scholarly journals A Labeled GM-PHD Filter for Explicitly Tracking Multiple Targets

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3932
Author(s):  
Yiyue Gao ◽  
Defu Jiang ◽  
Chao Zhang ◽  
Su Guo
Keyword(s):  
Numerical Experiments ◽  
Gaussian Mixture ◽  
Multitarget Tracking ◽  
Prior Density ◽  
Negative Effects ◽  
Density Region ◽  
Probability Hypothesis Density ◽  
Time Performance ◽  
Single State ◽  
The Individual

In this study, an explicit track continuity algorithm is proposed for multitarget tracking (MTT) based on the Gaussian mixture (GM) implementation of the probability hypothesis density (PHD) filter. Trajectory maintenance and multitarget state extraction in the GM-PHD filter have not been effectively integrated to date. To address this problem, we propose an improved GM-PHD filter. In this approach, the Gaussian components are classified and labeled, and multitarget state extraction is converted into multiple single-state extractions. This provides the identity label of the individual target and can shield against the negative effects of clutter in the prior density region on the estimates, thus realizing the integration of trajectory maintenance with state extraction in the GM-PHD filter. As no additional associated procedures are required, the overall real-time performance of the proposed filter is similar to or slightly lower than that of the basic GM-PHD filter. The results of numerical experiments demonstrate that the proposed approach can achieve explicit track continuity.

scholarly journals Multisensor RFS Filters for Unknown and Changing Detection Probability

Electronics ◽  
2019 ◽  
Vol 8 (7) ◽  
pp. 741
Author(s):  
Zhang ◽  
Li ◽  
Sun
Keyword(s):  
Detection Probability ◽  
A Priori ◽  
Gaussian Mixture ◽  
Multitarget Tracking ◽  
Probability Hypothesis Density ◽  
Inverse Gamma ◽  
Motion State ◽  
Performance Deterioration ◽  
Cphd Filter ◽  
Monitoring Area

The detection probability is an important parameter in multisensor multitarget tracking. The existing multisensor multi-Bernoulli (MS-MeMBer) filter and multisensor cardinalized probability hypothesis density (MS-CPHD) filter require that detection probability is a priori. However, in reality, the value of the detection probability is constantly changing due to the influence of sensors, targets, and other environmental characteristics. Therefore, to alleviate the performance deterioration caused by the mismatch of the detection probability, this paper applies the inverse gamma Gaussian mixture (IGGM) distribution to both the MS-MeMBer filter and the MS-CPHD filter. Specifically, the feature used for detection is assumed to obey the inverse gamma distribution and is statistically independent of the target’s spatial position. The feature is then integrated into the target state to iteratively estimate the target detection probability as well as the motion state. The experimental results demonstrate that the proposed methods can achieve a better filtering performance in scenarios with unknown and changing detection probability. It is also shown that the distribution of the sensors has a vital influence on the filtering accuracy, and the filters perform better when sensors are dispersed in the monitoring area.

scholarly journals Tracking Ground Targets with a Road Constraint Using a GMPHD Filter

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2723 ◽  
Author(s):  
Jihong Zheng ◽  
Meiguo Gao
Keyword(s):  
Coordinate Transformation ◽  
Gaussian Mixture ◽  
State Constraint ◽  
Transformation Process ◽  
Multitarget Tracking ◽  
Probability Hypothesis Density ◽  
Target State ◽  
Cluttered Environment ◽  
Predicted Probability ◽  
Track Association

The Gaussian mixture probability hypothesis density (GMPHD) filter is applied to the problem of tracking ground moving targets in clutter due to its excellent multitarget tracking performance, such as avoiding measurement-to-track association, and its easy implementation. For the existing GMPHD-based ground target tracking algorithm (the GMPHD filter incorporating map information using a coordinate transforming method, CT-GMPHD), the predicted probability density of its target state is given in road coordinates, while its target state update needs to be performed in Cartesian ground coordinates. Although the algorithm can improve the filtering performance to a certain extent, the coordinate transformation process increases the complexity of the algorithm and reduces its computational efficiency. To address this issue, this paper proposes two non-coordinate transformation roadmap fusion algorithms: directional process noise fusion (DNP-GMPHD) and state constraint fusion (SC-GMPHD). The simulation results show that, compared with the existing algorithms, the two proposed roadmap fusion algorithms are more accurate and efficient for target estimation performance on straight and curved roads in a cluttered environment. The proposed methods are additionally applied using a cardinalized PHD (CPHD) filter and a labeled multi-Bernoulli (LMB) filter. It is found that the PHD filter performs less well than the CPHD and LMB filters, but that it is also computationally cheaper.

scholarly journals GM-PHD Filter Combined with Track-Estimate Association and Numerical Interpolation

