Multi-Dimensional Automatic Detection of Scanning Radar Images of Marine Targets Based on Radar PPInet

Traditional radar target detection algorithms are mostly based on statistical theory. They have weak generalization capabilities for complex sea clutter environments and diverse target characteristics, and their detection performance would be significantly reduced. In this paper, the range-azimuth-frame information obtained by scanning radar is converted into plain position indicator (PPI) images, and a novel Radar-PPInet is proposed and used for marine target detection. The model includes CSPDarknet53, SPP, PANet, power non-maximum suppression (P-NMS), and multi-frame fusion section. The prediction frame coordinates, target category, and corresponding confidence are directly given through the feature extraction network. The network structure strengthens the receptive field and attention distribution structure, and further improves the efficiency of network training. P-NMS can effectively improve the problem of missed detection of multi-targets. Moreover, the false alarms caused by strong sea clutter are reduced by the multi-frame fusion, which is also a benefit for weak target detection. The verification using the X-band navigation radar PPI image dataset shows that compared with the traditional cell-average constant false alarm rate detector (CA-CFAR) and the two-stage Faster R-CNN algorithm, the proposed method significantly improved the detection probability by 15% and 10% under certain false alarm probability conditions, which is more suitable for various environment and target characteristics. Moreover, the computational burden is discussed showing that the Radar-PPInet detection model is significantly lower than the Faster R-CNN in terms of parameters and calculations.

Download Full-text

Realizing Target Detection in SAR Images Based on Multiscale Superpixel Fusion

Sensors ◽

10.3390/s21051643 ◽

2021 ◽

Vol 21 (5) ◽

pp. 1643

Author(s):

Ming Liu ◽

Shichao Chen ◽

Fugang Lu ◽

Mengdao Xing ◽

Jingbiao Wei

Keyword(s):

Synthetic Aperture Radar ◽

False Alarm ◽

False Alarm Rate ◽

Target Detection ◽

Synthetic Aperture ◽

False Alarms ◽

Sar Image ◽

Constant False Alarm Rate ◽

Sar Images ◽

Complex Scenes

For target detection in complex scenes of synthetic aperture radar (SAR) images, the false alarms in the land areas are hard to eliminate, especially for the ones near the coastline. Focusing on the problem, an algorithm based on the fusion of multiscale superpixel segmentations is proposed in this paper. Firstly, the SAR images are partitioned by using different scales of superpixel segmentation. For the superpixels in each scale, the land-sea segmentation is achieved by judging their statistical properties. Then, the land-sea segmentation results obtained in each scale are combined with the result of the constant false alarm rate (CFAR) detector to eliminate the false alarms located on the land areas of the SAR image. In the end, to enhance the robustness of the proposed algorithm, the detection results obtained in different scales are fused together to realize the final target detection. Experimental results on real SAR images have verified the effectiveness of the proposed algorithm.

Download Full-text

DETECTION OF SHIP TARGETS IN POLARIMETRIC SAR DATA USING 2D-PCA DATA FUSION

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xl-7-w3-1017-2015 ◽

2015 ◽

Vol XL-7/W3 ◽

pp. 1017-1024

Author(s):

C. Theoharatos ◽

A. Makedonas ◽

N. Fragoulis ◽

V. Tsagaris ◽

S. Costicoglou

Keyword(s):

