A Bayesian track-before-detect procedure for passive radars

International audienceThis article presents a Bayesian algorithm for detection and tracking of a target using the track-before-detect framework. This strategy enables to detect weak targets and to circumvent the data association problem originating from the detection stage of classical radar systems. We first establish a Bayesian recursion, which propagates the target state probability density function. Since raw measurements are generally related to the target state through a nonlinear observation function, this recursion does not admit a closed form expression. Therefore, in order to obtain a tractable formulation, we propose a Gaussian mixture approximation. Our targeted application is passive radar, with civilian broadcasters used as illuminators of opportunity. Numerical simulations show the ability of the proposed algorithm to detect and track a target at very low signal-to-noise ratios

A New Strategy for Identification of Highly Conserved microRNAs in Non-Model Insect, Spodoptera litura

The indigenous small non-coding RNAs, known as microRNAs (miRNAs), are important regulators of gene expression and many of them are evolutionarily conserved. Whether stem-loop RT-PCR, as a sensitive method, could be utilized to clone conserved miRNAs from non-model insects lacks information. Here, three miRNAs, sli-miR-14, sli-miR-2a and sli-bantam, were cloned from Spodoptera litura by stem-loop RT-PCR. Two groups of primers were designed, and one of them performed especially well and proved stable. The sequences of two highly conserved miRNAs, sli-miR-14 and sli-miR-2a were identical to those in Drosophila melanogaster. To validate the reliability of this strategy, pre-miR-14 and pre-miR-2a in S. litura as representatives were given as well; this shared high homology with those in D. melanogaster and Bombyx mori, and both mature sequences of sli-miR-14 and sli-miR-2a in their precursors shared 100% identity to the results shown by stem-loop RT-PCR. Moreover, expression patterns of these miRNAs were investigated by real-time quantitative PCR. Sli-miR-14 and sli-miR-2a could be detected successfully and their expression patterns showed similar characteristics with those in model insects, further suggesting stem-loop RT-PCR technology can be used for identification of highly conserved miRNAs in non-model insects. These results provide a simplified and efficient strategy for studying the structure and function of highly conserved miRNAs, especially some critical miRNAs in non-model insects

Detection and tracking of maneuvering targets on passive radar by Gaussian particles filtering

Cette thèse porte sur l'application des techniques de filtrage statistiques au radar passif. L'objectif de cette thèse est d'adapter les méthodes à somme de gaussiennes et les méthodes particulaires pour la détection et/ou la poursuite dans un contexte multi-cible. Nous nous intéressons aux problématiques liées à des cibles fortement manoeuvrantes à rapport signal sur bruit pouvant être très faible. En guise d'application, la radio FM et la télévision numérique DVB-T seront exploitées comme sources d'opportunité par le système de localisation passive. Dans un premier temps, cette thèse récapitule l'état de l'art dans le domaine du radar passif, du filtrage statistique et des approches conventionnelles de pistage radar à base de données seuillées. Dans un deuxième temps, cette thèse explore l'apport du filtrage particulaire en radar passif. Avec une modélisation convenable du problème de poursuite d'une cible sous la forme d'un système dynamique non-linéaire, nous montrons comment le filtrage particulaire, appliqué sur les sorties bruitées (non-seuillées) du corrélateur, améliore les performances en terme de poursuite par rapport aux approches conventionnelles. Une extension au cas multi-cible est également traitée. L'ingrédient essentiel de l'algorithme proposé est l'intégration d'un système de synchronisation de l'instant d'échantillonnage du corrélateur (et le cas échéant de la fréquence de corrélation) qui permet à l'algorithme particulaire de compenser automatiquement la dynamique des cibles. Dans un troisième temps, nous exposons un nouveau système de détection/poursuite multi cible basé sur le filtrage bayésien avec la méthodologie "track-before-detect". Ce système est implémenté par une approximation à base de somme de gaussiennes ou une approximation à base de filtrage particulaire. Nous proposons également une technique d'annulation successive d'interférence qui permet de gérer la présence de lobes secondaires importants. Des simulations utilisant un signal radio FM, ont permis de confirmer le potentiel du système de détection/poursuite proposé.The subject of this thesis is the application of statistical filtering techniques to passive radar. The objective of this thesis is to adapt Gaussian sum filtering and particle filtering methods to the detection and/or tracking in a multi-target context. Highly manoeuvring targets, at potentially very low signal-to-noise ratios, will be of particular interest. As an application, FM radio and terrestrial digital video broadcasting (DVB-T) will be exploited as illuminators of opportunity by the passive localization system. First, this thesis recapitulates the state-of-the-art in the domain of passive radar, statistical filtering and conventional radar tracking approaches based on the thresholded data. Second, this thesis explores the benefits of particle filtering in passive radar. With an appropriate modeling of the problem of target tracking as a non-linear dynamical system, we show how particle filtering, fed with the noisy unthresholded matched filter outputs, outperforms conventional tracking approaches. An extension to the multi-target case is also treated. The essential ingredient of the proposed algorithm is the inbuilt synchronization system of the correlator sampling instants (and potentially also of the correlation frequency), which allows the particle filter to compensate the dynamics of the targets automatically. Third, we present a new multi-target detection/tracking system, based on Bayesian filtering, using the track-before-detect methodology. This system is implemented with an approximation based on Gaussian sum filtering or an approximation based on particle filtering. We also propose a successive interference cancellation technique, which allows to handle the presence of large sidelobes. Simulations using FM radio confirmed the potential of the proposed detection/tracking system

