Approach of system error registration for two‐station coast radars for sea surface monitoring

Shang, Juan; Yao, Yuan

doi:10.1049/joe.2019.0749

Cited by 4 publications

(5 citation statements)

References 4 publications

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“…The integrated systematic error vector can be denoted as β = [∆r, ∆θ, ∆η, ∆t] . The estimated value β can be obtained through solving Equation (11). Obviously, Equation ( 11) is a nonlinear system of equations.…”

Section: Radar Measurement Error Modelmentioning

confidence: 99%

“…Obviously, Equation ( 11) is a nonlinear system of equations. In existing studies, no matter the GLS algorithm [22] or KF algorithm [21], Equation (11) needs to be transformed into a linear equation by using first-order Taylor expansion and ignoring higher-order terms, and then be solved. This approximation process introduces new deviations, making the estimation results inaccurate.…”

Section: Radar Measurement Error Modelmentioning

confidence: 99%

“…Traditional land-based radar calibration usually uses fixed targets with known positions on the ground or establishes registration models, and solves them through measuring the same target in the common observation area of multiple radars [9][10][11][12]. The calibration range of this method is limited, and it cannot be applied in the far sea area.…”

Section: Introductionmentioning

confidence: 99%

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Radar Error Correction Method Based on Improved Sparrow Search Algorithm

Liu,

Shi,

et al. 2024

Applied Sciences

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Aiming at the problem of the limited application range and low accuracy of existing radar calibration methods, this paper studies the radar calibration method based on cooperative targets, and establishes the integrated radar measurement error model. Then, the improved sparrow search algorithm (ISSA) is used to estimate the systematic error, so as to avoid the loss of partial accuracy caused by the process of approximating the nonlinear equation to the linear equation, thus improving the radar calibration effect. The sparrow search algorithm (SSA) is improved through integrating various strategies, and the convergence speed and stability of the algorithm are also improved. The simulation results show that the ISSA can solve radar systematic errors more accurately than the generalized least square method, Kalman filter, and SSA. It takes less time the than SSA and has a certain stability and real-time performance. The radar measurement error after correction is obviously smaller than that before correction, indicating that the proposed method is feasible and effective.

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Section: Radar Measurement Error Modelmentioning

confidence: 99%

Section: Radar Measurement Error Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Radar Error Correction Method Based on Improved Sparrow Search Algorithm

Liu,

Shi,

et al. 2024

Applied Sciences

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“…They show how root mean squared error (RMSE) of bias estimation varies with measurement noise and that the proposed approach outperforms five other approaches. Shang et al [10] use the exact maximum likelihood (EML) algorithm for spatial registration of a coastal multi-radar tracking system. They provide results from a simulated test environment of true sensor bias versus estimated sensor bias their approach.…”

Section: B State Of the Artmentioning

confidence: 99%

Performance Evaluation of Simultaneous Sensor Registration and Object Tracking Algorithm

Macdonald

Proudler

Davies

et al. 2022

2022 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)

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Reliable object tracking with multiple sensors requires that sensors are registered correctly with respect to each other. When an environment is Global Navigation Satellite System (GNSS) denied or limited -such as underwater, or in hostile regions -this task is more challenging. This paper performs uncertainty quantification on a simultaneous tracking and registration algorithm for sensor networks that does not require access to a GNSS. The method uses a particle filter combined with a bank of augmented state extended Kalman filters (EKFs). The particles represent hypotheses of registration errors between sensors, with associated weights. The EKFs are responsible for the tracking procedure and for contributing to particle state and weight updates. This is achieved through the evaluation of a likelihood. Registration errors in this paper are spatial, orientation, and temporal biases: seven distinct sensor errors are estimated alongside the tracking procedure. Monte Carlo trials are conducted for the uncertainty quantification. Since performance of particle filters is dependent on initialisation, a comparison is made between more and less favourable particle (hypothesis) initialisation. The results demonstrate the importance of initialisation, and the method is shown to perform well in tracking a fast (marginally sub-sonic) object following a bow-like trajectory (mimicking a representative scenario). Final results show the algorithm is capable of achieving angular bias estimation error of 0.0034 o , temporal bias estimation error of 0.0067 s, and spatial error of 0.021m.

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“…Pu W et al [10] used a two-stage nonlinear least squares method to solve the spatial registration problem of asynchronous multiple sensors, but only the range and azimuth errors were analyzed, which is only applicable to a two-dimensional radar. Shang J et al [11] used an exact great likelihood algorithm to achieve the spatial registration of two station coast radar systems based on noncooperative targets; the scenario posture is more realistic, but this method does not consider the attitude of the moving sensors and is only applicable in the two-dimensional plane. Lu X et al [12] took the measurement error and attitude error of the sensors into account simultaneously.…”

Section: Introductionmentioning

confidence: 99%

A Spatial Registration Method for Multi-UAVs Based on a Cooperative Platform in a Geodesic Coordinate Information-Free Environment

Dai,

Lu,

2023

Applied Sciences

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The satellite navigation system of Unmanned Aerial Vehicles (UAVs) is susceptible to external interference in a complex environment, resulting in the loss of their own geodetic coordinate information. A spatial registration method for multi-UAVs based on a cooperative platform in a geodesic coordinate information-free environment is proposed to solve this problem. The mutual observation information between UAVs is approximated by the observation information of the cooperative platform. Indirect observation information of the target can be obtained on account of mutual observation. On the basis of this, a close-range spatial registration algorithm without the geodetic coordinate information of UAVs is designed by means of the right-angle translation method. Finally, the Kalman filtering technique is used to track maritime targets. In this paper, the proposed method is verified by a simulation experiment and a practical experiment. The proposed method is 90% effective in reducing systematic errors. The tracking accuracy after alignment is significantly better than that of the original trajectory.

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Approach of system error registration for two‐station coast radars for sea surface monitoring

Cited by 4 publications

References 4 publications

Radar Error Correction Method Based on Improved Sparrow Search Algorithm

Radar Error Correction Method Based on Improved Sparrow Search Algorithm

Performance Evaluation of Simultaneous Sensor Registration and Object Tracking Algorithm

A Spatial Registration Method for Multi-UAVs Based on a Cooperative Platform in a Geodesic Coordinate Information-Free Environment

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