Continuous Tracking of Targets for Stereoscopic HFSWR Based on IMM Filtering Combined with ELM

Zhang, Ling; Mao, DongWei; Niu, Jiong; Wu, Qi; Ji, Yonggang

doi:10.3390/rs12020272

Cited by 16 publications

(9 citation statements)

References 24 publications

(31 reference statements)

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“…It simply used the classic state estimation method to evaluate the difference between the actual complex system’s reference trajectory and the estimation result. Similar methods include using learning algorithms to improve the extended Kalman filter [ 111 ] and the multimodel extended Kalman filter [ 112 ]. In principle, this type of method does not modify the model but uses the difference between the reference state and the classical estimation result to describe the model’s matching degree.…”

Section: State Estimation Based On Hybrid-driven Methodsmentioning

confidence: 99%

The New Trend of State Estimation: From Model-Driven to Hybrid-Driven Methods

Jin

Jeremiah

et al. 2021

Sensors

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State estimation is widely used in various automated systems, including IoT systems, unmanned systems, robots, etc. In traditional state estimation, measurement data are instantaneous and processed in real time. With modern systems’ development, sensors can obtain more and more signals and store them. Therefore, how to use these measurement big data to improve the performance of state estimation has become a hot research issue in this field. This paper reviews the development of state estimation and future development trends. First, we review the model-based state estimation methods, including the Kalman filter, such as the extended Kalman filter (EKF), unscented Kalman filter (UKF), cubature Kalman filter (CKF), etc. Particle filters and Gaussian mixture filters that can handle mixed Gaussian noise are discussed, too. These methods have high requirements for models, while it is not easy to obtain accurate system models in practice. The emergence of robust filters, the interacting multiple model (IMM), and adaptive filters are also mentioned here. Secondly, the current research status of data-driven state estimation methods is introduced based on network learning. Finally, the main research results for hybrid filters obtained in recent years are summarized and discussed, which combine model-based methods and data-driven methods. This paper is based on state estimation research results and provides a more detailed overview of model-driven, data-driven, and hybrid-driven approaches. The main algorithm of each method is provided so that beginners can have a clearer understanding. Additionally, it discusses the future development trends for researchers in state estimation.

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Section: State Estimation Based On Hybrid-driven Methodsmentioning

confidence: 99%

The New Trend of State Estimation: From Model-Driven to Hybrid-Driven Methods

Jin

Jeremiah

et al. 2021

Sensors

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show abstract

“…Lidar is typically employed in automobiles and offers good comprehensive performance, but it is costly. Millimeter-wave radar provides longer development potential in the field of traffic because of its advantages, which include its all-weather operation, compact size, low cost, and increased detection range [10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Target Tracking Algorithm Based on Adaptive Strong Tracking Extended Kalman Filter

Tian,

Guo,

2024

Electronics

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Kalman filtering is a common filtering method for millimeter-wave traffic radars. The proposal is for an Adaptive Strong Tracking Extended Kalman Filter (EKF) algorithm that aims to address the issues of classic EKF’s low accuracy and lengthy convergence time. This method, which incorporates time-varying fading effects into the covariance matrix of the traditional EKF, is based on the ST algorithm. It allows the recalibration of the covariance matrix and precise filtering and state estimation of the target vehicle. By altering the fading and attenuating factors of the ST algorithm and using orthogonality principles, many fine-tuned fading factors produced from least-squares optimization are introduced together with regionally optimum attenuation factors. The results of Monte Carlo experiments indicate that the average velocity inaccuracy is reduced by at least 38% in comparison to existing counterparts. The results validate the efficacy of this methodology in observing vehicular movements in metropolitan regions, satisfying the prerequisites of millimeter-wave radar technology for traffic monitoring.

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“…Target tracking has been extensively researched and widely used in areas such as situational awareness, precision navigation, and marine surveillance [1,2]. However, accurate non-cooperative target tracking remains challenging, due to the unknown maneuvers of the targets.…”

Section: Introductionmentioning

confidence: 99%

Fast Distributed Multiple-Model Nonlinearity Estimation for Tracking the Non-Cooperative Highly Maneuvering Target

Zhou¹,

Wang²,

Zheng³

et al. 2022

Remote Sensing

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The newly developed near-space vehicle has the characteristics of high speed and strong maneuverability, being able to perform vertical skips and a wide range of lateral maneuvers. Tracking this kind of target with ground-based radars is difficult because of the limited detection range caused by the curvature of the Earth. Compared with ground-based radars, satellite tracking platforms equipped with Synthetic Aperture Radars (SARs) have a wide detection range, and can keep the targets in custody, making them a promising approach to tracking near-space vehicles continuously. However, this approach may not work well, due to the unknown maneuvers of the non-cooperative target, and the limited computing power of the satellites. To enhance tracking stability and accuracy, and to lower the computational burden, we have proposed a Fast Distributed Multiple-Model (FDMM) nonlinearity estimation algorithm for satellites, which adopts a novel distributed multiple-model fusion framework. This approach first requires each satellite to perform local filtering based on its own single model, and the corresponding fusion factor derived by the Wasserstein distance is solved for each local estimate; then, after diffusing the local estimates, each satellite performs multiple-model fusion on the received estimates, based on the minimum weighted Kullback–Leibler divergence; finally, each satellite updates its state estimation according to the consensus protocol. Two simulation experiments revealed that the proposed FDMM algorithm outperformed the other four tracking algorithms: the consensus-based distributed multiple-model UKF; the improved consensus-based distributed multiple-model STUKF; the consensus-based strong-tracking adaptive CKF; and the interactive multiple-model adaptive UKF; the FDMM algorithm had high tracking precision and low computational complexity, showing its effectiveness for satellites tracking the near-space target.

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Continuous Tracking of Targets for Stereoscopic HFSWR Based on IMM Filtering Combined with ELM

Cited by 16 publications

References 24 publications

The New Trend of State Estimation: From Model-Driven to Hybrid-Driven Methods

The New Trend of State Estimation: From Model-Driven to Hybrid-Driven Methods

Target Tracking Algorithm Based on Adaptive Strong Tracking Extended Kalman Filter

Fast Distributed Multiple-Model Nonlinearity Estimation for Tracking the Non-Cooperative Highly Maneuvering Target

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