Pistage de cibles manoeuvrantes en radar passif par filtrage à particules gaussiennes

The subject of this thesis is the application of statistical filtering techniques to passive radar. The objective of this thesis is to adapt Gaussian sum filtering and particle filtering methods to the detection and/or tracking in a multi-target context. Highly manoeuvring targets, at potentially very low signal-to-noise ratios, will be of particular interest. As an application, FM radio and terrestrial digital video broadcasting (DVB-T) will be exploited as illuminators of opportunity by the passive localization system. First, this thesis recapitulates the state-of-the-art in the domain of passive radar, statistical filtering and conventional radar tracking approaches based on the thresholded data. Second, this thesis explores the benefits of particle filtering in passive radar. With an appropriate modeling of the problem of target tracking as a non-linear dynamical system, we show how particle filtering, fed with the noisy unthresholded matched filter outputs, outperforms conventional tracking approaches. An extension to the multi-target case is also treated. The essential ingredient of the proposed algorithm is the inbuilt synchronization system of the correlator sampling instants (and potentially also of the correlation frequency), which allows the particle filter to compensate the dynamics of the targets automatically. Third, we present a new multi-target detection/tracking system, based on Bayesian filtering, using the track-before-detect methodology. This system is implemented with an approximation based on Gaussian sum filtering or an approximation based on particle filtering. We also propose a successive interference cancellation technique, which allows to handle the presence of large sidelobes. Simulations using FM radio confirmed the potential of the proposed detection/tracking system.Cette thèse porte sur l'application des techniques de filtrage statistiques au radar passif. L'objectif de cette thèse est d'adapter les méthodes à somme de gaussiennes et les méthodes particulaires pour la détection et/ou la poursuite dans un contexte multi-cible. Nous nous intéressons aux problématiques liées à des cibles fortement manoeuvrantes à rapport signal sur bruit pouvant être très faible. En guise d'application, la radio FM et la télévision numérique DVB-T seront exploitées comme sources d'opportunité par le système de localisation passive. Dans un premier temps, cette thèse récapitule l'état de l'art dans le domaine du radar passif, du filtrage statistique et des approches conventionnelles de pistage radar à base de données seuillées. Dans un deuxième temps, cette thèse explore l'apport du filtrage particulaire en radar passif. Avec une modélisation convenable du problème de poursuite d'une cible sous la forme d'un système dynamique non-linéaire, nous montrons comment le filtrage particulaire, appliqué sur les sorties bruitées (non-seuillées) du corrélateur, améliore les performances en terme de poursuite par rapport aux approches conventionnelles. Une extension au cas multi-cible est également traitée. L'ingrédient essentiel de l'algorithme proposé est l'intégration d'un système de synchronisation de l'instant d'échantillonnage du corrélateur (et le cas échéant de la fréquence de corrélation) qui permet à l'algorithme particulaire de compenser automatiquement la dynamique des cibles. Dans un troisième temps, nous exposons un nouveau système de détection/poursuite multi cible basé sur le filtrage bayésien avec la méthodologie "track-before-detect". Ce système est implémenté par une approximation à base de somme de gaussiennes ou une approximation à base de filtrage particulaire. Nous proposons également une technique d'annulation successive d'interférence qui permet de gérer la présence de lobes secondaires importants. Des simulations utilisant un signal radio FM, ont permis de confirmer le potentiel du système de détection/poursuite proposé

