Track-to-Track Association Based on Maximum Likelihood Estimation for T/R-R Composite Compact HFSWR

Sun, Weifeng; Li, Xiaotong; Pang, Zhenzhen; Ji, Yonggang; Dai, Yongshou; Huang, Weimin

doi:10.1109/tgrs.2023.3253784

Cited by 19 publications

(9 citation statements)

References 32 publications

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“…Since the greater the correlation degree between tracks means the greater the probability of tracks being the same target, the objective function in (23) can be converted into the following two-dimensional allocation problem:…”

Section: Asynchronous Ttta Judgmentmentioning

confidence: 99%

“…By the work in [22], an algorithm for processing spatiotemporal bias and state estimation of asynchronous multi-sensor systems is proposed to obtain the enhanced state vector and establish the enhanced state model. Reference [23] uses the obtained radar track and corresponding automatic identification system (AIS) track data to calculate the measurement errors of distance, azimuth, and Doppler velocity, and a Gaussian distribution model is derived through probability distribution fitting. The work in [24], according to the statistical characteristics of Gaussian random vectors, led to the design for an anti-bias TTTA technique for aircraft platforms.…”

Section: Introductionmentioning

confidence: 99%

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Asynchronous Anti-Bias Track-to-Track Association Algorithm Based on Nearest Neighbor Interval Average Distance for Multi-Sensor Tracking Systems

Chen

Zhang

et al. 2023

Electronics

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Due to sensor characteristics, geographical environment, electromagnetic interference, electromagnetic silence, information countermeasures, and other reasons, there may be significant system errors in sensors in multi-sensor tracking systems, resulting in poor track-to-track association (TTTA) effect of the system. In order to solve the problem of TTTA under large system errors, this paper proposes an asynchronous anti-bias TTTA algorithm that utilizes the average distance between the nearest neighbor intervals between tracks. This algorithm proposes a systematic error interval processing method to track coordinates, and then defines the nearest neighbor interval average distance between interval coordinate datasets and interval coordinate points, and then uses grey theory to calculate the correlation degree between tracks. Finally, the Jonker–Volgenant algorithm is combined to use the canonical allocation method for TTTA judgment. The algorithm requires less prior information and does not require error registration. The simulation results show that the algorithm can ensure a high average correct association rate (over 98%) of asynchronous unequal rate tracks under large system errors, and achieve stable association, with good association and anti-bias performance. Compared with other algorithms, the algorithm maintains good performance for different target numbers and processing cycles, and has good superiority and robustness.

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Section: Asynchronous Ttta Judgmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Asynchronous Anti-Bias Track-to-Track Association Algorithm Based on Nearest Neighbor Interval Average Distance for Multi-Sensor Tracking Systems

Chen

Zhang

et al. 2023

Electronics

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“…High-frequency surface wave radar (HFSWR) is widely utilized in maritime applications for over-the-horizon target detection [10][11][12] and ocean remote sensing [13][14][15][16] due to its unique capabilities. Furthermore, there have been significant advances in HF-SWR ionospheric-sounding research in recent years [17,18].…”

Section: Introductionmentioning

confidence: 99%

Joint Analysis and Morphological Characterization of HFSWR Echo Properties during Severe Typhoon Muifa

Wang,

Lyu,

et al. 2024

Remote Sensing

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Investigating the dynamic evolution process of the ocean and ionosphere in sudden sea conditions poses a challenging problem. To address this objective, this study utilizes actual data from high-frequency surface wave radar (HFSWR) to analyze, validate, summarize, and characterize the echo properties of the ocean and ionosphere during the severe Typhoon Muifa. By employing the short-time Fourier transform (STFT) method, the HFSWR ocean and ionosphere echoes stimulated by typhoon-induced gravity waves are observed, and the joint gravity wave features of the ocean and ionosphere echoes at different time scales are extracted. Additionally, the phase-space reconstruction method is employed to characterize the dynamical evolution of the joint gravity wave features in higher-dimensional space. Furthermore, the chaotic dynamics behavior of the joint gravity wave features is analyzed using the largest Lyapunov exponents. By combining the gravity wave features with chaotic dynamics, this study introduces a method to characterize the joint gravity wave features. The extraction of joint gravity wave features in HFSWR echoes stimulated by typhoons, along with the construction of a chaotic characterization scheme for the gravity wave features, provides an innovative approach and a solid technical foundation for studying the ocean and ionosphere using HFSWR under sudden sea conditions.

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“…Because of its beyond-the line-of-sight monitoring and all-weather-continuous remote sensing [1,2], high-frequency surface wave radar (HFSWR) has gained considerable attention. HFSWR has been widely used for two dominant areas: real-time maritime surveillance [3] and sea surface target detection [4,5].…”

Section: Introductionmentioning

confidence: 99%

Ship Formation Identification with Spatial Features and Deep Learning for HFSWR

Wang,

Liu,

et al. 2024

Remote Sensing

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Ship detection has been an area of focus for high-frequency surface wave radar (HFSWR). The detection and identification of ship formation have proven significant in early warning, while studies on the formation identification are limited due to the complex background and low resolution of HFSWR. In this paper, we first establish a spatial distribution model of ship formation in HFSWR. Then, we propose a cascade identification algorithm of ship formation in the clutter edge. The proposed algorithm includes a preprocessing stage and a two-stage formation identification stage. The Faster R-CNN is introduced in the preprocessing stage to locate the clutter regions. In the first stage, we propose an extremum detector based on connected regions to extract suspicious regions. The suspicious regions contain ship formations, single-ship targets, and false targets. In the second stage, we design a network connected by a convolutional neural network (CNN) and an extreme learning machine (ELM) to identify two densely distributed ship formations from inhomogeneous clutter and single-ship targets. The experimental results based on the factual HFSWR background demonstrate that the proposed cascade identification algorithm is superior to the extremum detector combined with the classical CNN algorithm for ship formation identification. Meanwhile, the proposed algorithm performs well in weak formation and deformed formation identification.

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Track-to-Track Association Based on Maximum Likelihood Estimation for T/R-R Composite Compact HFSWR

Cited by 19 publications

References 32 publications

Asynchronous Anti-Bias Track-to-Track Association Algorithm Based on Nearest Neighbor Interval Average Distance for Multi-Sensor Tracking Systems

Asynchronous Anti-Bias Track-to-Track Association Algorithm Based on Nearest Neighbor Interval Average Distance for Multi-Sensor Tracking Systems

Joint Analysis and Morphological Characterization of HFSWR Echo Properties during Severe Typhoon Muifa

Ship Formation Identification with Spatial Features and Deep Learning for HFSWR

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