Local Filter Research Articles

Fully Distributed Filtering With a Stochastic Event-Triggered Mechanism

This article focuses on distributed filtering for a discrete time-varying system observed by multiple smart sensors, where every sensor only measures partial state information of the target system and then sends it to a corresponding remote estimator. Subsequently, the estimator performs the local Kalman filter and shares its estimates with the estimators in its neighborhood in a distributed way. This article aims to reduce the communication rate between sensors and estimators, and guarantee the estimation performance, simultaneously. To achieve this goal, a novel distributed information fusion algorithm is designed by embedding a stochastic event-triggered communication mechanism. Based on a new developed mathematics technique, the consistency and stability of the proposed distributed state estimation algorithms are both ensured. Furthermore, compared with the literature, the stability can be guaranteed with a milder collectively uniformly observable condition. Moreover, the tradeoff between the communication rate and estimation performance is analyzed in a closed-form expression. Finally, the effectiveness of the theoretical results is demonstrated by several comparative numerical examples.

Variance-Constrained Filtering Fusion for Nonlinear Cyber-Physical Systems With the Denial-of-Service Attacks and Stochastic Communication Protocol

In this paper, a new filtering fusion problem is studied for nonlinear cyber-physical systems under error-variance constraints and denial-of-service attacks. To prevent data collision and reduce communication cost, the stochastic communication protocol is adopted in the sensor-to-filter channels to regulate the transmission order of sensors. Each sensor is allowed to enter the network according to the transmission priority decided by a set of independent and identically-distributed random variables. From the defenders' view, the occurrence of the denial-of-service attack is governed by the randomly Bernoulli-distributed sequence. At the local filtering stage, a set of variance-constrained local filters are designed where the upper bounds (on the filtering error covariances) are first acquired and later minimized by appropriately designing filter parameters. At the fusion stage, all local estimates and error covariances are combined to develop a variance-constrained fusion estimator under the federated fusion rule. Furthermore, the performance of the fusion estimator is examined by studying the boundedness of the fused error covariance. A simulation example is finally presented to demonstrate the effectiveness of the proposed fusion estimator.

IMPROVING LOCAL ADAPTIVE FILTERING METHOD EMPLOYED IN RADIOMETRIC CORRECTION OF ANALOGUE AIRBORNE CAMPAIGNS

Abstract. An orthophotomosaic is as a single image that can be layered on a map. It is produced from a set of aerial images impaired by radiometric inhomogeneity mostly due to atmospheric phenomena, like hotspot, haze or high altitude clouds shadows as well as the camera itself, like lens vignetting. These create some unsightly radiometric inhomogeneity in the mosaic that could be corrected by using a local adaptive filter, also named Wallis filter. Yet this solution leads to a significant loss of contrast at small scales. This current work introduces two elementary studies. In a first time, in order to quantify the loss of contrast due to the use of Wallis filter, a simple multi-scale score is proposed based on mathematical morphology operations. In a second time, an optimal window size for the filter is identified by considering some systematic radiometric behaviours in the images forming the mosaic through Principal Component Analysis (PCA). These two elementary studies are preliminary steps leading to a method of radiometric correction combining Wallis filtering and PCA.

The roles of functional traits in canopy maintenance along a savanna/seasonally dry tropical forest gradient in northeastern Brazil

Vegetation gradients, such as those between savanna and seasonally dry tropical forest (SDTF) vegetations, may experience nearly identical macroclimatic conditions but still differ because of local ecological filters selecting for distinct plant functional aspects related to water storage and use. We examined how leaf phenology, water potential, wood density, and wood saturated water content varied seasonally along a savanna, transition, and SDTF vegetation gradient at the eastern border of the Chapada Diamantina Highlands, Brazil. We monitored the leaf phenologies of 523 individuals of 48 woody species (20 savanna, 14 transition, and 14 SDTF species) for two years. We identified four phenological groups: brevideciduous, deciduous, evergreen (EG) plants having continuous growth, and evergreen plants having seasonal growth. Deciduous species were found throughout the gradient, while EG species accounted for more than 80% of relative density in savanna areas. Precipitation was negatively related to leaf fall for all phenological groups, and positively related for leaf flushing in deciduous species. More than 80% of all species exhibited wood densities between 0.50 and 0.91 g cm−3. The first principal component explained 77.64% of the observed variance, associated with wood saturated water content, water potential, and wood density. All of the savanna species were distributed along the positive axis of the principal component analysis as compared to SDTF species. We demonstrated that the effects of water limitations along the gradient were critical to the selection of functional traits associated with water-use strategies and expressive deciduousness in SDTF vegetation, interpreted as drought avoidance strategies.

