A modified matrix CFAR detector based on maximum eigenvalue for target detection in the sea clutter

Zhao, Wenjing; Jin, Minglu; Liu, Wenlong

doi:10.1109/radar.2018.8378679

Cited by 7 publications

(3 citation statements)

References 15 publications

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“…Formally, the problem of detecting radar moving target in the background of sea clutter plus noise can be represented by the following binary hypothesis model [23], [24]:…”

Section: Problem Formulationmentioning

confidence: 99%

Maximum Eigenvalue Matrix CFAR Detection Using Pre-Processing in Sea Clutter

Zhao

Jin

2019

IEEE Access

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For the pulse Doppler radar system with short pulse sequence, maximum eigenvalue-based matrix CFAR detector (MEMD) provides an effective solution to the problem of detecting moving targets in the background of K distribution sea clutter when the target Doppler frequency is within clutter spectrum. However, its performance is inferior to adaptive normalized matched filter method (ANMF) when the target Doppler frequency heavily deviates from the clutter spectrum. In essence, the reason for the better performance of ANMF method is the clutter is effectively suppressed by whitening and matched filter. Motivated by this, the fusion idea of eigenvalue detection and pre-processing via fast Fourier transform is considered taking advantage of the priori information of target steering vector. A pre-processing-based maximum eigenvalue matrix CFAR detection (P-MEMD) method is proposed to suppress the influence of clutter on detection performance. Finally, the numerical experimental results based on simulated data and real sea clutter data show that the proposed method achieves better detection performance and robustness than some conventional methods. INDEX TERMSMoving target detection, pre-processing, sea clutter.

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“…Formally, the problem of detecting radar moving target in the background of sea clutter plus noise can be represented by the following binary hypothesis model [23], [24]:…”

Section: Problem Formulationmentioning

confidence: 99%

Maximum Eigenvalue Matrix CFAR Detection Using Pre-Processing in Sea Clutter

Zhao

Jin

2019

IEEE Access

Self Cite

View full text Add to dashboard Cite

show abstract

“…It is supposed that radar transmits M pulses in a coherent processing interval, the received data is properly sampled and organised into

y

. In general, the detection problem can be formulated in terms of the following binary hypothesis test problem [9]:

({, \begin{matrix} 1em4pt \\ {normalH}_{1} : ({, \begin{matrix} 1em4pt \\ \begin{matrix} 1em4pt \\ y = s + c \\ {bold-italicy}_{l} = {bold-italicc}_{l} \end{matrix} & l = 1, 2, \dots, N \end{matrix}) \\ {normalH}_{0} : ({, \begin{matrix} 1em4pt \\ \begin{matrix} 1em4pt \\ y = c \\ {bold-italicy}_{l} = {bold-italicc}_{l} \end{matrix} & l = 1, 2, \dots, N \end{matrix}) \end{matrix}) .

Under the null hypothesis

{normalH}_{0}

, the received data y consists only of the clutter c . c l

{bold-italicc}_{l}

represents the M dimensional received data in reference cells.…”

Section: Problem Formulationmentioning

confidence: 99%

“…However, the detection performance of M‐CFAR detector based on Riemannian distance is not satisfactory in practical detection scenarios. [9] proposed an modified M‐CFAR detector based on the maximum eigenvalue to improve the detection performance. [10, 11] provided a new Kullback‐Leibler divergence‐based detection method to improve detection performance.…”

Section: Introductionmentioning

confidence: 99%