SIMM Method Based on Acceleration Extraction for Nonlinear Maneuvering Target Tracking

Son, Hyun Seung; Park, Jin Bae; Joo, Young Hoon

doi:10.5370/jeet.2012.7.2.255

Cited by 8 publications

(5 citation statements)

References 22 publications

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“…Step 1 (Initialisation): Substituting the predicted point 1 d (k|k − 1) into the measurement z d (k|k) yields the estimated positional errorê d (k|k − 1) from the aforementioned (1), (2), (3), (13) and (14). The set E(k|k − 1) is used as the input data of the segmentalised FCM as shown in the Fig.…”

Section: Calculating the Maximum Noise Levelmentioning

confidence: 99%

“…Tracking problems including target dynamics, manoeuvring target tracking and measurement origin uncertainty are components not only in military applications, but also closely linked to other areas of human life [3–6]. In order to solve these computational problems, a variety of techniques have been studied and developed in the field of state estimation over a period of decades [7–13].…”

Section: Introductionmentioning

confidence: 99%

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Fuzzy c‐means clustering‐based smart tracking model for three‐dimensional manoeuvring target including unknown acceleration input

Son¹,

Park²,

Joo³

2013

IET Radar, Sonar & Navigation

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This study proposes a smart tracking model with multiple structures. To achieve optimal performance, an ideal tracking system should maintain contact with the target and constantly update its tracking data. One of the major problems to consider arises from the target motion uncertainty. Although the variance of the overall noise is time-varying and may reduce tracking efficiency, it is difficult to adaptively approximate this uncertainty. To solve these computational problems, a variety of techniques have been studied, and the adaptive interacting multiple model has shown the best result. However, predefined sub-models with different conditions for multiple models are required. Moreover, the acceleration intervals for different acceleration levels should be determined in advance. To alleviate these problems, the authors propose a multiple-structured smart tracking algorithm which properly separates the acceleration from the overall noise by fuzzy c-means clustering. The filter in the algorithm recognises the manoeuvring target as if it does not include the acceleration input, and precisely tracks the target with multiple structures. This study focuses on the acceleration causing the uncertainty and three-dimensional (3D) motion of the real target motion in the tracking problem. Finally, a 3D cruise missile example is provided to show the effectiveness of the proposed algorithm.

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Section: Calculating the Maximum Noise Levelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fuzzy c‐means clustering‐based smart tracking model for three‐dimensional manoeuvring target including unknown acceleration input

Son¹,

Park²,

Joo³

2013

IET Radar, Sonar & Navigation

View full text Add to dashboard Cite

show abstract

“…Third, the measurement should be processed adaptively when unknown external inputs are included in the manoeuvres of the target [18–20]. In particular, the acceleration inputs should be separated from the noise term and should be offset appropriately to improve the tracking performance [21–23]. Fourth, the dispersion of the measurement residuals should be analysed to enhance the tracking performance.…”

Section: Introductionmentioning

confidence: 99%

Smart tracking algorithm for multi‐static sonar based on expectation maximisation

Son

2020

IET Radar, Sonar & Navigation

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“…A variety of techniques have been studied and developed in the field of state estimation over many decades to resolve this ambiguity in the error component related to the complex manoeuvring [4–10]. The following three methods are used from a modelling perspective: firstly, the discrete‐time model is commonly used since the evolution of the digital computer.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy c ‐means‐based intelligent tracking algorithm for an underwater manoeuvring target

Son

Park

Joo

2014

IET Radar, Sonar & Navigation

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The effort to detect a target conflicts with the covertness in anti-submarine warfare. The underwater detection rate is extremely low and sound waves are the only detection method unlike the airborne operational environment. The data of an underwater target based on noise levels are more difficult to interpret when the acceleration inputs are included. Hence, the authors propose a complementary algorithm for the tracking problem in an active sonar system. The bearing-range data of the target yield the measurement residuals, and the residuals are used as inputs to the clustering method. The clustering method estimates the acceleration and the noise level. The estimates aid in positional compensation and enhance the tracking performance. The fuzzy c-means clustering is utilised for the clustering and maintains the filter sustainable. Finally, some examples are provided to show the effectiveness of the proposed method.

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SIMM Method Based on Acceleration Extraction for Nonlinear Maneuvering Target Tracking

Cited by 8 publications

References 22 publications

Fuzzy c‐means clustering‐based smart tracking model for three‐dimensional manoeuvring target including unknown acceleration input

Fuzzy c‐means clustering‐based smart tracking model for three‐dimensional manoeuvring target including unknown acceleration input

Smart tracking algorithm for multi‐static sonar based on expectation maximisation

Fuzzy c ‐means‐based intelligent tracking algorithm for an underwater manoeuvring target

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