2015 ◽  
Vol 2015 ◽  
pp. 1-9
Author(s):  
Jinguang Chen ◽  
Bugao Xu ◽  
Lili Ma ◽  
Rui Sun
Keyword(s):  
False Alarm ◽  
Detection Rate ◽  
Gaussian Mixture ◽  
Multitarget Tracking ◽  
Probability Hypothesis Density ◽  
Interpolation Technique ◽  
Numerical Interpolation ◽  
Simulation Results

For the standard Gaussian mixture probability hypothesis density (GM-PHD) filter, the number of targets can be overestimated if the clutter rate is too high or underestimated if the detection rate is too low. These problems seriously affect the accuracy of multitarget tracking for the number and the value of measurements and clutters cannot be distinguished and recognized. Therefore, we proposed an improved GM-PHD filter to tackle these problems. Firstly, a track-estimate association was implemented in the filtering process to detect and remove false-alarm targets. Secondly, a numerical interpolation technique was used to compensate the missing targets caused by low detection rate. At the end of this paper, simulation results were presented to demonstrate the proposed GM-PHD algorithm is more effective in estimating the number and state of targets than the previous ones.

Grünbrücke Loterbuck A 4.2.9: Eine Erfolgskontrolle nach drei Jahren | Fauna Overbridge Loterbuck A 4.2.9: Success Control after a Period of Three Years

2000 ◽  
Vol 151 (8) ◽  
pp. 290-297
Author(s):  
Stephan Hatt
Keyword(s):  
Local People ◽  
Traffic Network ◽  
Negative Effects ◽  
The Individual

The expansion of the traffic network, in particular the construction of highways, has continuously diminished and divided into small sections the habitat of wild-living animals during the last decades. However, these negative effects can be minimised if suitable measures with regard to line-conduction and construction are taken against. One of these possibilities are the sown-down overbridges. It is essential that these constructions are planned and built in order to meet the requirements of their future users – the various wild-living animals. This study investigates the success of one of these sown-down overbridges. It is this the Loterbuck-overbridge on the A 4.2.9 near Henggart in the canton of Zurich, Switzerland. The focus of this investigation was to find out which species of wild-living animals use the bridge and how much it is frequented. Local people and specialists of the region were interviewed and tracks were picked up on site. Taking into consideration five criteria (species of wild-living animals, positioning and number of overbridges nearby, dimensioning and design of the individual overbridges), the interviews and tracks were assessed. The Loterbuck-overbridge is used by all larger wild-living animals of the region. Especially the browsing and rubbing tracks of deer show that the overbridge has been accepted not only as sown-down overbridge but also as habitat.

scholarly journals A numerical study of gravity-driven instability in strongly coupled dusty plasma. Part 1. Rayleigh–Taylor instability and buoyancy-driven instability

2021 ◽  
Vol 87 (2) ◽  
Author(s):  
Vikram S. Dharodi ◽  
Amita Das
Keyword(s):  
Dusty Plasma ◽  
Numerical Study ◽  
Fluid Model ◽  
Density Region ◽  
Strongly Coupled ◽  
Rising Bubble ◽  
Inhomogeneous Density ◽  
Gravity Driven ◽  
The Individual ◽  
Strongly Coupled Dusty Plasma

Rayleigh–Taylor (RT) and buoyancy-driven (BD) instabilities are driven by gravity in a fluid system with inhomogeneous density. The paper investigates these instabilities for a strongly coupled dusty plasma medium. This medium has been represented here in the framework of the generalized hydrodynamics (GHD) fluid model which treats it as a viscoelastic medium. The incompressible limit of the GHD model is considered here. The RT instability is explored both for gradual and sharp density gradients stratified against gravity. The BD instability is discussed by studying the evolution of a rising bubble (a localized low-density region) and a falling droplet (a localized high-density region) in the presence of gravity. Since both the rising bubble and falling droplet have symmetry in spatial distribution, we observe that a falling droplet process is equivalent to a rising bubble. We also find that both the gravity-driven instabilities get suppressed with increasing coupling strength of the medium. These observations have been illustrated analytically as well as by carrying out two-dimensional nonlinear simulations. Part 2 of this paper is planned to extend the present study of the individual evolution of a bubble and a droplet to their combined evolution in order to understand the interaction between them.

scholarly journals Optimal Nitrogen Fertilization to Reach the Maximum Grain and Stover Yields of Maize (Zea mays L.): Tendency Modeling

Agronomy ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1354
Author(s):  
Sergio E. Medina-Cuéllar ◽  
Deli N. Tirado-González ◽  
Marcos Portillo-Vázquez ◽  
Sergio Orozco-Cirilo ◽  
Marco A. López-Santiago ◽  
...  
Keyword(s):  
Nitrogen Fertilization ◽  
Zea Mays L ◽  
N Fertilization ◽  
Human Consumption ◽  
Alluvial Soils ◽  
Grain Production ◽  
Negative Effects ◽  
Maize Stover ◽  
The Individual ◽  
Quadratic Models

