Millimeter Wave Radar Target Tracking Based on Adaptive Kalman Filter

Zhai, Guangyao; Wu, Cheng; Wang, Yiming

doi:10.1109/ivs.2018.8500505

Cited by 30 publications

(16 citation statements)

References 12 publications

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“…This section reports on the pre-processing methods that are mentioned in the papers. Simulation with a ray tracing tool [145], [162], [184], [186], single-radar simulation [140], multi-radar simulation [101], imaging simulation [156], and radar data modeling [174], [201], [202] are considered to be [42] Spectrogram (range, velocity) Radar [47] Point cloud frame (x, y, z) Radar [48] Spectrogram (range, velocity) Radar IF I/Q signal, (time, IF channel), Spectrogram (range, velocity) [51] R, θ, and vr Undefined Radar [52] Long. and lat.…”

Section: B Pre-processingmentioning

confidence: 99%

“…Several Bayesian filters exist, such as the custom Bayesian [45], [185], particle [44], [136], α − β [128], Kalman [46], [53], [58], extended Kalman [55], [94], [96], [114], [115], [135], [186], fusion extended Kalman [95], [146], unscented Kalman [89], fusion adaptive Kalman [51], and adaptive Sage-Husa Kalman [52], [61] filter. The Kalman filter [219], under linear, quadratic, and Gaussian assumptions, can represent the state transition and observation functions x t = g(x t−1 , pn t ) and s t = h(x t , mn t ) as a set of linear equations.…”

Section: Analytical Modelingmentioning

confidence: 99%

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Millimeter Wave Sensing: A Review of Application Pipelines and Building Blocks

et al. 2021

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The increasing bandwidth requirement of new wireless applications has lead to standardization of the millimeter wave spectrum for high-speed wireless communication. The millimeter wave spectrum is part of 5G and covers frequencies between 30 and 300 GHz that correspond to wavelengths ranging from 10 to 1 mm. Although millimeter wave is often considered as a communication medium, it has also proved to be an excellent 'sensor', thanks to its narrow beams, operation across a wide bandwidth, and interaction with atmospheric constituents. In this paper, which is to the best of our knowledge the first review that completely covers millimeter wave sensing application pipelines, we provide a comprehensive overview and analysis of different basic application pipeline building blocks, including hardware, algorithms, analytical models, and model evaluation techniques. The review also provides a taxonomy that highlights different millimeter wave sensing application domains. By performing a thorough analysis, complying with the systematic literature review methodology and reviewing 165 papers, we not only extend previous investigations focused only on communication aspects of the millimeter wave technology and using millimeter wave technology for active imaging, but also highlight scientific and technological challenges and trends, and provide a future perspective for applications of millimeter wave as a sensing technology.

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Section: B Pre-processingmentioning

confidence: 99%

Section: Analytical Modelingmentioning

confidence: 99%

Millimeter Wave Sensing: A Review of Application Pipelines and Building Blocks

et al. 2021

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“…Based on the above, a radar system tracking model can be established [1,8,9]:{Xk=f(Xk−1)+Γ(Xk−1,tk)+ωkZk=h(Xk)+vkwhere f(·) is the target dynamic model, Γ(·) is the dynamic model bias caused by the target maneuver, which is a time-varying nonparametric and unknown component, ωk is the dynamic model random error with the Gaussian white noise distribution p(ωk)~N(0,Qk), h(·) is a non-linear measurement function, and vk is the measurement random error with another Gaussian white noise distribution p(vk)~N(0,Rk)…”

Section: Basic Models and Related Workmentioning

confidence: 99%

“…Target tracking is a fundamental and critical task in many sensor-based practical applications including radar-based tracking [1], sonar-based tracking [2], wireless sensor networks [3], video surveillance [4], navigation [5], and mobile robotics [6]. Tracking maneuvering targets is a challenging task because sensor systems are inevitably inaccurate and they are unaware of the uncertain external forces that may be acting on targets, so the target’s dynamic properties cannot be modeled exactly.…”

Section: Introductionmentioning

confidence: 99%

Maneuvering Target Tracking Using Simultaneous Optimization and Feedback Learning Algorithm Based on Elman Neural Network

Liu

Xia

Wang

2019

Sensors

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Tracking maneuvering targets is a challenging problem for sensors because of the unpredictability of the target’s motion. Unlike classical statistical modeling of target maneuvers, a simultaneous optimization and feedback learning algorithm for maneuvering target tracking based on the Elman neural network (ENN) is proposed in this paper. In the feedback strategy, a scale factor is learnt to adaptively tune the dynamic model’s error covariance matrix, and in the optimization strategy, a corrected component of the state vector is learnt to refine the final state estimation. These two strategies are integrated in an ENN-based unscented Kalman filter (UKF) model called ELM-UKF. This filter can be trained online by the filter residual, innovation and gain matrix of the UKF to simultaneously achieve maneuver feedback and an optimized estimation. Monte Carlo experiments on synthesized radar data showed that our algorithm had better performance on filtering precision compared with most maneuvering target tracking algorithms.

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“…Radar is an important part of the contemporary intelligent transportation system [1][2][3]. Multi-target tracking with radar is also a hot issue in intelligent transportation research [4][5][6]. By tracking passing vehicles, risky driving behavior can be predicted and an early warning signal can be issued [7,8].…”

Section: Introductionmentioning

confidence: 99%

A Novel Density Peak Fuzzy Clustering Algorithm for Moving Vehicles Using Traffic Radar

et al. 2019

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The detection of adjacent vehicles in highway scenes has the problem of inaccurate clustering results. In order to solve this problem, this paper proposes a new clustering algorithm, namely Spindle-based Density Peak Fuzzy Clustering (SDPFC) algorithm. Its main feature is to use the density peak clustering algorithm to perform initial clustering to obtain the number of clusters and the cluster center of each cluster. The final clustering result is obtained by a fuzzy clustering algorithm based on the spindle update. The experimental data are the radar echo signal collected in the real highway scenes. Compared with the DBSCAN, FCM, and K-Means algorithms, the algorithm has higher clustering accuracy in certain scenes. The average clustering accuracy of SDPFC can reach more than 95%. It is also proved that the proposed algorithm has strong robustness in certain highway scenes.

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Millimeter Wave Radar Target Tracking Based on Adaptive Kalman Filter

Cited by 30 publications

References 12 publications

Millimeter Wave Sensing: A Review of Application Pipelines and Building Blocks

Millimeter Wave Sensing: A Review of Application Pipelines and Building Blocks

Maneuvering Target Tracking Using Simultaneous Optimization and Feedback Learning Algorithm Based on Elman Neural Network

A Novel Density Peak Fuzzy Clustering Algorithm for Moving Vehicles Using Traffic Radar

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