An Adaptive Clutter Suppression Technique based on Environmental Perception

Zhou, Jianyang; Liu, Yi; Guan, Linhai; Zhang, Kai; Li, Zhaomin; Ai, Jun

doi:10.1109/cisce55963.2022.9851096

Cited by 3 publications

(3 citation statements)

References 7 publications

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“…where e(n) is the error data, d(n) is the reference data input, w N is the window coefficient, N is the number of filter length, and u(n) is the target data input. This error substitutes Equation (7) for Equation (8).…”

Section: Least Mean Squares (Lms)mentioning

confidence: 99%

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Clutter Cancellation Methods for Small Target Detection Using High-Resolution W-band Radar

Hwang,

Jang,

Choi

2023

Sensors

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Drones are currently being used for various applications. However, the detection of drones for defense or security purposes has become problematic because of the use of plastic materials and the small size of these drones. Any drone can be placed under surveillance to accurately determine its position by collecting high-resolution data using various detectors such as the radar system proposed in this paper. The W-band radar has a high carrier frequency, which makes it easy to design a wide bandwidth system, and the wideband FMCW signal is suitable for creating high resolution images from a distance. Unfortunately, the huge amounts of data gathered in this way also contain clutter (such as background data and noise) that is usually generated from unstable radar systems and complex environmental factors, and which frequently gives rise to distorted data. Accurate extraction of the position of the target from this big data requires the clutter to be suppressed and canceled, but conventional clutter cancellation methods are not suitable. Four clutter cancellation algorithms are assessed and compared: standard deviation, adaptive least mean squares (LMS), recursive least squares (RLS), and the proposed LMS. The proposed LMS has combined LMS with the standard deviation method. First, the big data pertaining to the target position is collected using the W-band radar system. Subsequently, the target position is calculated by applying these algorithms. The performance of the proposed algorithms is measured and compared to that of the other three algorithms by conducting outdoor experiments.

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Section: Least Mean Squares (Lms)mentioning

confidence: 99%

“…Many algorithms have been proposed to reduce clutter [4][5][6][7][8][9][10][11][12][13]. Most of these algorithms are based on adaptive least mean squares (LMS) or recursive least squares (RLS) processing since these methods are efficient and basic.…”

Section: Introductionmentioning

confidence: 99%

Clutter Cancellation Methods for Small Target Detection Using High-Resolution W-band Radar

Hwang,

Jang,

Choi

2023

Sensors

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“…The method has a better clutter suppression effect and is easy to implement. In 2022, a scholar proposed an adaptive clutter suppression technique based on environmental awareness [5], which obtains the cancellation rate in the full distance range of the minimum scale by purposefully processing the data before and after MTI cancellation. After several iterations, the cancellation parameters for a specific distance segment were set.…”

Section: Resource Allocation Technology Based On Radar Position Envir...mentioning

confidence: 99%

An Improved Segmented MTI Filter Based on a Clustering Algorithm

Liao

Liu

2023

J. Phys.: Conf. Ser.

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A K-Means clustering algorithm modified MTI (Moving Target Indication) filter is proposed for refining signal processing of the array environment in response to the current demand for clutter suppression by array adaptive technology. The method first uses the clutter distribution type and distance as parameters of the K-Means clustering algorithm to divide the echo’s into different clutter distance segments. Then an adaptive MTI filter is used to filter each clutter distance segment separately. The identification was carried out using measured ground radar echo data. The results show that the k-means clustering algorithm has better clutter distance segmentation capability. Compared with the conventional two-pulse MTI filter, the improved segmented MTI filter of the k-means algorithm has better clutter suppression in complex clutter environments.

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