scholarly journals Clutter and Range Ambiguity Suppression Using Diverse Pulse Train in Pulse Doppler System

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2326 ◽  
Author(s):  
Jiacen Xu ◽  
Lixiang Ren ◽  
Huayu Fan ◽  
Erke Mao ◽  
Quanhua Liu
Keyword(s):  
Target Detection ◽  
Pulse Train ◽  
Moving Target ◽  
Clutter Suppression ◽  
Moving Targets ◽  
Range Ambiguity ◽  
Doppler System ◽  
Pulse Doppler ◽  
The Cost ◽  
Conventional Systems

Pulse Doppler (PD) systems are widely used for moving target detection, especially in scenarios with clutter. Range ambiguity, which arises from fixed parameters in waveforms, is an inherent drawback in conventional systems. By using a diverse pulse train such as a train of coherent diverse phase coded pulses, these ambiguous peaks can be suppressed effectively but at the cost of sidelobe dispersions. In this work, a novel efficient PD process is proposed to suppress range ambiguity and detect moving targets under strong clutter. Poly-phase coded pulses are employed along with optimal receiving filters, by which the dispersed sidelobes are mitigated to a great extent. Moreover, a novel clutter suppression procedure is included in the PD process, by which strong clutter can be greatly suppressed. Well-designed receiving and inverse filters are employed. Simulation examples are presented to verify the theories. Compared with conventional methods, much better detection results are obtained for both near and remote targets, especially in scenarios with strong clutter.

Researching a Moving Target Detection Method Based on Magnetic Flux Induction Technology

Metals ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 1967
Author(s):  
Chaoqun Xu ◽  
Li Yang ◽  
Kui Huang ◽  
Yang Gao ◽  
Shaohua Zhang ◽  
...  
Keyword(s):  
Magnetic Flux ◽  
Magnetic Dipole ◽  
Target Detection ◽  
Electromotive Force ◽  
Detection Method ◽  
Field Tests ◽  
Moving Target ◽  
Moving Targets ◽  
Moving Target Detection ◽  
Modern Warfare

The ocean is a very important arena in modern warfare where all marine powers deploy their military forces. Due to the complex environment of the ocean, underwater equipment has become a very threatening means of surprise attack in modern warfare. Therefore, the timely and effective detection of underwater moving targets is the key to obtaining warfare advantages and has important strategic significance for national security. In this paper, magnetic flux induction technology was studied with regard to the difficulty of detecting underwater concealed moving targets. Firstly, the characteristics of a magnetic target were analyzed and an equivalent magnetic dipole model was established. Secondly, the structure of the rectangular induction coil was designed according to the model, and the relationship between the target’s magnetism and the detection signal was deduced. The variation curves of the magnetic flux and the electromotive force induced in the coil were calculated by using the numerical simulation method, and the effects of the different motion parameters of the magnetic dipole and the size parameters of the coil on the induced electromotive force were analyzed. Finally, combined with the wavelet threshold filter, a series of field tests were carried out using ships of different materials in shallow water in order to verify the moving target detection method based on magnetic flux induction technology. The results showed that this method has an obvious response to moving targets and can effectively capture target signals, which verifies the feasibility of the magnetic flux induction detection technology.

scholarly journals Phase-Based GLRT Detection of Moving Targets with Pixel Tracking in Low-Resolution SAR Image Sequences

2021 ◽  
Vol 13 (19) ◽  
pp. 3855
Author(s):  
Yulun Li ◽  
Chunsheng Li ◽  
Xiaodong Peng ◽  
Shuo Li ◽  
Hongcheng Zeng ◽  
...  
Keyword(s):  
Image Sequences ◽  
Moving Target ◽  
Ratio Test ◽  
Moving Targets ◽  
Low Resolution ◽  
Motion Signal ◽  
Resolution Imaging ◽  
Noise Statistics ◽  
The Cost ◽  
Area Monitoring

Spaceborne synthetic aperture radar (SAR) can provide ground area monitoring with large coverage. However, achieving a wide observation scope comes at the cost of resolution reduction owing to the trade-off between these parameters in conventional SAR. In low-resolution imaging, the moving target appears unresolved, weakly scattered, and slow moving in the image sequence, which can be generated by the subaperture technique. This article proposes a novel moving target detection method. First, interferometric phase statistics are combined with the generalized likelihood ratio test detector. A pixel tracking strategy is further exploited to determine whether a motion signal is present. These methods rely on the approximation of both clutter and noise statistics using Gaussian distributions in a low-resolution scenario. In addition, the motion signals are imaged with a subpixel offset. The proposed method is primarily validated using four real image sequences from TerraSAR-X data, which represent two types of homogeneous areas. The results reveal that moving targets can be detected in nearby areas using this strategy. The method is compared with the stack averaged coherence change detection and particle-filter-based tracking strategies.

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.

scholarly journals A Macro-Pulse Photon Counting Lidar for Long-Range High-Speed Moving Target Detection

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2204 ◽  
Author(s):  
Yang Yu ◽  
Bo Liu ◽  
Zhen Chen ◽  
ZhiKang Li
Keyword(s):  
Long Range ◽  
Target Detection ◽  
High Speed ◽  
Photon Counting ◽  
Pulse Method ◽  
Moving Target ◽  
Moving Targets ◽  
Moving Target Detection ◽  
Pulse Photon ◽  
Accumulation Method

A macro-pulse photon counting Lidar is described in this paper, which was designed to implement long-range and high-speed moving target detection. The ToF extraction method for the macro-pulse photon counting Lidar system is proposed. The performance of the macro pulse method and the traditional pulse accumulation method were compared in theory and simulation experiments. The results showed that the performance of the macro-pulse method was obviously better than that of the pulse accumulation method. At the same time, a laboratory verification platform for long range and high-speed moving targets was built. The experimental results were highly consistent with the theoretical and simulation results. This proved that the macro pulse photon counting Lidar is an effective method to measure long range high-speed moving targets.