Numerical experiments with real data for estimating greenhouse gas fluxes in a region

This work is devoted to the problem of obtaining an estimate of methane emissions using satellite data and the results of mathematical modeling. To implement the algorithm, a variant of the local Kalman ensemble filter (LETKF) is used, which represents an optimal estimate of the desired parameter based on observational data and a forecast based on a metematic model in a given time interval. This algorithm has properties that allow it to be used locally, i.e., to assimilate data by subdomains. The paper presents the implementation of the algorithm for real observational data and the results of mathematical modeling (calculation of the forecast of the state of the system). The results of the three-dimensional model of transport and diffusion (MOZART-4) are taken as the results of mathematical modeling, and satellite observations (AIRS data) are used as observational data. As a result of the algorithm, an average estimate of methane fluxes in the subdomain was obtained at specified time intervals.

Neural-Network-Based Filtering for A General Class of Nonlinear Systems under Dynamically Bounded Innovations Over Sensor Networks

In this paper, the design problem of distributed filters is studied for nonlinear systems over sensor networks. At each sensor node, filters are designed by making use of both local measurements and innovation information sent from the neighboring nodes via interaction network. To mitigate adverse effects from abnormal data during transmissions, in the constructed local filters, a mechanism is proposed which utilizes a saturation function to constrain the propagated innovations within a dynamically changeable bound. A neural-network-based algorithm is used for the approximation of the nonlinear dynamics, where the weight matrix is co-designed with the filter gains. By resorting to certain convex optimization techniques, sufficient conditions are determined with respect to the solvability of our addressed problem, which ensure that 1) the estimation errors at each sensor node are confined within a pre-specified ellipsoidal region; and 2) the finite-horizon <inline-formula><tex-math notation="LaTeX">$H_{\infty }$</tex-math></inline-formula> performance specification is achieved. Moreover, within the established framework, an optimal problem is established to determine a locally optimal filter gain. Finally, a simulation example is given to demonstrate the correctness of the proposed filtering algorithm.

Fault-tolerant SINS/HSB/DVL underwater integrated navigation system based on variational Bayesian robust adaptive Kalman filter and adaptive information sharing factor

To solve the problem that position information obtained by SINS/DVL integrated navigation system is divergent in the underwater navigation applications, this paper introduces hydroacoustic single beacon (HSB) into the underwater integrated system and proposes a novel SINS/HSB/DVL fault-tolerant federated variational Bayesian robust adaptive Kalman filter (FVBRAKF). In the FVBRAKF, the traditional KF is replaced by VBRAKF, which not only suppress the adverse influence of outliers based on Mahalanobis distance (MD) effectively, but also estimate the unknown measurement noise covariance based on variational Bayesian (VB) approximation adaptively. Meanwhile, a novel adaptive information sharing factor (ISF) method is proposed during the information fusion process to form an improved FVBRAKF（IFVBRAKF）, which can adjust the fusion weights in real-time according to the accuracy of local filter. The semi-physical simulation experiments for SINS/HSB/DVL underwater integrated navigation system based on the test data are carried out to verify the adaptive ability of the ISF and the robust adaptive ability of the method, respectively. Experimental results demonstrate that the proposed algorithm can not only improve the estimation accuracy, but also enhance the fault-tolerant performance.

Distributed resilient fusion filtering for nonlinear systems with random sensor delay under round-robin protocol

In this paper, we address the distributed resilient fusion filtering (DRFF) problem for a class of nonlinear multi-sensor networked systems (MSNSs) with random sensor delay (RSD) under round-robin protocol (RRP). The RSD is depicted by the Bernoulli random variable with known occurrence probability. In order to relieve the network congestion, the RRP that can deal with information overload issue of the transmission process from sensor to the estimator is utilised. The major objective of this paper is that the resilient fusion filter is designed for nonlinear MSNSs with RSD and RRP in the light of the inverse covariance intersection approach, where the local upper bound regarding the filtering error covariance is obtained and then minimised by suitably exploiting the local filter gain. Finally, a simulation example that can show the validity of the provided DRFF algorithm is presented.

