A Bootstrapped PMHT with Feature Measurements

Lu, Qin; Domrese, Katherine; Willett, Peter; Bar‐Shalom, Yaakov; Pattipati, Krishna R.

doi:10.1109/taes.2017.2704698

Cited by 5 publications

(3 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…How to assist the association by exploiting the target-feature information is essential for improving the target tracking performance [85,86]. Previous studies have incorporated the feature information into PMHT [87][88][89][90][91][92][93][94][95][96][97]. In some of these studies [92][93][94], the target is spatially expanded by the ellipsoid model and the tracking is assisted by the structural information of the target.…”

Section: Multiple Target Tracking With Switching Of Attribute Statesmentioning

confidence: 99%

Advanced technology of high-resolution radar: target detection, tracking, imaging, and recognition

Liang

Liu

2019

Sci. China Inf. Sci.

View full text Add to dashboard Cite

In recent years, the performances of radar resolution, coverage, and detection accuracy have been significantly improved through the use of ultra-wideband, synthetic aperture and digital signal processing technologies. High-resolution radars (HRRs) utilize wideband signals and synthetic apertures to enhance the range and angular resolutions of tracking, respectively. They also generate one-, two-, and even threedimensional high-resolution images containing the feature information of targets, from which the targets can be precisely classified and identified. Advanced signal processing algorithms in HRRs obtain important information such as range-Doppler imaging, phase-derived ranging, and micro-motion features. However, the advantages and applications of HRRs are restricted by factors such as the reduced signal-to-noise ratio (SNR) of multi-scatter point targets, decreased tracking accuracy of multi-scatter point targets, high demands of motion compensation, and low sensitivity of the target attitude. Focusing on these problems, this paper systematically introduces the novel technologies of HRRs and discusses the issues and solutions relevant to detection, tracking, imaging, and recognition. Finally, it reviews the latest progress and representative results of HRR-based research, and suggests the future development of HRRs.

show abstract

Section: Multiple Target Tracking With Switching Of Attribute Statesmentioning

confidence: 99%

Advanced technology of high-resolution radar: target detection, tracking, imaging, and recognition

Liang

Liu

2019

Sci. China Inf. Sci.

View full text Add to dashboard Cite

show abstract

“…The PMHT algorithm is based on the expectation maximization (EM) algorithm which is suitable for multitarget tracking in dense clutter environment [25]. For the bearings-only multitarget tracking problem, define the following:…”

Section: A Basic Pmht Algorithmmentioning

confidence: 99%

Underwater Bearings-Only Multitarget Tracking Based on Modified PMHT in Dense-Cluttered Environment

Zhao

et al. 2019

IEEE Access

View full text Add to dashboard Cite

Underwater bearings-only multitarget tracking has many advantages, and the probabilistic multiple hypothesis tracking (PMHT) is an elegant algorithm for multitarget tracking problem. However, the basic PMHT has following weakness: 1) the targets' posterior probability may convergent to the local maximum, which would degrade the tracking accuracy and 2) the tracking performance is sensitive to the targets' initialization. In order to overcome these weaknesses, this paper proposes a modified PMHT algorithm. The key idea of the modified PMHT algorithm is that it allows the bearings measurements at one scan come from any Gaussian density, which has the same mean and different covariance with the same target. In addition, the modified PMHT algorithm uses the deterministic annealing to reduce the dependence on the targets' initialization. To deal with the nonlinear bearings measurements, the paper uses the unscented Kalman smoother to update the target states. The simulation treats the cross moving targets and closely spaced targets for both multiple stationary sensors and single maneuvering sensor scenarios in a dense clutter environment. The simulation results show the superiority of the modified PMHT algorithm over the basic PMHT algorithm respect to accuracy and robustness for underwater bearings-only multitarget tracking problem when setting a relatively bad initialization value.INDEX TERMS Underwater, bearings-only, data association, multitarget tracking, deterministic annealing, unscented Kalman smoother, probabilistic multi-hypothesis tracker.

show abstract

“…The JPDA makes multiple hypotheses into a single hypothesis and performs the Kalman update with composite measurements. The PMHT is based on the expectation maximization (EM), which optimizes the multipletarget states' maximum a posteriori (MAP) estimation [34][35][36]. Different from the data association algorithms, the PHD, CPHD, and MBF are based on the random finite set theory, which makes all the measurements a measurement set and all the targets a target set.…”

Section: Introductionmentioning

confidence: 99%

Passive Sonar Multiple-Target Tracking with Nonlinear Doppler and Bearing Measurements Using Multiple Sensors

Ali

et al. 2021

International Journal of Aerospace Engineering

View full text Add to dashboard Cite

The major advantage of the passive multiple-target tracking is that the sonars do not emit signals and thus they can remain covert, which will reduce the risk of being attacked. However, the nonlinearity of the passive Doppler and bearing measurements, the range unobservability problem, and the measurement to target data association uncertainty make the passive multiple-target tracking problem challenging. To deal with the target to measurement data association uncertainty problem from multiple sensors, this paper proposed a batch recursive extended Rauch-Tung-Striebel smoother- (RTSS-) based probabilistic multiple hypothesis tracker (PMHT) algorithm, which can effectively handle a large number of passive measurements including clutters. The recursive extended RTSS which consists of a forward filter and a backward smoothing is used to deal with the nonlinear Doppler and bearing measurements. The target range unobservability problem is avoided due to using multiple passive sensors. The simulation results show that the proposed algorithm works well in a passive multiple-target tracking system under dense clutter environment, and its computing cost is low.

show abstract

A Bootstrapped PMHT with Feature Measurements

Cited by 5 publications

References 15 publications

Advanced technology of high-resolution radar: target detection, tracking, imaging, and recognition

Advanced technology of high-resolution radar: target detection, tracking, imaging, and recognition

Underwater Bearings-Only Multitarget Tracking Based on Modified PMHT in Dense-Cluttered Environment

Passive Sonar Multiple-Target Tracking with Nonlinear Doppler and Bearing Measurements Using Multiple Sensors

Contact Info

Product

Resources

About