Gaussian sum pseudolinear Kalman filter for bearings‐only tracking

Jiang, Haonan; Cai, Yuanli

doi:10.1049/iet-cta.2019.0597

Cited by 13 publications

(5 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To verify the performance of the proposed method, the field tests are conducted by a modified HAVAL SUV with drive/brake/steer-by-wire systems as shown in Figure 10, and the specification of the vehicle is given in Table 2. To demonstrate the performances of the proposed method, two test scenes are adopted in this section and a standardized KF-based tracking method [19][20][21] is also performed for comparison. Moreover, an ACC control method 45 is utilized to obtain the real-time referenced acceleration of ACC system to evaluate the riding comfort based on these two CIPV tracking methods.…”

Section: Experimental Verificationmentioning

confidence: 99%

“…To demonstrate the performances of the proposed method, two test scenes are adopted in this section and a standardized KF-based tracking method 19–21 is also performed for comparison. Moreover, an ACC control method 45 is utilized to obtain the real-time referenced acceleration of ACC system to evaluate the riding comfort based on these two CIPV tracking methods.…”

Section: Experimental Verificationmentioning

confidence: 99%

“…17 The KF-based and its improved methods are the preferred and widely used techniques. 18–21 However, there are some disturbances of the sensor measurements and driving environment, 22 which will produce a bad robustness or tracking accuracy within some complex environment. To restrict the effect of exogenous disturbances, the H ∞ theory is the preferred tool.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An innovative method for scene recognition and target tracking for adaptive cruise control in complex traffic environment

Zhang

Liu

Wang

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part D:

View full text Add to dashboard Cite

The adaptive cruise control (ACC) system as a typical advanced driver assistant system (ADAS) has been commercially application in automotive industry for decades. An innovative method is proposed in this paper for scene recognition and target tracking for ACC application in some complex traffic environment. Firstly, a multi-sensor fusion method is established to estimate the curvature integrated by the quadratic programing (QP)-based lane boundaries detection, vehicle dynamics of lateral motion, and an improved Kalman filter (IKF) to introduce more measurement information into the feedback correction process. Then, the closet in-path vehicle (CIPV) can be selected according to the statistical distance between the tracked targets and the predicted driving path of ego vehicle. To distinguish the lane changing and curve driving behaviors, the trajectory models of obstacles are established as an ellipsoid domain equation and transformed into a regression model, which is recast as a standardized QP problem. Hence, the behaviors and scenes can be recognized effectively. To restrain the disturbance and improve the accuracy and robustness of target tracking, an [Formula: see text]-based switched tracking method is proposed by combining of the low pass filter (LPF) and [Formula: see text] theory. Finally, an accurate and robust tracker is provided for the CIPV by incorporating with four steps: IKF-based curvature fusion estimation, CIPV selection, QP-based scene recognition, and the [Formula: see text]-based observer. Moreover, two real car experiments are adopted and the results verify the effectiveness and real-time performance of the proposed method.

show abstract

Section: Experimental Verificationmentioning

confidence: 99%

Section: Experimental Verificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An innovative method for scene recognition and target tracking for adaptive cruise control in complex traffic environment

Zhang

Liu

Wang

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part D:

View full text Add to dashboard Cite

show abstract

“…A robust state estimator based on the Mahalanobis distance adaptive factor (MRE), proposed in [24], effectively addresses both model errors and spikes in observation noise, yet this adaptive factor is still not the optimal solution. Through a broader exploration of different adaptive factor design methods, we find that robust estimators (RMARE and HRE) designed using RMA methods [25] and Huber methods [26] have advantages under low noise amplification scenarios, while robust estimators (MRRE) designed using p-Huber (M-estimation) methods [27] excel in high noise amplification scenarios.…”

Section: Introductionmentioning

confidence: 99%

Multiple adaptive factors based interacting multiple model estimator

Sun,

Duan,

Xia

et al. 2024

IET Control Theory & Appl

View full text Add to dashboard Cite

In the field of optoelectronic tracking, precisely modeling the motion equations of the tracked target is often challenging, and in some cases, they may even be entirely unknown. This necessitates the use of a robust state estimator for accurate state estimation. Additionally, atmospheric turbulence, variations in illumination, and intricate observation backgrounds may introduce a significant increase in observation noise for the tracked target. To address these challenges, one approach is to introduce adaptive factors, such as the Mahalanobis method, into the robust state estimator to enhance estimation accuracy. However, further exploration has revealed that adaptive factors designed using different methods offer unique advantages in scenarios with varying levels of noise amplification. In this paper, different adaptive factors are further combined using an interacting multiple model approach, allowing the designed state estimator to exhibit stronger adaptability to noise amplification. The stability and effectiveness of this algorithm are validated through program simulations, double reflection mirror experiment, and drone trace prediction, demonstrating its applicability and reliability in diverse scenarios.

show abstract

“…In ref. [23], a control protocol based on the local bearing-only information is developed to estimate the target distance and speed by the circumnavigating agent. An estimator employing bearing information is introduced in ref.…”

Section: Introductionmentioning

confidence: 99%

Target localization and encirclement control for multi‐UAVs with limited information

Wang

Chen

Jia

et al. 2022

IET Control Theory & Appl

View full text Add to dashboard Cite

The problem of localization and encirclement control of a moving target by multi unmanned aerial vehicles (UAVs) is considered in this paper. The main objective is to guide the UAVs to form evenly spaced formations along the circumference, with the centre of the circumference tracking the movement of the target. Firstly, each UAV is assumed only to obtain the bearing angle information of the target, for which an estimator is developed to localize the target by bearing measurements. Thereafter, a distributed encirclement control algorithm based on cooperative moving path following (CMPF) is designed to drive the multi‐UAVs to circumnavigate around a moving target at a desired distance, thus developing to pursue cooperatively. Finally, the simulation results demonstrate the effectiveness of the proposed method.

show abstract

Gaussian sum pseudolinear Kalman filter for bearings‐only tracking

Cited by 13 publications

References 26 publications

An innovative method for scene recognition and target tracking for adaptive cruise control in complex traffic environment

An innovative method for scene recognition and target tracking for adaptive cruise control in complex traffic environment

Multiple adaptive factors based interacting multiple model estimator

Target localization and encirclement control for multi‐UAVs with limited information

Contact Info

Product

Resources

About