Iris recognition based on local circular Gabor filters and multi-scale convolution feature fusion network

Iris recognition based on local circular Gabor filters and multi-scale convolution feature fusion network

Energy-Efficient Diffusion Kalman Filtering for Multiagent Networks in IoT

Increasing the energy efficiency of an Internet of Things (IoT) system is a major challenge for its successful implementation. To reduce the computation and storage burden and enhance the efficiency of traditional IoT, an energy-efficient diffusion-based algorithm for state estimation in multiagent networks is proposed in this article. In the proposed algorithm [referred to as the reduced-link diffusion Kalman filter (RL-diffKF)] the nodes (agents) can communicate only with a fraction of their neighbors and each node runs a local Kalman filter to estimate the state of a linear dynamic system. This algorithm results in a significant reduction in communication cost during both adaptation and aggregation processes albeit at the expense of possible degradation in the network performance. To justify the stability and convergence of the RL- diffKF algorithm, an in-depth analysis of the performance is reported. We also consider the problem of optimal selection of combination weights and use the idea of minimum variance estimation to analytically derive the adaptive combiners. The theoretical findings are verified through numerical simulations.

Reduced‐order multisensory fusion estimation with application to object tracking

This paper investigates the track-to-track state estimation for a class of linear time-varying multisensory systems. We propose a novel low-complexity reduced-order filter (ROF) under the Kalman filtering framework. Unlike the majority of previous track-to-track strategies, the proposed fusion strategy applies only to special variables or required components that contain critical information about a target system of interest. Also, unlike existing suboptimal fusion filters such as the covariance intersection, the proposed ROF algorithm makes use of nonzero cross-covariances between local filters that greatly improve its estimation accuracy. The theoretical aspect of ROF application to multisensory systems with identical sensors is also thoroughly investigated. Finally, we show the effectiveness and accuracy of the ROF when applied to objects (including a drone) performing a two-dimensional maneuver using numerical simulations.

Environmental drivers of taxonomic and functional diversity of dung beetles across a chronosequence of tropical grasslands with different cattle grazing removal ages

AbstractLivestock is a globally widespread farming practice, with benefits and harms for biodiversity. Biodiversity responses to vegetation and soil conditions associated with cattle grazing removal are poorly understood in tropical grassy ecosystems, especially in a long‐term chronosequence. In this study, we aimed to identify the main drivers of local conditions on dung beetle taxonomic and functional diversity across a chronosequence of natural grasslands with different cattle grazing removal ages. We expect that vegetation and soil variables associated with environmental changes caused by longer time after cattle grazing removal will drive the taxonomic and functional diversity of dung beetles. We sampled dung beetles and recorded local environmental filters (vegetation and soil variables) in 14 natural grasslands with distinct cattle grazing removal ages (from 3 months to 22 years) and also in 10 reference sites (with cattle grazing). We used structural equation models to evaluate the relationships between the most informative explanations (vegetation and soil variables) of dung beetle taxonomic and functional metrics. Our results provide evidences that local conditions related to vegetation and soil variables in natural grasslands affect the taxonomic and functional diversity of dung beetles differently. Soil conditions (compaction and silt content) had more influence, mostly negative, on taxonomic metrics, while vegetation complexity had more influence on functional metrics, with positive or negative effects depending on the functional metric evaluated. Understanding the temporal change in vegetation and soil conditions can shed light on the process of recovering biodiversity and ecosystem functions in areas with different times of abandonment of livestock farming. Finally, using taxonomic and functional approaches together can also help to better understand how organisms are responding to the process of abandoning livestock farming over time.

Inertial Navigation System Based Vehicle Temporal Relative Localization With Split Covariance Intersection Filter

In autonomous vehicle navigation, it usually involves a basic process of estimating the vehicle pose at one instant relative to the vehicle pose at a previous instant, and we refer to this process as temporal relative localization (TRL). Accurate and reliable TRL is valuable for vehicle localization and mapping which plays a fundamental role in autonomous vehicle navigation. A common way for realizing TRL is dead reckoning based on motion sensor such as inertial navigation system (INS). To augment the performance of INS based TRL, a nine-degree-of-freedom (9-DOF) vehicle dynamics model (VDM) is proposed as the system model for the filtering process of TRL; it can effectively characterize vehicle pitch and roll motion, especially in slope road scenarios. For better fault tolerance, a distributed fusion framework which consists of a master filter and two local filters for multi-source data fusion is introduced. The split covariance intersection filter (Split CIF) is applied to handle unknown temporal and spatial correlation that inevitably exists in such distributed fusion mechanism. A comparative experimental study demonstrates the advantage of the proposed method in terms of accuracy and robustness.

