Labeled Multi-Bernoulli Filter Joint Detection and Tracking of Radar Targets

Liu, Rang; Fan, Hongqi; Xiao, Hanguang

doi:10.3390/app9194187

Cited by 9 publications

(9 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [22], [24], [25], the Rayleigh distribution describes the amplitude fluctuation of the target and clutter. The probability density functions of the clutter and target amplitudes are expressed as follows:…”

Section: A Active Sonar Measurementmentioning

confidence: 99%

“…GLMB filtering was implemented in [14], [15] using only position measurements of the target and clutter. In [24], [25], amplitude information aided LMB filtering (i.e., A-LMB) was proposed. We further model multidimensional measurements (position, radial velocity and echo amplitude) in active sonar, calculating the target likelihood and clutter likelihood, which can be used in the update step of the GLMB filter.…”

Section: Multidimensional Measurement Modelingmentioning

confidence: 99%

“…In [22], [23], the A-PHD filter was proposed based on the echo amplitude information, through which the variance in the cardinality estimation was significantly reduced compared to the PHD filter. In [24], [25], the echo amplitude information was also used to propose the A-LMB filter, which was applied to radar target tracking and achieved better processing results than the LMB filter. Using the multidimensional measurement information, we propose a multidimensional information fusion method via the generalized labeled multi-Bernoulli (MDI-GLMB) filter.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Multidimensional Information Fusion in Active Sonar via the Generalized Labeled Multi-Bernoulli Filter

Sun

Zhou

2020

IEEE Access

View full text Add to dashboard Cite

Under the conditions of low detection probabilities, high clutter rates, low data-sampling rates, large measurement errors, and unknown prior information of the target position, multi-object tracking is difficult. This paper proposes a multidimensional information fusion method in active sonar via the generalized labeled multi-Bernoulli (GLMB) filter. After modeling the position measurement, radial velocity measurement and amplitude measurement of the target and clutter, new target births are adaptively generated by the measurement-driven model, predictions are made by the target motion model and updates are performed via multidimensional measurements with the generalized likelihood in the GLMB filter, which enables the measurement information of different dimensions to be elegantly applied to information fusion and significantly improves the filter performance. The contribution of specific dimension measurement to fusion can be evaluated by the Kullback-Leibler (KL) divergence. In the efficient implementation, we propose flat Gibbs sampling to realize multiple hypothesis optimization. Moreover, the filtering recursion is derived from Gaussian mixtures. Simulations are presented to verify the proposed method. INDEX TERMS Random finite sets, generalized labeled multi-Bernoulli filter, multidimensional information fusion, flat Gibbs sampling.

show abstract

Section: A Active Sonar Measurementmentioning

confidence: 99%

Section: Multidimensional Measurement Modelingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multidimensional Information Fusion in Active Sonar via the Generalized Labeled Multi-Bernoulli Filter

Sun

Zhou

2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…The ocean parameters such as wavelength, wave direction, wave height and wave period can be obtained [1], and the ocean dynamic parameters such as wind, current and wave can be extracted [2][3][4]. In the process of ocean information extraction, many noises affect the extraction accuracy, including co-channel interference, target and rainfall [5,6]. Among them, rainfall has the greatest impact on the radar image and seriously interferes with the accurate extraction of ocean wave information [7].…”

Section: Introductionmentioning

confidence: 99%

A Method for Rainfall Detection and Rainfall Intensity Level Retrieval from X-Band Marine Radar Images

Lu¹,

Sun²,

Zhou³

2021

Applied Sciences

View full text Add to dashboard Cite

Currently, it is a hot research topic to retrieve the wave parameters by using X-band marine radar. However, the rainfall noise usually exists in the collected marine radar images, which seriously interferes with the extraction of the wave parameters. To reduce the influence of rainfall noise, the zero-pixel percentage (ZPP) method is widely used to detect rainfall in radar images, but the detection accuracy is limited, and the selection of the threshold needs to be further studied. Based on the ZPP method, the ratio of zero intensity to echo (RZE) method for rainfall detection is proposed in this paper. The detection threshold is determined by statistical analysis of a large amount of radar data. Additionally, it is proposed for the first time to retrieve the rainfall intensity level from X-band marine radar images. In addition, the concept of the occlusion area is proposed. The proposed area and the wave area are used as the rainfall detection area of the radar image, respectively, for experimental research. The data obtained from the Pingtan experimental base in Fujian Province are used to verify the effectiveness of the proposed method. The experimental results show that the detection accuracy of the proposed method is 11.7% higher than that of the ZPP method, and the accuracy of rainfall intensity level retrieval is 84%.

show abstract

“…Target tracking techniques have been studied extensively with fruitful results and widely developed in both military and civil fields [1][2][3][4][5][6][7][8][9][10]. In traditional tracking applications, it is generally assumed that at most one measurement can be received by sensors per time and the target is modeled as a single point mass.…”

Section: Introductionmentioning

confidence: 99%

Tracking of Multiple Closely Spaced Extended Targets Based on Prediction-Driven Measurement Sub-Partitioning Algorithm

Sun

et al. 2020

Applied Sciences

View full text Add to dashboard Cite

For multiple extended target tracking, the accuracy of measurement partitioning directly affects the target tracking performance, so the existing partitioning algorithms tend to use as many partitions as possible to obtain accurate estimates of target number and states. Unfortunately, this may create an intolerable computational burden. What is worse is that the measurement partitioning problem of closely spaced targets is still challenging and difficult to solve well. In view of this, a prediction-driven measurement sub-partitioning (PMS) algorithm is first proposed, in which target predictions are fully utilized to determine the clustering centers for obtaining accurate partitioning results. Due to its concise mathematical forms and favorable properties, redundant measurement partitions can be eliminated so that the computational burden is largely reduced. More importantly, the unreasonable target predictions may be marked and replaced by PMS for solving the so-called cardinality underestimation problem without adding extra measurement partitions. PMS is simple to implement, and based on it, an effective multiple closely spaced extended target tracking approach is easily obtained. Simulation results verify the benefit of what we proposed—it has a much faster tracking speed without degrading the performance compared with other approaches, especially in a closely spaced target tracking scenario.

show abstract

Labeled Multi-Bernoulli Filter Joint Detection and Tracking of Radar Targets

Cited by 9 publications

References 31 publications

Multidimensional Information Fusion in Active Sonar via the Generalized Labeled Multi-Bernoulli Filter

Multidimensional Information Fusion in Active Sonar via the Generalized Labeled Multi-Bernoulli Filter

A Method for Rainfall Detection and Rainfall Intensity Level Retrieval from X-Band Marine Radar Images

Tracking of Multiple Closely Spaced Extended Targets Based on Prediction-Driven Measurement Sub-Partitioning Algorithm

Contact Info

Product

Resources

About