Data Fusion ◽

Target Detection ◽

Single Channel ◽

False Alarms ◽

Detection Accuracy ◽

Constant False Alarm Rate ◽

Additional Advantage ◽

Detection Model ◽

Background Clutter ◽

Fusion Scheme

Data fusion has lately received a lot of attention as an effective technique for several target detection and classification applications in different remote sensing areas. In this work, a novel data fusion scheme for improving the detection accuracy of ship targets in polarimetric data is proposed, based on 2D principal components analysis (2D-PCA) technique. By constructing a fused image from different polarization channels, increased performance of ship target detection is achieved having higher true positive and lower false positive detection accuracy as compared to single channel detection performance. In addition, the use of 2D-PCA provides the ability to discriminate and classify objects and regions in the resulting image representation more effectively, with the additional advantage of being more computational efficient and requiring less time to determine the corresponding eigenvectors, compared to e.g. conventional PCA. Throughout our analysis, a constant false alarm rate (CFAR) detection model is applied to characterize the background clutter and discriminate ship targets based on the Weibull distribution and the calculation of local statistical moments for estimating the order statistics of the background clutter. Appropriate pre-processing and post-processing techniques are also introduced to the process chain, in order to boost ship discrimination and suppress false alarms caused by range focusing artifacts. Experimental results provided on a set of Envisat and RadarSat-2 images (dual and quad polarized respectively), demonstrate the advantage of the proposed data fusion scheme in terms of detection accuracy as opposed to single data ship detection and conventional PCA, in various sea conditions and resolutions. Further investigation of other data fusion techniques is currently in progress.

Download Full-text

Moving Target Detection with Modified Logarithm Background Subtraction and Its Application to the GF-3 Spotlight Mode

Remote Sensing ◽

10.3390/rs11101190 ◽

2019 ◽

Vol 11 (10) ◽

pp. 1190

Author(s):

Wenjie Shen ◽

Wen Hong ◽

Bing Han ◽

Yanping Wang ◽

Yun Lin

Keyword(s):

False Alarm ◽

Target Detection ◽

Background Subtraction ◽

Image Sequence ◽

False Alarms ◽

Moving Target ◽

Constant False Alarm Rate ◽

Moving Targets ◽

Moving Target Detection ◽

Original Algorithm

Spaceborne spotlight SAR mode has drawn attention due to its high-resolution capability, however, the studies about moving target detection with this mode are less. The paper proposes an image sequence-based method entitled modified logarithm background subtraction to detect ground moving targets with Gaofen-3 Single Look Complex (SLC) spotlight SAR images. The original logarithm background subtraction method is designed by our team for airborne SAR. It uses the subaperture image sequence to generate a background image, then detects moving targets by using image sequence to subtract background. When we apply the original algorithm to the spaceborne spotlight SAR data, a high false alarm problem occurs. To tackle the high false alarm problem due to the target’s low signal-to-noise-ratio (SNR) in spaceborne cases, several improvements are made. First, to preserve most of the moving target signatures, a low threshold CFAR (constant false alarm rate) detector is used to get the coarse detection. Second, because the moving target signatures have higher density than false detections in the coarse detection, a modified DBSCAN (density-based spatial-clustering-of-applications-with-noise) clustering method is then adopted to reduce false alarms. Third, the Kalman tracker is used to exclude the residual false detections, due to the real moving target signature having dynamic behavior. The proposed method is validated by real data, the shown results also prove the feasibility of the proposed method for both Gaofen-3 and other spaceborne systems.

Download Full-text

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

Symmetry ◽

10.3390/sym11121482 ◽

2019 ◽

Vol 11 (12) ◽

pp. 1482

Author(s):

Jiafei Zhao ◽

Rongkun Jiang ◽

Xuetian Wang ◽

Hongmin Gao

Keyword(s):

Machine Learning ◽

False Alarm ◽

Clustering Algorithm ◽

Spatial Clustering ◽

Optimal Parameter ◽

False Alarm Probability ◽

Masking Effect ◽

Constant False Alarm Rate ◽

Sea Clutter ◽

Cfar Detection

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.

Download Full-text

Implementation of a Modified Faster R-CNN for Target Detection Technology of Coastal Defense Radar

Remote Sensing ◽

10.3390/rs13091703 ◽

2021 ◽

Vol 13 (9) ◽

pp. 1703

Author(s):

He Yan ◽

Chao Chen ◽

Guodong Jin ◽

Jindong Zhang ◽

Xudong Wang ◽

...