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.

scholarly journals Moving Target Detection with Modified Logarithm Background Subtraction and Its Application to the GF-3 Spotlight Mode

2019 ◽  
Vol 11 (10) ◽  
pp. 1190
Author(s):  
Wenjie Shen ◽  
Wen Hong ◽  
Bing Han ◽  
Yanping Wang ◽  
Yun Lin
Keyword(s):  
False Alarm ◽  
Target Detection ◽  
Background Subtraction ◽  
Image Sequence ◽  
False Alarms ◽  
Moving Target ◽  
Constant False Alarm Rate ◽  
Moving Targets ◽  
Moving Target Detection ◽  
Original Algorithm

Spaceborne spotlight SAR mode has drawn attention due to its high-resolution capability, however, the studies about moving target detection with this mode are less. The paper proposes an image sequence-based method entitled modified logarithm background subtraction to detect ground moving targets with Gaofen-3 Single Look Complex (SLC) spotlight SAR images. The original logarithm background subtraction method is designed by our team for airborne SAR. It uses the subaperture image sequence to generate a background image, then detects moving targets by using image sequence to subtract background. When we apply the original algorithm to the spaceborne spotlight SAR data, a high false alarm problem occurs. To tackle the high false alarm problem due to the target’s low signal-to-noise-ratio (SNR) in spaceborne cases, several improvements are made. First, to preserve most of the moving target signatures, a low threshold CFAR (constant false alarm rate) detector is used to get the coarse detection. Second, because the moving target signatures have higher density than false detections in the coarse detection, a modified DBSCAN (density-based spatial-clustering-of-applications-with-noise) clustering method is then adopted to reduce false alarms. Third, the Kalman tracker is used to exclude the residual false detections, due to the real moving target signature having dynamic behavior. The proposed method is validated by real data, the shown results also prove the feasibility of the proposed method for both Gaofen-3 and other spaceborne systems.

Moving Target Detection and Tracking Based on Improved FCM Algorithm

2020 ◽  
Vol 14 (1) ◽  
pp. 63-74 ◽  
Author(s):  
Wang Ke Feng ◽  
Sheng Xiao Chun
Keyword(s):  
Target Detection ◽  
Clustering Algorithm ◽  
Rapid Development ◽  
Moving Target ◽  
Moving Targets ◽  
Moving Target Detection ◽  
Complex Situation ◽  
Fcm Algorithm ◽  
Detection And Tracking ◽  
Moving Vehicles

With the rapid development of computer intelligence technology, the majority of scholars have a great interest in the detection and tracking of moving targets in the field of video surveillance and have been involved in its research. Moving target detection and tracking has also been widely used in military, industrial control, and intelligent transportation. With the rapid progress of the social economy, the supervision of traffic has become more and more complicated. How to detect the vehicles on the road in real time, monitor the illegal vehicles, and control the illegal vehicles effectively has become a hot issue. In view of the complex situation of moving vehicles in various traffic videos, the authors propose an improved algorithm for effective detection and tracking of moving vehicles, namely improved FCM algorithm. It combines traditional FCM algorithm with genetic algorithm and Kalman filter algorithm to track and detect moving targets. Experiments show that this improved clustering algorithm has certain advantages over other clustering algorithms.

scholarly journals Intelligent Monitoring Method of Short-Distance Swimming Physical Function Fatigue Limit Mobile Calculation

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Jianxia Yin ◽  
Shimeng Huang ◽  
Lei Lei ◽  
Jing Yao
Keyword(s):  
Target Detection ◽  
Difference Method ◽  
Detection Method ◽  
Classification Problem ◽  
Moving Target ◽  
Moving Targets ◽  
Moving Target Detection ◽  
Monitoring Method ◽  
Intelligent Monitoring ◽  
Frame Difference

The detection and classification of moving targets have always been a key technology in intelligent video surveillance. Current detection and classification algorithms for moving targets still face many difficulties, mainly because of the complexity of the monitoring environment and the limitations of target characteristics. Therefore, this article conducts corresponding research on moving target detection and classification in intelligent video surveillance. According to the Gaussian Mixture Background Model and Frame Difference Method, this paper proposes a moving target detection method based on GMM (Gaussians Mixture Model) and Frame Difference Method. This method first proposes a new image combination algorithm that combines GMM and frame difference method, which solves the problems of noise and voids inside the target caused by the fusion of traditional GMM and frame difference method. The moving target detection method can effectively solve the problems of incomplete moving target detection, target internal gap, and noise, and it plays a vital role in the subsequent moving target classification process. Then, the method adds image inpainting technology to compensate the moving target in space and obtain a better target shape. The innovation of this paper is that in order to solve the multiobject classification problem, a binary tree decision support vector machine based on statistical learning is constructed as a classifier for moving object classification. Improve the learning efficiency of the classifier, solve the competitive classification problem of the traditional SVM, and increase the efficiency of the mobile computing intelligent monitoring method by more than 70%.

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