated probability density functions (pdfs) of selected models with SAR clutter data histogram. With the use of appropriate background clutter model, CFAR detection algorithm is applied to moving and stationary target acquisition and recognition(MSTAR) data. Experimental results demonstrate that, accuracy of CFAR detector is more for Weibull clutter compared to the same for lognormal clutter.

MLP-CFAR for improving coherent radar detectors robustness in variable scenarios

Generalized OS CFAR detector with noncoherent integration

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.

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.

Using a linear feature mapping framework, a detector and classifier were developed for detecting and recognizing 2D target patterns in data collected by the SRI ultra wideband synthetic aperture radar (SAR), where limited prior knowledge of the target pattern and clutter statistics was known. Preliminary results on 0.1 square kilometers of the illustrative example site show that this linear feature mapping detector (LFMD) improves the receiver operating characteristic (ROC) curve by 1.5 orders of magnitude over the amplitude only 2-parameter constant false alarm rate (CFAR) detector, and the linear feature mapping classifier (LFMC) was able to classify 100% of the targets with no false alarms. In this report, the performances of both LFMD and LFMC are evaluated using ROC curves for 54 square kilometers of UHF SAR clutter data. Extensive experimental performance analysis using the large clutter data set shows that the LFMD and LFMC are very effective in detecting and classifying targets embedded in intense clutter background in UHF SAR data. Performance evaluation on 54 square kilometers of clutter data shows that the LFMD has only 0.3 false alarm per square kilometer and approximately 3 orders of magnitude improvement over the amplitude-only 2- parameter CFAR detector at probability of detection Pd equals 100%. The LFMC yields 100% correct classification by perfectly discriminating against the detected potential targets from the clutter data as nontargets.© (1995) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Complex approximated message passing (CAMP) is an iterative recovery algorithm for L1 regularization reconstruction which can achieve sparse and non-sparse estimations of original signal simultaneously. This paper demonstrates a CAMP-based synthetic aperture radar (SAR) image regularization reconstruction method along with a constant false alarm rate (CFAR) detection via the output non-sparse image of CAMP iterative algorithm. Compared with iterative thresholding algorithm (ITA) and orthogonal matching pursuit (OMP), the conventional L 1 regularization reconstruction techniques, it not only can improve SAR image performance, but also its non-sparse estimation retains a similar background statistical distribution as conventional matched filtering (MF)-based techniques, which can be used for CFAR detection efficiently. Simulated and experimental results validate the effectiveness of the designed CFAR detector for the CAMP reconstructed SAR image.

Achieving reliable target detection in the field of sonar imagery represents a significant challenge due to the complex underwater interference patterns characterized by speckle noise, tunnel effects, and low-signal-to-noise ratio (SNR) environments. Currently, constant false alarm rate (CFAR) detection denotes a fundamental target detection method in sonar target recognition. However, conventional CFAR methods face some limitations, including a slow computational speed, a high false alarm rate (FAR), and a notable missed detection rate (MDR). To address these limitations, this study proposes an innovative segmentation–detection framework. The proposed framework employs a global segmentation algorithm to identify regions of interest containing potential targets, which is followed by localized two-dimensional CFAR detection. This hierarchical framework can significantly improve computational efficiency while reducing the FAR, thus enabling the practical implementation of advanced, computationally intensive CFAR detection methods in real-time target detection in sonar imagery. In addition, an innovative segmented-ordered-weighting CFAR (SOW-CFAR) detection method that integrates multiple weighting windows to implement ordered weighting of reference cells is developed. This method can effectively reduce both the FAR and MDR through optimized reference cell processing. The experimental results demonstrate that the proposed method can achieve superior detection performance in sonar imagery applications compared to the existing methods. The proposed SOW-CFAR detection method can achieve fast and accurate target detection in the sonar imagery field.

The objective of the proposed work is to develop optimal polarimetric Constant False Alarm Rate (CFAR) detector for ship detection. Polarimetric transformations and decompositions, clutter analysis, modeling, Principal Component Analysis (PCA), and multi-CFAR detection are the necessary components of optimal polarimetric CFAR ship detectors. The resulting CFAR detector outperforms the conventional polarimetric CFAR detector by providing higher probability of detection. Optimal polarimetric CFAR detection procedures are proposed in this report. Given the simulated polarimetric RADARSAT-2 data, different polarimetric transformations and decompositions are applied. The resulting images will be transmitted to an adaptive Principal Component Analysis (PCA) block. Through the adaptive PCA block, the image of the first principal component which has the highest SNR among all the images (including the original, transformed/decomposed images, and the images after the adaptive PCA) will be used for ship detection. Optimal multi-CFAR detection will be applied to this image and then the final decision will be made.

For high-resolution synthetic aperture radar (SAR), constant false alarm rate (CFAR) detectors are widely used to separate targets from background and it is proved that generalised gamma distribution (GΓD) can model the non-homogeneous clutter appropriately. However, CFAR detectors based on GΓD possess two problems. First, the methods for solving highly nonlinear equations of GΓD resort to numerical iterative algorithms, which are computationally expensive. To avoid this, the authors present a novel analytic solution for parameter estimation of GΓD by exploiting a third-order approximation of the polygamma function. This novel analytic solution can result in more accurate parameter estimation and can fit a wider range of the parameters. The second problem is that in a multi-target SAR image, some target pixels may be classified as clutter pixels. To select pixels of interest, an iterative sliding window approach is often used in CFAR. They analyse the relationship between the detection probability and the number of iterations, and prove that this strategy can reduce the miss rate and false detection rate effectively. On the basis of the aforementioned parameter estimation method and the iterative sliding window approach, an innovative and effective CFAR detector is proposed in this study and its superiority is demonstrated by experiments.

Ship detection in high-resolution synthetic aperture radar (SAR) images has attracted great attention. As a popular method, a constant false alarm rate (CFAR) detection algorithm is widely used. However, the detection performance of CFAR is easily affected by speckle noise. Moreover, the sliding window technique cannot effectively differentiate between clutter and target pixels and easily leads to a high computation load. In this paper, we propose a new superpixel-based non-window CFAR ship detection method for SAR images, which introduces superpixels to CFAR detection to resolve the aforementioned drawbacks. Firstly, our previously proposed fast density-based spatial clustering of applications with noise (DBSCAN) superpixel generation method is utilized to produce the superpixels for SAR images. With the assumption that SAR data obeys gamma distribution, the superpixel dissimilarity is defined. Then, superpixels can be accurately used to estimate the clutter parameters for the tested pixel, even in the multi-target situations, avoiding the drawbacks of the sliding window in the traditional CFAR. Moreover, a local superpixel contrast is proposed to optimize CFAR detection, which can eliminate numerous clutter false alarms, such as man-made urban areas and low bushes. Experimental results with real SAR images indicate that the proposed method can achieve ship detection with a higher speed and accuracy in comparison with other state-of-the-art methods.