Tracking multiple space objects using multiple surveillance sensors is a critical approach in many Space Situation Awareness(SSA) applications. In this process, the uncertainties of targets,dynamics, and observations ...Tracking multiple space objects using multiple surveillance sensors is a critical approach in many Space Situation Awareness(SSA) applications. In this process, the uncertainties of targets,dynamics, and observations are usually represented by the probability distributions. However, precise characterization of uncertainty becomes challenging due to imperfect knowledge about some key aspects, such as birth targets and sensor detection profiles. To overcome this challenge, this paper proposes a multi-sensor possibility PHD filter based on the theory of outer probability measures. An effective compensation method is introduced to tackle variations in the fields of view of SSA sensors or instances of missed detections, aiming to mitigate the inconsistency in localized information. The proposed method is adapted to centralized and distributed sensor networks, offering effective solutions for multi-sensor multi-target tracking. The major innovation of the proposed method compared with typical methods is the proper description of epistemic uncertainty, which yields more robust performance in the scenarios of lacking some information about the system.The effectiveness of the multi-sensor possibility PHD filter is demonstrated by a comparison with conventional methods in two simulated scenarios.展开更多
Plant Homeo Domain(PHD)proteins are involved in diverse biological processes during plant growth.However,the regulation of PHD genes on rice cold stress response remains largely unknown.Here,we reported that PHD17 neg...Plant Homeo Domain(PHD)proteins are involved in diverse biological processes during plant growth.However,the regulation of PHD genes on rice cold stress response remains largely unknown.Here,we reported that PHD17 negatively regulated cold tolerance in rice seedlings as a cleavage target of miR1320.PHD17 expression was greatly induced by cold stress,and was down-regulated by miR1320 overexpression and up-regulated by miR1320 knockdown.Through 5'RACE and dual luciferase assays,we found that miR1320 targeted and cleaved the 3'UTR region of PHD17.PHD17 was a nuclearlocalized protein and acted as a transcriptional activator in yeast.PHD17 overexpression reduced cold tolerance of rice seedlings,while knockout of PHD17 increased cold tolerance,partially via the CBF cold signaling.By combining transcriptomic and physiological analyses,we demonstrated that PHD17 modulated ROS homeostasis and flavonoid accumulation under cold stress.K-means clustering analysis revealed that differentially expressed genes in PHD17 transgenic lines were significantly enriched in the jasmonic acid(JA)biosynthesis pathway,and expression of JA biosynthesis and signaling genes was verified to be affected by PHD17.Cold stress tests applied with MeJA or IBU(JA synthesis inhibitor)further suggested the involvement of PHD17 in JA-mediated cold signaling.Taken together,our results suggest that PHD17 acts downstream of miR1320 and negatively regulates cold tolerance of rice seedlings through JA-mediated signaling pathway.展开更多
An algorithm to track multiple sharply maneuvering targets without prior knowledge about new target birth is proposed. These targets are capable of achieving sharp maneuvers within a short period of time, such as dron...An algorithm to track multiple sharply maneuvering targets without prior knowledge about new target birth is proposed. These targets are capable of achieving sharp maneuvers within a short period of time, such as drones and agile missiles.The probability hypothesis density (PHD) filter, which propagates only the first-order statistical moment of the full target posterior, has been shown to be a computationally efficient solution to multitarget tracking problems. However, the standard PHD filter operates on the single dynamic model and requires prior information about target birth distribution, which leads to many limitations in terms of practical applications. In this paper,we introduce a nonzero mean, white noise turn rate dynamic model and generalize jump Markov systems to multitarget case to accommodate sharply maneuvering dynamics. Moreover, to adaptively estimate newborn targets’information, a measurement-driven method based on the recursive random sampling consensus (RANSAC) algorithm is proposed. Simulation results demonstrate that the proposed method achieves significant improvement in tracking multiple sharply maneuvering targets with adaptive birth estimation.展开更多
基金funded by the National Natural Science Foundation of China(No.12202049)the Beijing Institute of Technology Research Fund Program for Young Scholars,China.
文摘Tracking multiple space objects using multiple surveillance sensors is a critical approach in many Space Situation Awareness(SSA) applications. In this process, the uncertainties of targets,dynamics, and observations are usually represented by the probability distributions. However, precise characterization of uncertainty becomes challenging due to imperfect knowledge about some key aspects, such as birth targets and sensor detection profiles. To overcome this challenge, this paper proposes a multi-sensor possibility PHD filter based on the theory of outer probability measures. An effective compensation method is introduced to tackle variations in the fields of view of SSA sensors or instances of missed detections, aiming to mitigate the inconsistency in localized information. The proposed method is adapted to centralized and distributed sensor networks, offering effective solutions for multi-sensor multi-target tracking. The major innovation of the proposed method compared with typical methods is the proper description of epistemic uncertainty, which yields more robust performance in the scenarios of lacking some information about the system.The effectiveness of the multi-sensor possibility PHD filter is demonstrated by a comparison with conventional methods in two simulated scenarios.
基金supported by the National Natural Science Foundation of China (31971826,U20A2025)Natural Science Foundation of Heilongjiang province (JQ2021C002)the College Student Innovation and Entrepreneurship Program Training Program (202210223055)。
文摘Plant Homeo Domain(PHD)proteins are involved in diverse biological processes during plant growth.However,the regulation of PHD genes on rice cold stress response remains largely unknown.Here,we reported that PHD17 negatively regulated cold tolerance in rice seedlings as a cleavage target of miR1320.PHD17 expression was greatly induced by cold stress,and was down-regulated by miR1320 overexpression and up-regulated by miR1320 knockdown.Through 5'RACE and dual luciferase assays,we found that miR1320 targeted and cleaved the 3'UTR region of PHD17.PHD17 was a nuclearlocalized protein and acted as a transcriptional activator in yeast.PHD17 overexpression reduced cold tolerance of rice seedlings,while knockout of PHD17 increased cold tolerance,partially via the CBF cold signaling.By combining transcriptomic and physiological analyses,we demonstrated that PHD17 modulated ROS homeostasis and flavonoid accumulation under cold stress.K-means clustering analysis revealed that differentially expressed genes in PHD17 transgenic lines were significantly enriched in the jasmonic acid(JA)biosynthesis pathway,and expression of JA biosynthesis and signaling genes was verified to be affected by PHD17.Cold stress tests applied with MeJA or IBU(JA synthesis inhibitor)further suggested the involvement of PHD17 in JA-mediated cold signaling.Taken together,our results suggest that PHD17 acts downstream of miR1320 and negatively regulates cold tolerance of rice seedlings through JA-mediated signaling pathway.
基金supported by the National Natural Science Foundation of China (61773142)。
文摘An algorithm to track multiple sharply maneuvering targets without prior knowledge about new target birth is proposed. These targets are capable of achieving sharp maneuvers within a short period of time, such as drones and agile missiles.The probability hypothesis density (PHD) filter, which propagates only the first-order statistical moment of the full target posterior, has been shown to be a computationally efficient solution to multitarget tracking problems. However, the standard PHD filter operates on the single dynamic model and requires prior information about target birth distribution, which leads to many limitations in terms of practical applications. In this paper,we introduce a nonzero mean, white noise turn rate dynamic model and generalize jump Markov systems to multitarget case to accommodate sharply maneuvering dynamics. Moreover, to adaptively estimate newborn targets’information, a measurement-driven method based on the recursive random sampling consensus (RANSAC) algorithm is proposed. Simulation results demonstrate that the proposed method achieves significant improvement in tracking multiple sharply maneuvering targets with adaptive birth estimation.