Keyword(s):

False Alarm ◽

False Alarm Rate ◽

Target Detection ◽

Real Data ◽

Detection Performance ◽

Detection Accuracy ◽

Constant False Alarm Rate ◽

Data Set ◽

Detection Technology ◽

Coastal Defense

The traditional method of constant false-alarm rate detection is based on the assumption of an echo statistical model. The target recognition accuracy rate and the high false-alarm rate under the background of sea clutter and other interferences are very low. Therefore, computer vision technology is widely discussed to improve the detection performance. However, the majority of studies have focused on the synthetic aperture radar because of its high resolution. For the defense radar, the detection performance is not satisfactory because of its low resolution. To this end, we herein propose a novel target detection method for the coastal defense radar based on faster region-based convolutional neural network (Faster R-CNN). The main processing steps are as follows: (1) the Faster R-CNN is selected as the sea-surface target detector because of its high target detection accuracy; (2) a modified Faster R-CNN based on the characteristics of sparsity and small target size in the data set is employed; and (3) soft non-maximum suppression is exploited to eliminate the possible overlapped detection boxes. Furthermore, detailed comparative experiments based on a real data set of coastal defense radar are performed. The mean average precision of the proposed method is improved by 10.86% compared with that of the original Faster R-CNN.

Download Full-text

Determination of level and time thresholds for detectors with normalized indicator process

MORSKIE INTELLEKTUAL`NYE TEHNOLOGII ◽

10.37220/mit.2020.48.2.056 ◽

2020 ◽

Author(s):

С.Б. Егоров ◽

Р.И. Горбачев

Keyword(s):

False Alarm ◽

Average Rate ◽

Probability Model ◽

False Alarm Probability ◽

Statistical Characteristics ◽

False Alarms ◽

Time Interval ◽

Number Of False Alarms ◽

Time Threshold ◽

Selection Threshold

«Выбросовая» вероятностная модель работы обнаружителя в режиме ожидания сигнала, предложенная авторами в [1], использована для оценки влияния селекции выбросов по длительности на вероятность ложной тревоги. Флюктуационные выбросы помехового индикаторного процесса, превысившие пороги селекции по уровню и длительности, трактуются как редкие события на интервале ожидания сигнала, подчиняющиеся вероятностному закону Пуассона. При условии, что средний период следования ложных выбросов превышает интервал корреляции индикаторного процесса, получено соотношение между средним числом выбросов любой длительности и средним числом выбросов, превысивших пороговую длительность. На основании известных числовых и вероятностных характеристик выбросов нормального стационарного случайного процесса получен уравнения, связывающие относительные пороги селекции по уровню и длительности с вероятностью ложной тревоги на интервале ожидания сигнала. Предложена методика определения порога селекции по длительности для снижения порога селекции по уровню до заданной величины. «Emissional» probability model of the detector in stand-by mode proposed by the authors in [1], is intended for estimation of false alarm rate dependence from the value of time-selection threshold. Fluctuation emissions of the noise indicator process are interpreted as rare events correspond to Poisson distribution. Assuming that average rate of false alarms exceeds the correlation interval of indicator process, obtained equation between average number of false alarms of any duration and average number of false alarms exceed the time threshold. Based on known numerical and statistical characteristics of emissions of normal stationary random process obtained equations, relating time and level thresholds with false alarm probability on stand-by mode time interval. Also suggested a method of determining time threshold intended to reduce level threshold.

Download Full-text

Statistical Properties of Energy Detection for Spectrum Sensing by Using Estimated Noise Variance

Journal of Sensor and Actuator Networks ◽

10.3390/jsan8020028 ◽

2019 ◽

Vol 8 (2) ◽

pp. 28 ◽

Cited By ~ 1

Author(s):

Xiao-Li Hu ◽

Pin-Han Ho ◽

Limei Peng

Keyword(s):