Decentralised H ∞ filtering of interconnected discrete-time systems

This paper studies the decentralised filtering problem for interconnected discrete-time systems. In the developed decentralised filter scheme, only local output information is available for each local filter. The dynamics of the original system and the dynamics of the proposed filter are expressed as an augmented system. The system matrices of the augmented system consist of matrix inversion terms, which are computationally expensive and even meet numerical stability problems. In order to avoid the matrix inversion computation, this paper derives novel computationally attractive sufficient conditions without inversion terms to guarantee the performance of the augmented system. Then, these conditions are transformed into linear matrix inequalities. The decentralised filter parameters can be obtained by solving linear matrix inequalities. Finally, two simulation examples are given to demonstrate the effectiveness and the advantages of the proposed approach.

Underwater image enhancement by color correction and color constancy via Retinex for detail preserving

In underwater, light attenuation causes non-uniform illumination that degrades underwater image. To enhance the degraded image, we propose an underwater image enhancement method that includes color correction, color constancy, multi-scale retinex (MSR), edge-aware and texture smoothing filters, and contrast stretching. The formulated color correction compensates the red and blue channels by masking. Color constancy transforms non-uniform illumination into uniform illumination. Furthermore, the gamma correction in the color constancy improves the contrast of the image. The distorted colors are restored using the MSR with contrast limited adaptive histogram equalization (CLAHE). The edges are preserved using a fast local laplacian filter (FLLF), while the detailed texture of the image is smoothed using a rolling guidance filter (RGF) The contrast stretching enhances the image by stretching the pixel values. The qualitative and quantitative evaluation of the proposed algorithm outperforms the existing methods in terms of naturalness, contrast, visibility and enhancement of low-light images.

Anthropogenic landscape alteration promotes higher disease risk in wild New Zealand avian communities.

Anthropogenic changes can have dramatic effects on wild populations. Moreover, by promoting the emergence of vector-borne diseases in many ecosystems, those changes can lead to local extinction of native wildlife. One of those diseases, avian malaria, has been shown to be on the rise in New Zealand, threatening native bird species that are among the most extinction-prone in the world. It is thus of prime importance to better understand the potential cascading effects that anthropogenic modifications have on those fragile species. Here, we aim to test how long-lasting modification to regional environmental filters can subsequently alter local biotic filters, in turn promoting the emergence of avian malaria in New Zealand avian communities. To this end, we used Bayesian structural equation modelling to unravel the drivers of disease emergence within the complex interplay between landscape and local species pools. We show that altered landscape, quantified through a lower enhanced vegetation index, leads to more infections in Turdus spp. and modification in avian community composition, potentially raising the probability of infection for other species in the community. In addition, we show that climatic variables associated with the presence of vectors play a predominant role in shaping the regional pattern of avian malaria occurrence. Our results suggest long-lasting impacts of anthropogenic changes on regional environmental filters and demonstrate that conservation efforts should align toward restoring the landscape to prevent further emergence of infectious diseases in wild ecosystems.

VIPDA: A Visually Driven Point Cloud Denoising Algorithm Based on Anisotropic Point Cloud Filtering

Point clouds (PCs) provide fundamental tools for digital representation of 3D surfaces, which have a growing interest in recent applications, such as e-health or autonomous means of transport. However, the estimation of 3D coordinates on the surface as well as the signal defined on the surface points (vertices) is affected by noise. The presence of perturbations can jeopardize the application of PCs in real scenarios. Here, we propose a novel visually driven point cloud denoising algorithm (VIPDA) inspired by visually driven filtering approaches. VIPDA leverages recent results on local harmonic angular filters extending image processing tools to the PC domain. In more detail, the VIPDA method applies a harmonic angular analysis of the PC shape so as to associate each vertex of the PC to suit a set of neighbors and to drive the denoising in accordance with the local PC variability. The performance of VIPDA is assessed by numerical simulations on synthetic and real data corrupted by Gaussian noise. We also compare our results with state-of-the-art methods, and we verify that VIPDA outperforms the others in terms of the signal-to-noise ratio (SNR). We demonstrate that our method has strong potential in denoising the point clouds by leveraging a visually driven approach to the analysis of 3D surfaces.

Sargasso Sea bacterioplankton community structure and drivers of variance as revealed by DNA metabarcoding analysis.

