Space-Range-Doppler Focus-Based Low-observable Moving Target Detection Using Frequency Diverse Array MIMO Radar

Chen, Xiaolong; Chen, Baoxin; Guan, Jian; Huang, Yong; He, You

doi:10.1109/access.2018.2863745

Cited by 57 publications

(21 citation statements)

References 46 publications

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“…T HE radar systems radiate electromagnetic signals into the space to detect the echoes that reflected from the targets [1]- [5]. The typical goal of radar systems is to detect, localize, and track targets based on the echoes [6]- [8].…”

Section: A Background and Motivationmentioning

confidence: 99%

Closed-Form Asymptotic Approximation of Target’s Range Information in Radar Detection Systems

Luo

et al. 2020

IEEE Access

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The echoes of the radar systems can provide useful information about the target, including range, velocity, shape, and angular direction. Extensive studies have utilized the information of the target to improve system performance, whereas the problem of a favorable closed-form asymptotic approximation of the target's range information (RI) is seldom investigated. In this paper, we address the problem of obtaining a closed-form asymptotic approximation of the target's RI in all SNR regions for radar detection systems. The RI is formulated as the mutual information (MI) between the random range and the received signals with complex additive white Gaussian noise (CAWGN). The basis of our scheme is to employ the a posteriori probability density function (PDF) of the range to extract RI from the echoes. We show the a posteriori PDF is a function of the autocorrelation function (ACF) of signal and the cross-correlation function (CCF) of signal-noise. By dividing the integration interval of the a posteriori PDF into the signal-noise interval and noise interval, a closed-form asymptotic approximation of RI is derived based on the probability of range distinguishability and the normalized a posteriori entropies of low and high signal-to-noise ratio (SNR). The results also reveal an interesting relationship between the RI and the uncertainty of the target. As special cases, the closed-form approximations of RI in low and high SNR are obtained. Moreover, for the problem of slightly looser in medium SNR approximation, a better approximation is derived based on a linear model approach. Numerical results are presented to validate the proposed theory and verify the effectiveness of the proposed approximation.

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Section: A Background and Motivationmentioning

confidence: 99%

Closed-Form Asymptotic Approximation of Target’s Range Information in Radar Detection Systems

Luo

et al. 2020

IEEE Access

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“…Using some novel radars, e.g. multiple‐input multiple‐output (MIMO) radar [7, 8], ubiquitous radar etc., the observation time can be increased greatly, which is helpful for the improvement of integration gain. For example, the phased array radar enables long‐time observation and integration of the target.…”

Section: Introductionmentioning

confidence: 99%

Sparse long‐time coherent integration‐based detection method for radar low‐observable manoeuvring target

Chen¹,

Guan²,

Chen

et al. 2020

IET Radar, Sonar & Navigation

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“…In fields such as robotics, remote sensing and autonomous vehicles driving, real-time object detection is the basis for target tracking. To improve the accuracy and speed of object detection, various algorithms have been proposed over the last few decades [1,2,3]. Conventional object detection relies on robust image feature descriptors, such as Binary Robust Independent Elementary Features (BRIEF) [4], Oriented FAST and Rotated BRIEF (ORB) [5] and Histogram of Oriented Gradient (HOG) [6].…”

Section: Introductionmentioning

confidence: 99%

An FPGA-Based Ultra-High-Speed Object Detection Algorithm with Multi-Frame Information Fusion

Long

et al. 2019

Sensors

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An ultra-high-speed algorithm based on Histogram of Oriented Gradient (HOG) and Support Vector Machine (SVM) for hardware implementation at 10,000 frames per second (FPS) under complex backgrounds is proposed for object detection. The algorithm is implemented on the field-programmable gate array (FPGA) in the high-speed-vision platform, in which 64 pixels are input per clock cycle. The high pixel parallelism of the vision platform limits its performance, as it is difficult to reduce the strides between detection windows below 16 pixels, thus introduce non-negligible deviation of object detection. In addition, limited by the transmission bandwidth, only one frame in every four frames can be transmitted to PC for post-processing, that is, 75% image information is wasted. To overcome the mentioned problem, a multi-frame information fusion model is proposed in this paper. Image data and synchronization signals are first regenerated according to image frame numbers. The maximum HOG feature value and corresponding coordinates of each frame are stored in the bottom of the image with that of adjacent frames’. The compensated ones will be obtained through information fusion with the confidence of continuous frames. Several experiments are conducted to demonstrate the performance of the proposed algorithm. As the evaluation result shows, the deviation is reduced with our proposed method compared with the existing one.

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Space-Range-Doppler Focus-Based Low-observable Moving Target Detection Using Frequency Diverse Array MIMO Radar

Cited by 57 publications

References 46 publications

Closed-Form Asymptotic Approximation of Target’s Range Information in Radar Detection Systems

Closed-Form Asymptotic Approximation of Target’s Range Information in Radar Detection Systems

Sparse long‐time coherent integration‐based detection method for radar low‐observable manoeuvring target

An FPGA-Based Ultra-High-Speed Object Detection Algorithm with Multi-Frame Information Fusion

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