A Particle Fuzzy Decisive Framework for Moving Target Detection in the Multichannel SAR Framework

2020 ◽  
Vol 19 (04) ◽  
pp. 2050032
Author(s):  
Eppili Jaya ◽  
B. T. Krishna
Keyword(s):  
Fourier Transform ◽  
Target Detection ◽  
Fuzzy System ◽  
Target Location ◽  
Fractional Fourier Transform ◽  
Moving Target ◽  
Detection Time ◽  
Moving Target Detection ◽  
Genetic Fuzzy System ◽  
Linguistic Rules

Target detection is one of the important subfields in the research of Synthetic Aperture Radar (SAR). It faces several challenges, due to the stationary objects, leading to the presence of scatter signal. Many researchers have succeeded on target detection, and this work introduces an approach for moving target detection in SAR. The newly developed scheme named Adaptive Particle Fuzzy System for Moving Target Detection (APFS-MTD) as the scheme utilizes the particle swarm optimization (PSO), adaptive, and fuzzy linguistic rules in APFS for identifying the target location. Initially, the received signals from the SAR are fed through the Generalized Radon-Fourier Transform (GRFT), Fractional Fourier Transform (FrFT), and matched filter to calculate the correlation using Ambiguity Function (AF). Then, the location of target is identified in the search space and is forwarded to the proposed APFS. The proposed APFS is the modification of standard Adaptive genetic fuzzy system using PSO. The performance of the MTD based on APFS is evaluated based on detection time, missed target rate, and Mean Square Error (MSE). The developed method achieves the minimal detection time of 4.13[Formula: see text]s, minimal MSE of 677.19, and the minimal moving target rate of 0.145, respectively.

scholarly journals Moving Target Detection in Multichannel SAR Framework using Adaptive Neuro Fuzzy Decisive Technique

2019 ◽  
Vol 8 (2) ◽  
pp. 4517-4523 ◽  
Keyword(s):  
Target Detection ◽  
Target Location ◽  
Fractional Fourier Transform ◽  
Research Area ◽  
Moving Target ◽  
Moving Targets ◽  
Moving Target Detection ◽  
Neuro Fuzzy ◽  
Linguistic Rules ◽  
Background Clutter

Precise and efficacious detection of moving targets is a prominent task in on-going synthetic aperture radar (SAR) technique. The perception of moving object allows quite significant data about the situation under observation for both surveillance and intelligence activities. The task of accurately locating moving targets against strong background clutter in minimum of time is of utmost interest in the current research area. Fractional Fourier Transform (FrFT) concentrates the energy of the required chirp signal so that it can be well separated from the chirp like noise. The proposed SAR Moving Target Detection (MTD) process is based on the combination of FrFT with the adaptive-neuro fuzzy decisive technique. The correlation among the received signal and the FrFT of the received signal are computed which maximizes the required signal energy and applied to the adaptive-neuro fuzzy decisive module that detects the target location adaptively using the fuzzy linguistic rules. The simulation is performed by changing the number of targets, different Pulse repetition intervals, antenna turn velocity, iterations and the analysis is carried out based on the metrics, like detection time, missed target rate, and Mean Square Error (MSE), proving that the proposed Adaptive-Neuro Fuzzy-based MTD process detected the object in 5.0237 secs with a minimum missed target rate of 0.1210 and MSE of 23377.48.

Deep Convolutional Neural Network driven Neuro-Fuzzy System for Moving Target Detection Using the Radar Signals

2021 ◽  
Author(s):  
M. Bharat Kumar ◽  
P. Rajesh Kumar
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Target Detection ◽  
Fuzzy System ◽  
Deep Convolutional Neural Network ◽  
Moving Target ◽  
Moving Targets ◽  
Moving Target Detection ◽  
Neuro Fuzzy ◽  
Deep Cnn

In radar signal processing, detecting the moving targets in a cluttered background remains a challenging task due to the moving out and entry of targets, which is highly unpredictable. In addition, detection of targets and estimation of the parameters have become a major constraint due to the lack of required information. However, the appropriate location of the targets cannot be detected using the existing techniques. To overcome such issues, this paper presents a developed Deep Convolutional Neural Network-enabled Neuro-Fuzzy System (Deep CNN-enabled Neuro-Fuzzy system) for detecting the moving targets using the radar signals. Initially, the received signal is presented to the Short-Time Fourier Transform (STFT), matched filter, radar signatures-enabled Deep Recurrent Neural Network (Deep RNN), and introduced deep CNN to locate the targets. The target location output results are integrated using the newly introduced neuro-fuzzy system to detect the moving targets effectively. The proposed deep CNN-based neuro-fuzzy system obtained effective moving target detection results by varying the number of targets, iterations, and the pulse repetition level for the metrics, like detection time, missed target rate, and MSE with the minimal values of 1.221s, 0.022, and 1,952.15.

Fuzzy-based MTD

2020 ◽  
Vol 54 (1) ◽  
pp. 66-84
Author(s):  
Eppili Jaya ◽  
B.T. Krishna
Keyword(s):  
Design Methodology ◽  
Moving Objects ◽  
Target Location ◽  
Fractional Fourier Transform ◽  
Search Space ◽  
Moving Target ◽  
Content Type ◽  
Linguistic Rules ◽  
Peak Energy ◽  
Fuzzy Linguistic

Purpose Synthetic aperture radar exploits the receiving signals in the antenna for detecting the moving targets and estimates the motion parameters of the moving objects. The limitation of the existing methods is regarding the poor power density such that those received signals are essentially to be transformed to the background ratio. To overcome this issue, fractional Fourier transform (FrFT) is employed in the moving target detection (MTD) process. The paper aims to discuss this issue. Design/methodology/approach The proposed MTD method uses the fuzzy decisive approach for detecting the moving target in the search space. The received signal and the FrFT of the received signal are subjected to the calculation of correlation using the ambiguity function. Based on the correlation, the location of the target is identified in the search space and is fed to the fuzzy decisive module, which detects the target location using the fuzzy linguistic rules. Findings The simulation is performed, and the analysis is carried out based on the metrics, like detection time, missed target rate, and MSE. From the analysis, it can be shown that the proposed Fuzzy-based MTD process detected the object in 5.0237 secs with a minimum missed target rate of 0.1210 and MSE of 23377.48. Originality/value The proposed Fuzzy-MTD is the application of the fuzzy rules for locating the moving target in search space based on the peak energy of the original received signal and FrFT of the original received signal.

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