A new CFAR processor based on guard cells information

Moustafa, A.-E.A.; Ahmed, Fathy M.; Moustafa, K. H.; Halwagy, Y.

doi:10.1109/radar.2012.6212125

Cited by 9 publications

(5 citation statements)

References 8 publications

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“…The thresholding process is achieved through realizing a Cell-Averaging Constant False Alarm Rate (CA-CFAR) processor with total window size of 16 cells [21]. Figure (6) shows the relation between the probabilities of false alarm for Nyquist rate based approaches (ℓ 1 -norm and CAMP), Pseudo random based approach, and traditional FFT-based approach at different CFAR threshold gain.…”

Section: Simulation Resultsmentioning

confidence: 99%

Application of Compressive Sensing in LFMCW Radar

Salem¹,

Ahmed²,

Ibrahim³

et al. 2013

International Conference on Aerospace Sciences and Aviation Tec

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Compressive Sensing (CS) theory is a newly developed theory which combines the signal sampling and compression based on the sparsity characteristics of the signal. Applying CS theory in radar signal processing may lead to a reduction in sampling rate, complexity, power consumption, and cost. On the other hand, performance is a critical point to be considered. In the present paper, an important question of the worthy of applying CS in the signal processing of Linear Frequency Modulated Continuous Wave (LFMCW) radar is considered. Two approaches of CS are considered; Nyquist rate based approach, and pseudo random based approach. The detection performance of LFMCW radar signal processor using CS based approaches is compared to the traditional one which is based on Fast Fourier Transform (FFT) through Receiver Operating Characteristics (ROC) curves. Comparative analysis between CS approaches and the traditional one regarding the performance and complexity is presented.

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Section: Simulation Resultsmentioning

confidence: 99%

Application of Compressive Sensing in LFMCW Radar

Salem¹,

Ahmed²,

Ibrahim³

et al. 2013

International Conference on Aerospace Sciences and Aviation Tec

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“…This algorithm is designed to has good detection for neighboring targets. On the other hand, this tends to increase the probability of false alarm specially, in the presence of inhomogeneous clutter or high sidelobes [7]. To illustrate the enhancement in target detection, some scenarios have been assumed in the following section.…”

Section: Performance Analysis Of the Nested Compound Optimum Filtermentioning

confidence: 99%

Encoding, Decoding and Sidelobes Cancellation of Nested Compound Binary phase coded signal

Sobhy

Elbardawiny

Ahmed

et al. 2018

The International Conference on Electrical Engineering

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One of the methods for improving maximum radar range performance is increasing the average transmitted power. In this paper, this objective is achieved by nesting binary phase coded waveforms with different lengths. The decoding of these nested codes leads to appearance of high sidelobes which are canceled by nesting corresponding optimum filters. The hardware structure of the encoder and sidelobes cancellation processor of these nested codes are simply realized by cascading the corresponding original Barker code filters with changing the filter length in each cascaded level.

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“…However, the major challenge for radar systems is how to detect weak target signals from the strong ground clutter of airport runway background [10]. The constant false alarm rate (CFAR) methods including cell-average CFAR (CA-CFAR) [11] and cluttermap CFAR (CM-CFAR) [12] are widely utilized in radar automatic target recognition systems. CFAR can automatically adjust the threshold to detect targets when the intensity of external interference changes.…”

Section: Introductionmentioning

confidence: 99%

A FOD Detection Approach on Millimeter-Wave Radar Sensors Based on Optimal VMD and SVDD

Zhong

Gou

Shu

et al. 2021

Sensors

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Foreign object debris (FOD) on airport runways can cause serious accidents and huge economic losses. FOD detection systems based on millimeter-wave (MMW) radar sensors have the advantages of higher range resolution and lower power consumption. However, it is difficult for traditional FOD detection methods to detect and distinguish weak signals of targets from strong ground clutter. To solve this problem, this paper proposes a new FOD detection approach based on optimized variational mode decomposition (VMD) and support vector data description (SVDD). This approach utilizes SVDD as a classifier to distinguish FOD signals from clutter signals. More importantly, the VMD optimized by whale optimization algorithm (WOA) is used to improve the accuracy and stability of the classifier. The results from both the simulation and field case show the excellent FOD detection performance of the proposed VMD-SVDD method.

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A new CFAR processor based on guard cells information

Cited by 9 publications

References 8 publications

Application of Compressive Sensing in LFMCW Radar

Application of Compressive Sensing in LFMCW Radar

Encoding, Decoding and Sidelobes Cancellation of Nested Compound Binary phase coded signal

A FOD Detection Approach on Millimeter-Wave Radar Sensors Based on Optimal VMD and SVDD

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