Edem1 activity in the fat body regulates insulin signalling and metabolic homeostasis in <i>Drosophila</i>

In Drosophila, nutrient status is sensed by the fat body, a functional homolog of mammalian liver and white adipocytes. The fat body conveys nutrient information to insulin-producing cells through humoral factors which regulate Drosophila insulin-like peptide levels and insulin signalling. Insulin signalling has pleiotropic functions, which include the management of growth and metabolic pathways. Here, we report that Edem1 (endoplasmic reticulum degradation–enhancing α-mannosidase–like protein 1), an endoplasmic reticulum–resident protein involved in protein quality control, acts in the fat body to regulate insulin signalling and thereby the metabolic status in Drosophila. Edem1 limits the fat body–derived Drosophila tumor necrosis factor-α Eiger activity on insulin-producing cells and maintains systemic insulin signalling in fed conditions. During food deprivation, edem1 gene expression levels drop, which aids in the reduction of systemic insulin signalling crucial for survival. Overall, we demonstrate that Edem1 plays a vital role in helping the organism to endure a fluctuating nutrient environment by managing insulin signalling and metabolic homeostasis.</jats:p

Edem1 activity in the fat body regulates insulin signalling and metabolic homeostasis in <i>Drosophila</i>

AbstractIn Drosophila, nutrient status is sensed by the fat body, a functional homolog of mammalian liver and white adipocytes. The fat body conveys nutrient information to insulin-producing cells (IPCs) through humoral factors which regulate Drosophila insulin-like peptide (DILP) levels and insulin signalling. Insulin signalling has pleiotropic functions, which include the management of growth and metabolic pathways. Here, we report that Edem1 (endoplasmic reticulum degradation-enhancing α-mannosidase-like protein 1), an endoplasmic reticulum-resident protein involved in protein quality control, acts in the fat body to regulate insulin signalling and thereby the metabolic status in Drosophila. Edem1 limits the fat body derived Drosophila TNFα Eiger activity on IPCs and maintains systemic insulin signalling in fed conditions. During food deprivation, edem1 gene expression levels drop, which aids in the reduction of systemic insulin signalling crucial for survival. Overall we demonstrate that Edem1 plays a vital role in helping the organism to endure a fluctuating nutrient environment by managing insulin signalling and metabolic homeostasis.</jats:p

Pistage de cibles manoeuvrantes en radar passif par filtrage à particules gaussiennes

