Square-Law Detection of Exponential Targets in Weibull-Distributed Ground Clutter

García, Fernando Darío Almeida; Mora, Henry Ramiro Carvajal; Fraidenraich, Gustavo; Filho, José Cândido Silveira Santos

doi:10.1109/lgrs.2020.3009304

Cited by 11 publications

(5 citation statements)

References 18 publications

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“…where P D is the probability of detection obtained via MATH-EMATICA's built-in numerical integration. 7 Observe that for 9 different parameter settings, (43) converges rapidly requiring between 23 and 83 terms to guarantee an accuracy of 10 −10 . Moreover, the computation time dropped dramatically, thereby providing reduction times above 88%.…”

Section: Numerical Results and Discussionmentioning

confidence: 99%

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New Findings on GLRT Radar Detection of Non-Fluctuating Targets via Phased Arrays

2021

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This paper addresses the standard generalized likelihood ratio test (GLRT) detection problem of weak signals in background noise. In so doing, we consider a non-fluctuating target embedded in complex white Gaussian noise (CWGN), in which the amplitude of the target echo and the noise power are assumed to be unknown. Important works have analyzed the performance for the referred scenario and proposed GLRT-based detectors. Such detectors are projected at an early stage (i.e., prior to the formation of a postbeamforming scalar waveform), thereby imposing high demands on hardware, processing, and data storage. From a hardware perspective, most radar systems fail to meet these strong requirements. In fact, due to hardware and computational constraints, most radars use a combination of analog and digital beamformers (sums) before any estimation or further pre-processing. The rationale behind this study is to derive a GLRT detector that meets the hardware and system requirements. In this work, we design and analyze a more practical and easy-to-implement GLRT detector, which is projected after the analog beamforming. The performance of the proposed detector is analyzed and the probabilities of detection (PD) and false alarm (PFA) are derived in closed form. An alternative fast convergent series for the PD is also derived. This series proves to be very efficient and computationally tractable, saving both computation time and computational load. Moreover, we show that in the low signal-to-noise ratio (SNR) regime, the post-beamforming GLRT detector performs better than both the classic pre-beamforming GLRT detector and the square-law detector. This finding suggests that if the signals are weak, instead of processing the signals separately, we first must reinforce the overall signal and then assembling the system's detection statistic. We also showed that the PFA of the post-beamforming GLRT detector is independent of the number of antennas. This property allows us to improve the PD (by increasing the number of antennas) while maintaining a fixed PFA. At last, the SNR losses are quantified, in which the superiority of the post-beamforming GLRT detector was evidenced as the number of antennas and samples increase.INDEX TERMS Generalized likelihood ratio test, non-fluctuating targets, complex white Gaussian noise, phased array radar, probability of detection.

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Section: Numerical Results and Discussionmentioning

confidence: 99%

“…A general implementation for the multivariate Fox's Hfunction is not yet available in mathematical packages such as MATHEMATICA, MATLAB, or MAPLE. Some works have been done to alleviate this problem [41]- [43]. Specifically in [41], the Fox's H-function was implemented from one up to four variables.…”

Section: B Fox's H-function-based Representationmentioning

confidence: 99%

New Findings on GLRT Radar Detection of Non-Fluctuating Targets via Phased Arrays

2021

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“…The second is to analyze and control the design and implementation process of the web page to determine whether the page is running correctly and complete the corresponding operations before starting to perform the corresponding functions. Finally, there should be a reasonable and effective user management mechanism for the foreground interface, including system login, data backup and other contents Distributed Processing System [11][12]. Figure 1 is the architecture diagram of net remoting.…”

Section: Principlementioning

confidence: 99%

Development of Distributed Remote Processing System Based on Net Remoting Technology

2022

DPS

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With the development of network technology, multimedia teaching has become an indispensable part of today's education, and remting is also a new type of remote processing server. The system is designed and implemented with java development framework and JS architecture. This topic mainly introduces the function modules such as management of simulation interactive single machine training center, database management and transaction logic engine based on net platform, and analyzes and explains them respectively. At the same time, the front-end page is designed and built in detail, including the scheme of web page layout and data access control and the application of related technologies. After that, this paper tests the operation and processing of the distributed remote processing system. The test results show that the IP address information is sent to the client through the serial port for interactive operation. At the same time, it can also receive the return command from the terminal device to support the normal 5-10S time control of the remote processing, and complete the analysis and test of the performance and function.

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“…It can be used to avoid computer overloading, which is caused by unknown and time-varying clutter background, and obtain high detection performance. Although the CFAR technique has been researched for several decades, the requirement from diverse engineering applications still drives it to continuously develop and progress [1][2][3][4][5]. The simplest one of the CFAR detectors is the cell-averaging (CA) scheme which estimates the noise level in the test cell by averaging the outputs of nearby resolution cells.…”

Section: Introductionmentioning

confidence: 99%

Modified rank sum nonparametric CFAR to combat clutter edge

Xiangwei

Meng

2023

EURASIP J. Adv. Signal Process.

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The classical rank sum (RS) nonparametric constant false alarm rate (CFAR) detector plays an important role in the theoretical study and practical application of radar target detection. In order to improve the ability of the classical RS nonparametric detector to control the false alarm rate at clutter edges, a modified rank sum (MRS) nonparametric CFAR based on the mean ratio of the samples in the leading and lagging windows is proposed. The analytical expressions of the detection probability and false alarm rate of the MRS nonparametric CFAR in homogeneous background and at clutter edges are derived, and a comparison to the performance of the classical RS nonparametric detector along with some conventional parametric CFAR schemes in homogeneous background, multiple targets situation and clutter edges is made. The numerical results show that the detection performance of the MRS nonparametric CFAR in homogeneous background and in a moderate number of interfering targets situation is close to that of the classical RS nonparametric detector, and its ability to control the rise of the false alarm rate at clutter edges is evidently improved.

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Square-Law Detection of Exponential Targets in Weibull-Distributed Ground Clutter

Cited by 11 publications

References 18 publications

New Findings on GLRT Radar Detection of Non-Fluctuating Targets via Phased Arrays

New Findings on GLRT Radar Detection of Non-Fluctuating Targets via Phased Arrays

Development of Distributed Remote Processing System Based on Net Remoting Technology

Modified rank sum nonparametric CFAR to combat clutter edge

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