Performance analysis of mean level constant false alarm rate detectors with binary integration in Weibull background

Baadeche, Mohamed; Soltani, Faouzı

doi:10.1049/iet-rsn.2014.0053

Cited by 13 publications

(6 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Note that if MN 1 (SISO radars), (24) leads to the result obtained in [28]. antennas in homogenous and non-homogeneous Weibull background.…”

Section: Analysis Of the So-cfar Detectormentioning

confidence: 68%

“…In the hypothesis H1, we should derive the PDF of Swerling I target plus Weibull clutter but this cannot be obtained in closed form. Therefore, we resort to the approximation proposed in [28,37] where the PDF of the sample in the CUT is given by…”

Section: Analysis Of the So-cfar Detectormentioning

confidence: 99%

“…Among distributions used to model radar clutter returns, the Weibull distribution is found to be a good fit to the experimental data arising from high-resolution and/or low grazing angle radar emission [27]. The performance of CFAR detectors in Weibull distributed clutter was analyzed in several works; mean-level CFAR detectors in [28], ordered CFAR detectors in [29][30][31]. Recently, Zhang et al [32] proposed and analyzed a new CFAR detector based on skewness considering a Weibull distributed clutter.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Performance comparison of mean-level CFAR detectors in homogeneous and non-homogeneous Weibull clutter for MIMO radars

Baadeche

Soltani

Gini

2019

SIViP

View full text Add to dashboard Cite

In this paper, we analyze and compare the performance of the CA-CFAR, GO-CFAR and the SO-CFAR detectors in homogeneous and non-homogeneous Weibull background with known shape parameter for Multi-Input Multi-Output radars with widely separated antennas. The non-homogeneity is represented by the presence of interfering targets and a clutter edge in the reference window. We derive closed-form expressions of the probability of false alarm of the three detectors in homogeneous environment. Detector performance in non-homogeneous environment is investigated by means of Monte Carlo simulations. The numerical results show that the best performance is obtained by the SO-CFAR in the case of a high number of interferences, whereas the GO-CFAR has the best false-alarm regulation when the number of cells contaminated by clutter exceeds half the number of reference cells. Keywords MIMO radar • Weibull clutter • CFAR detectors • Non-homogeneous clutter 1 IntroductionMulti-Input Multi-Output (MIMO) is a technique used in communications to increase data throughput and link range without additional bandwidth or transmit power [1,2]. Later on this concept has been also adopted for radar applications, where multiple antennas are employed both at transmission and at reception. Unlike multistatic radar system where the same waveform is transmitted, orthogonal waveforms are used for the MIMO radar system to exploit spatial and waveform diversity gain simultaneously. This concept has the ability to improve parameter estimation and beamforming performance by having transmit and receive antennas close

show abstract

“…Note that if MN 1 (SISO radars), (24) leads to the result obtained in [28]. antennas in homogenous and non-homogeneous Weibull background.…”

Section: Analysis Of the So-cfar Detectormentioning

confidence: 68%

Section: Analysis Of the So-cfar Detectormentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

Performance comparison of mean-level CFAR detectors in homogeneous and non-homogeneous Weibull clutter for MIMO radars

Baadeche

Soltani

Gini

2019

SIViP

View full text Add to dashboard Cite

show abstract

“…Table 8 is the processing flow [102]. After the threshold comparison is completed for a single pixel, the sliding reference window detects other pixels in a certain direction [103]. As shown in figure 15, when traversing all pixels, the enhanced seafloor pipeline image will be output [104].…”

Section: Methodmentioning

confidence: 99%

Sonar image intelligent processing in seabed pipeline detection: review and application

Shi,

Cao,

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

Subsea pipelines rely primarily on imaging sonar for detection and identification. We analyze the imaging principles of side scan sonar, multi-beam sonar, synthetic aperture sonar, seafloor penetrating sonar and forward-looking sonar. We discuss their effectiveness in detecting seabed pipelines, as well as their limitations in image recognition capabilities. As intelligent algorithms have become increasingly important in the field of image processing, we review the sonar image intelligent detection and recognition algorithms in the past six years and summarize the internal principles and application effects of classic algorithms such as SIFT (Scale-Invariant Feature Transform), KMA (K-means algorithm), and CFAR (Constant False-Alarm Rate) that currently show good application prospects. Simultaneously, we review the particular strengths exhibited by these algorithms, such as contour feature extraction, image segmentation and clustering, target recognition under background noise, etc. The research on intelligent processing of sonar images opens up a new way to solve the difficult problem of the seabed targets detection and recognition.

show abstract

“…The structure of the CA-CFAR detector is shown in Figure 2. The relationship between Pfa−CA, and the normalization factor T in the CA-CFAR method is [32,33]…”

Section: Ca-cfarmentioning

confidence: 99%

Joint Ship Detection Based on Time-Frequency Domain and CFAR Methods with HF Radar

et al. 2021

View full text Add to dashboard Cite

Compact high-frequency surface wave radar (HFSWR) plays a critical role in ship surveillance. Due to the wide antenna beam-width and low spatial gain, traditional constant false alarm rate (CFAR) detectors often induce a low detection probability. To solve this problem, a joint detection algorithm based on time-frequency (TF) analysis and the CFAR method is proposed in this paper. After the TF ridge extraction, CFAR detection is performed to test each sample of the ridges, and a binary integration is run to determine whether the entire TF ridge is of a ship. To verify the effectiveness of the proposed algorithm, experimental data collected by the Ocean State Monitoring and Analyzing Radar, type SD (OSMAR-SD) were used, with the ship records from an automatic identification system (AIS) used as ground truth data. The processing results showed that the joint TF-CFAR method outperformed CFAR in detecting non-stationary and weak signals and those within the first-order sea clutters, whereas CFAR outperformed TF-CFAR in identifying multiple signals with similar frequencies. Notably, the intersection of the matched detection sets by TF-CFAR and CFAR alone was not immense, which takes up approximately 68% of the matched number by CFAR and 25% of that by TF-CFAR; however, the number in the union detection sets was much (>30%) greater than the result of either method. Therefore, joint detection with TF-CFAR and CFAR can further increase the detection probability and greatly improve detection performance under complicated situations, such as non-stationarity, low signal-to-noise ratio (SNR), and within the first-order sea clutters.

show abstract

Performance analysis of mean level constant false alarm rate detectors with binary integration in Weibull background

Cited by 13 publications

References 21 publications

Performance comparison of mean-level CFAR detectors in homogeneous and non-homogeneous Weibull clutter for MIMO radars

Performance comparison of mean-level CFAR detectors in homogeneous and non-homogeneous Weibull clutter for MIMO radars

Sonar image intelligent processing in seabed pipeline detection: review and application

Joint Ship Detection Based on Time-Frequency Domain and CFAR Methods with HF Radar

Contact Info

Product

Resources

About