Cette thèse porte sur l'application des techniques de filtrage statistiques au radar passif. L'objectif de cette thèse est d'adapter les méthodes à somme de gaussiennes et les méthodes particulaires pour la détection et/ou la poursuite dans un contexte multi-cible. Nous nous intéressons aux problématiques liées à des cibles fortement manoeuvrantes à rapport signal sur bruit pouvant être très faible. En guise d'application, la radio FM et la télévision numérique DVB-T seront exploitées comme sources d'opportunité par le système de localisation passive. Dans un premier temps, cette thèse récapitule l'état de l'art dans le domaine du radar passif, du filtrage statistique et des approches conventionnelles de pistage radar à base de données seuillées. Dans un deuxième temps, cette thèse explore l'apport du filtrage particulaire en radar passif. Avec une modélisation convenable du problème de poursuite d'une cible sous la forme d'un système dynamique non-linéaire, nous montrons comment le filtrage particulaire, appliqué sur les sorties bruitées (non-seuillées) du corrélateur, améliore les performances en terme de poursuite par rapport aux approches conventionnelles. Une extension au cas multi-cible est également traitée. L'ingrédient essentiel de l'algorithme proposé est l'intégration d'un système de synchronisation de l'instant d'échantillonnage du corrélateur (et le cas échéant de la fréquence de corrélation) qui permet à l'algorithme particulaire de compenser automatiquement la dynamique des cibles. Dans un troisième temps, nous exposons un nouveau système de détection/poursuite multi cible basé sur le filtrage bayésien avec la méthodologie "track-before-detect". Ce système est implémenté par une approximation à base de somme de gaussiennes ou une approximation à base de filtrage particulaire. Nous proposons également une technique d'annulation successive d'interférence qui permet de gérer la présence de lobes secondaires importants. Des simulations utilisant un signal radio FM, ont permis de confirmer le potentiel du système de détection/poursuite proposé.The subject of this thesis is the application of statistical filtering techniques to passive radar. The objective of this thesis is to adapt Gaussian sum filtering and particle filtering methods to the detection and/or tracking in a multi-target context. Highly manoeuvring targets, at potentially very low signal-to-noise ratios, will be of particular interest. As an application, FM radio and terrestrial digital video broadcasting (DVB-T) will be exploited as illuminators of opportunity by the passive localization system. First, this thesis recapitulates the state-of-the-art in the domain of passive radar, statistical filtering and conventional radar tracking approaches based on the thresholded data. Second, this thesis explores the benefits of particle filtering in passive radar. With an appropriate modeling of the problem of target tracking as a non-linear dynamical system, we show how particle filtering, fed with the noisy unthresholded matched filter outputs, outperforms conventional tracking approaches. An extension to the multi-target case is also treated. The essential ingredient of the proposed algorithm is the inbuilt synchronization system of the correlator sampling instants (and potentially also of the correlation frequency), which allows the particle filter to compensate the dynamics of the targets automatically. Third, we present a new multi-target detection/tracking system, based on Bayesian filtering, using the track-before-detect methodology. This system is implemented with an approximation based on Gaussian sum filtering or an approximation based on particle filtering. We also propose a successive interference cancellation technique, which allows to handle the presence of large sidelobes. Simulations using FM radio confirmed the potential of the proposed detection/tracking system.EVRY-INT (912282302) / SudocSudocFranceF

Larval nutrition influences adult fat stores and starvation resistance in Drosophila.

Insulin plays a major role in connecting nutrient availability to energy homeostasis by regulating metabolic pathways. Defects in insulin signalling is the primary cause for diabetes, obesity and various metabolic disorders. Nutritional status during growth and developmental stages play a crucial role in determining adult size, fecundity and ageing. However, the association between developmental nutrition and adult metabolic disorders has not been fully explored. Here, we address the effects of nutrient status during the larval growth phase on adult metabolism in Drosophila. We report that restricted food supply in larvae led to higher fat reserves and starvation resistance in mature adult flies, which we attribute to low insulin signalling. A lesser amount of stored fat was mobilised during early adult stages and during acute starvation, which accounts for the metabolic effects. Furthermore, larval diet influenced the expression of fat mobilisation genes brummer and lipid storage droplet-2 in adult flies, which led to the metabolic phenotypes reported here. Thus, the restricted nutrient environment in developing larvae led to adaptive changes that entrain the adult flies for scarce food availability