False Alarm ◽

Spectrum Sensing ◽

False Alarm Probability ◽

Energy Detection ◽

Radio Spectrum ◽

Statistical Properties ◽

Constant False Alarm Rate ◽

Noise Variance ◽

Total Noise ◽

Noise Receiver

In energy detection for cognitive radio spectrum sensing, the noise variance is usually assumed given, by which a threshold is set to guarantee a desired constant false alarm rate (CFAR) or a constant detection rate (CDR). However, in practical situations, the exact information of noise variance is generally unavailable to a certain extent due to the fact that the total noise consists of time-varying thermal noise, receiver noise, and environmental noise, etc. Hence, setting the thresholds by using an estimated noise variance may result in different false alarm probabilities from the desired ones. In this paper, we analyze the basic statistical properties of the false alarm probability by using estimated noise variance, and propose a method to obtain more suitable CFAR thresholds for energy detection. Specifically, we first come up with explicit descriptions on the expectations of the resultant probability, and then analyze the upper bounds of their variance. Based on these theoretical preparations, a new method for precisely obtaining the CFAR thresholds is proposed in order to assure that the expected false alarm probability can be as close to the predetermined as possible. All analytical results derived in this paper are testified by corresponding numerical experiments.

Download Full-text

New matrix constant false alarm rate detectors for radar target detection

The Journal of Engineering ◽

10.1049/joe.2019.0454 ◽

2019 ◽

Vol 2019 (19) ◽

pp. 5597-5601 ◽

Cited By ~ 1

Author(s):

Wenjing Zhao ◽

Deyue Zou ◽

Wenlong Liu ◽

Minglu Jin

Keyword(s):

False Alarm ◽

False Alarm Rate ◽

Target Detection ◽

Radar Target ◽

Constant False Alarm Rate

Download Full-text

Adjacent Infrared Multitarget Detection Using Robust Background Estimation

Journal of Sensors ◽

10.1155/2016/7279081 ◽

2016 ◽

Vol 2016 ◽

pp. 1-10 ◽

Cited By ~ 2

Author(s):

Sungho Kim ◽

Kyung-Tae Kim

Keyword(s):

False Alarm ◽

False Alarm Rate ◽

Target Detection ◽

Detection Method ◽

Detection Performance ◽

Detection Methods ◽

Constant False Alarm Rate ◽

Cluttered Environments ◽

Background Estimation ◽

Cfar Detection

Small target detection is very important for infrared search and track (IRST) problems. Grouped targets are difficult to detect using the conventional constant false alarm rate (CFAR) detection method. In this study, a novel multitarget detection method was developed to identify adjacent or closely spaced small infrared targets. The neighboring targets decrease the signal-to-clutter ratio in hysteresis threshold-based constant false alarm rate (H-CFAR) detection, which leads to poor detection performance in cluttered environments. The proposed adjacent target rejection-based robust background estimation can reduce the effects of the neighboring targets and enhance the small multitarget detection performance in infrared images by increasing the signal-to-clutter ratio. The experimental results of the synthetic and real adjacent target sequences showed that the proposed method produces an upgraded detection rate with the same false alarm rate compared to the recent target detection methods (H-CFAR, Top-hat, and TDLMS).

Download Full-text

AN AUTOMATIC EVENT DETECTOR AT THE TONTO FOREST SEISMIC OBSERVATORY

Geophysics ◽

10.1190/1.1440453 ◽

1974 ◽

Vol 39 (5) ◽

pp. 633-643 ◽

Cited By ~ 65

Author(s):

R. R. Blandford

Keyword(s):

False Alarm ◽

False Alarm Rate ◽

Kuril Island ◽

Detection Threshold ◽

Maximum Effect ◽

False Alarms ◽

Constant False Alarm Rate ◽

Statistical Bias ◽

Short Period ◽

On Line

The on‐line operation of an automatic event detector has been evaluated at the Tonto Forest Observatory short‐period seismic array. For 31 seismometers and one fixed threshold, the 90 percent incremental detection threshold on the Kuril Island beam, centered at Δ=70 degrees, is [Formula: see text] with an experimentally determined false alarm rate of 0.17 per day. This compares favorably with the capabilities of a human operator. Storms in the Kurils significantly affect the distribution of amplitudes of the F-statistic detection trace, and we estimate that most of the false alarms observed at the operating threshold can be traced to the statistical bias introduced by this storm‐generated energy. If the threshold were adjusted to maintain a constant false alarm rate, the maximum effect on the threshold magnitude would be [Formula: see text].

Download Full-text