Marine microbes provide the backbone for pelagic ecosystems by cycling and fixing nutrients and establishing the base of food webs. Microbial communities are often assumed to be highly connected and genetically mixed, with localized environmental filters driving minor changes in structure. Our study applied high-throughput Illumina 16S ribosomal RNA gene amplicon sequencing on whole-community bacterial samples to characterize geographic, environmental, and stochastic drivers of community diversity. DNA was extracted from seawater collected from the surface (N = 18) and at depth just below the deep chlorophyll-a maximum (DCM mean depth = 115.4 m; N = 22) in the Sargasso Sea and adjacent oceanographic regions. Discrete bacterioplankton assemblages were observed at varying depths in the North Sargasso Sea, with a signal for distance-decay of bacterioplankton community similarity found only in surface waters. Bacterial communities from different oceanic regions could be distinguished statistically but exhibited a low magnitude of divergence. Redundancy analysis identified temperature as the key environmental variable correlated with community structuring. The effect of dispersal limitation was weak, while variation partitioning and neutral community modeling demonstrated stochastic processes influencing the communities. This study advances understanding of microbial biogeography in the pelagic ocean and highlights the use of high-throughput sequencing methods in studying microbial community structure.

A Tracking Algorithm for Sparse and Dynamic Underwater Sensor Networks

An underwater sensor network (UWSN) has sparse and dynamic characteristics. In sparse and dynamic UWSNs, the traditional particle filter based on multi-rate consensus/fusion (CF/DPF) has the problems of a slow convergence rate and low filtering accuracy. To solve these problems, a tracking algorithm for sparse and dynamic UWSNs based on particle filter (TASD) is proposed. Firstly, the estimation results of a local particle filter are processed by a weighted average consensus filter (WACF). In this way, the reliability difference of state estimation between nodes in sparse and dynamic UWSN is reasonably eliminated. Secondly, a delayed update mechanism (DUM) is added to WACF, which effectively solves the problem of time synchronization between the two particle filters. Thirdly, under the condition of limited communication energy consumption, an alternating random scheme (ARS) is designed, which optimizes the mean square convergence rate of the fusion particle filter. Simulation results show that the proposed algorithm can be applied to maneuvering target tracking in sparse and dynamic UWSN effectively. Compared with the traditional method, it has higher tracking accuracy and faster convergence speed. The average estimation error of TASD is 91.3% lower than that of CF/DPF, and the weighted consensus tracking error of TASD is reduced by 85.6% compared with CF/DPF.

Prospective validation of urine based FGFR screening by Uromonitor within the real-world clinicopathological register trial BRIDGister.

471 Background: The objective of the present study was to prospectively evaluate FGFR mutation detetion in matched urine and tissue samples from patients suspicious of bladder cancer and undergoing first TURB within the framework of the BRIDGister RealWorld Experience trial. Methods: For this pilot study paraffin fixed pretreatment tissue samples from the first TURB of 48 pts participating in the BRIDGister trial and matched urine samples were prospectively collected and analyzed. RNA from FFPE tissues were extracted by commercial kits and analyzed by Therascreen FGFR IVD kit (Qiagen GmbH, Hilden). In addition urine samples were filtered at local urology and filters were shipped for central extraction of cellular DNA (Uromonitor, Porto). Concordance, Kruskal-Wallis, MannWhitney and Sensitivity/Specificity tests were analyzed by JMP 9.0.0 (SAS software). Results: The pilote cohort of the BRIDGister trial consisted of 47 patients (median age: 77, male 65% vs female 35%) of diverse clinical stages (benign lesions/no tumor 38%, pTa 23%, pT1 20%, pT2 19%) and WHO 1973 grade (G1 11%, G2 43%, G3 23%). Based on FFPE tissue testing using Therascreen FGFR IVD kit 10 out of 47 patients exhibited FGFR alterations (25%), while urine filtering for cellular components and subsequent PCR testing revealed 13 out of 40 matched urine sampels were FGFR positive (33%). Comparison with tissue testing as probable gold standard revealed 100% sensitivity, 90% specificity, 77% PPV, 100% NPV as well as high concordance (kappa 0,82, p &lt; 0,0001). There were 3 patients being FGFR positive for Uromonitor from urine with no mutation found in the corresponding TUR biopsy. Conclusions: Filtering urine for cells and subsequent DNA extraction followed by PCR detection results in highly sensitive mutation testing being feasible with good concordance to matched tissue testing. Prospective testing validated the diagnostic accuracy of the Uromonitor FGFR test in a real world setting.