Utilization of maize stover to the production of meat and milk and saving the grains for human consumption would be one strategy for the optimal usage of resources. Variance and tendency analyses were applied to find the optimal nitrogen (N) fertilization dose (0, 100, 145, 190, 240, and 290 kg/ha) for forage (F), stover (S), cob (C), and grain (G) yields, as well as the optimal grain-to-forage, cob-to-forage, and cob-to-stover ratios (G:F, C:F, and C:S, respectively). The study was performed in central Mexico (20.691389° N and −101.259722° W, 1740 m a.m.s.l.; Cwa (Köppen), 699 mm annual precipitation; alluvial soils). N-190 and N-240 improved the individual yields and ratios the most. Linear and quadratic models for CDM, GDM, and G:F ratio had coefficients of determination (R2) of 0.20–0.46 (p < 0.03). Cubic showed R2 = 0.30–0.72 (p < 0.02), and the best models were for CDM, GDM, and the G:F, C:F, and C:S DM ratios (R2 = 0.60–0.72; p < 0.0002). Neither SHB nor SDM negatively correlated with CDM or GDM (r = 0.23–0.48; p < 0.0001). Excess of N had negative effects on forage, stover, cobs, and grains yields, but optimal N fertilization increased the proportion of the G:F, C:F, and C:S ratios, as well as the SHB and SDM yields, without negative effects on grain production.

scholarly journals Individual behavioural traits not social context affects learning about novel objects in archerfish

2021 ◽  
Vol 75 (3) ◽  
Author(s):  
Nick A. R. Jones ◽  
Helen C. Spence-Jones ◽  
Mike Webster ◽  
Luke Rendell
Keyword(s):  
Social Context ◽  
Social Environment ◽  
Social Contexts ◽  
Negative Effects ◽  
Behavioural Phenotype ◽  
Social Species ◽  
The Social ◽  
Social Animals ◽  
Novel Objects ◽  
The Individual

Abstract Learning can enable rapid behavioural responses to changing conditions but can depend on the social context and behavioural phenotype of the individual. Learning rates have been linked to consistent individual differences in behavioural traits, especially in situations which require engaging with novelty, but the social environment can also play an important role. The presence of others can modulate the effects of individual behavioural traits and afford access to social information that can reduce the need for ‘risky’ asocial learning. Most studies of social effects on learning are focused on more social species; however, such factors can be important even for less-social animals, including non-grouping or facultatively social species which may still derive benefit from social conditions. Using archerfish, Toxotes chatareus, which exhibit high levels of intra-specific competition and do not show a strong preference for grouping, we explored the effect of social contexts on learning. Individually housed fish were assayed in an ‘open-field’ test and then trained to criterion in a task where fish learnt to shoot a novel cue for a food reward—with a conspecific neighbour visible either during training, outside of training or never (full, partial or no visible presence). Time to learn to shoot the novel cue differed across individuals but not across social context. This suggests that social context does not have a strong effect on learning in this non-obligatory social species; instead, it further highlights the importance that inter-individual variation in behavioural traits can have on learning. Significance statement Some individuals learn faster than others. Many factors can affect an animal’s learning rate—for example, its behavioural phenotype may make it more or less likely to engage with novel objects. The social environment can play a big role too—affecting learning directly and modifying the effects of an individual’s traits. Effects of social context on learning mostly come from highly social species, but recent research has focused on less-social animals. Archerfish display high intra-specific competition, and our study suggests that social context has no strong effect on their learning to shoot novel objects for rewards. Our results may have some relevance for social enrichment and welfare of this increasingly studied species, suggesting there are no negative effects of short- to medium-term isolation of this species—at least with regards to behavioural performance and learning tasks.

scholarly journals Strong Tracking PHD Filter Based on Variational Bayesian with Inaccurate Process and Measurement Noise Covariance

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1126
Author(s):  
Zhentao Hu ◽  
Linlin Yang ◽  
Yong Jin ◽  
Han Wang ◽  
Shibo Yang
Keyword(s):  
Real Time ◽  
Gaussian Mixture ◽  
Measurement Noise ◽  
Tracking Accuracy ◽  
Process Noise ◽  
Probability Hypothesis Density ◽  
Measurement Noise Covariance ◽  
Multi Target Tracking ◽  
Noise Covariance ◽  
Fading Factor

Assuming that the measurement and process noise covariances are known, the probability hypothesis density (PHD) filter is effective in real-time multi-target tracking; however, noise covariance is often unknown and time-varying for an actual scene. To solve this problem, a strong tracking PHD filter based on Variational Bayes (VB) approximation is proposed in this paper. The measurement noise covariance is described in the linear system by the inverse Wishart (IW) distribution. Then, the fading factor in the strong tracking principle uses the optimal measurement noise covariance at the previous moment to control the state prediction covariance in real-time. The Gaussian IW (GIW) joint distribution adopts the VB approximation to jointly return the measurement noise covariance and the target state covariance. The simulation results show that, compared with the traditional Gaussian mixture PHD (GM-PHD) and the VB-adaptive PHD, the proposed algorithm has higher tracking accuracy and stronger robustness in a more reasonable calculation time.

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