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To address the data security issue of distributed state estimation, this paper proposes a novel consensus-based cubature information filtering algorithm for sensitive nonlinear target tracking under restricted communication. Through a privacy-preserving approach via state decomposition, the algorithm can protect the privacy of local information from adversaries without sacrificing global estimation accuracy. Based on the push-sum consensus, the distributed approach is further extended to switching directed topologies, which is more feasible for tracking with communication constraints. Our method's average convergence, privacy preservation, and stability are theoretically proved. Finally, simulations are conducted to evaluate the algorithm's performance on security, accuracy, and robustness. • A distributed filter is developed for secure tracking with communication constraints. • The algorithm preserves each node's privacy without sacrificing estimation accuracy. • The algorithm's average convergence, privacy preservation, and stability are proved. [ABSTRACT FROM AUTHOR]

Acoustic vector sensor (AVS) is an effective tool to tracking acoustic sources. However, for the problem of tracking multiple wideband sources using distributed AVS array (DAVS), there are still unsolved issues which include measurements-to-targets association and targets tracking under incorrect or unknown statistics of measurement noise. Joint probabilistic data association (JPDA) is an effective algorithm to solve data association between measurements and targets and JPDA based cubature information filter (MTCIF) is designed for nonlinear system. Meanwhile, noise statistics estimator (NSE) based on modified Sage-Husa maximum posterior (SHMP) is constructed to cope with incorrect or unknown statistics of measurement noise. Then, a two-step distributed information fusion based on weighted average consensus (WAC) is built for DAVS to improve the stability and accuracy of state estimator and NSE. Numerical simulations demonstrate the effectiveness of the proposed algorithms.

To settle the problem of distributed nonlinear state estimation in sensor networks with naive nodes, a novel distributed high-degree cubature information filter with embedded hybrid consensus (DHCIF) is proposed. The multi-dimensional Gaussian weighted integrals involved in the filtering process are approximated by the fifth-degree cubature rule. A novel scheme, with consideration for the predicted measurement errors, is applied to compute the information contribution. With parallel consensus performed on both prior and measurement information, the proposed DHCIF is derived. The stability analysis with regard to consistency and boundedness of estimation errors for the proposed DHCIF is also developed. Finally, the effectiveness and advantage of the proposed DHCIF is validated by a typical maneuvering target tracing scenario. The experimental results indicate that the proposed DHCIF outperforms the existing algorithms in the aspects of estimation accuracy, consistency and consensus at the cost of a little more extra computation burden.

In multi-extended target tracking, each target may generate more than one observation. The traditional probability hypothesis density (PHD)-based methods are no longer effective in such scenarios. Recently, the Gaussian mixture PHD approach for the extended target tracking (ET-GM-PHD) has been presented to solve such a problem. The tracking performance of this approach has been restricted by the following disadvantages. First, it only focuses on the linear models. When targets are moving with nonlinear models, it may lead to great tracking errors. Second, the birth intensities are commonly assumed as a prior. In practice, these intensities are always unknown. In order to improve the tracking performance of the traditional ET-GM-PHD approach, a novel extended target tracking approach, namely the ET-cubature information GM-PHD (ET-CIGM-PHD) approach, has been proposed in this paper. To be more specific, we, first, utilize the cubature information filter (CIF) and gating methods to predict and update the Gaussian mixture components of the ET-GM-PHD approach. In the merit of high estimating accuracy of the CIF method, the tracking accuracy of the traditional ET-GM-PHD approach can be significantly improved. Due to the gating method, only part of cells can be used to construct the observation set in the update stage. Thus, the computational load of our approach can be significantly reduced. Then, we propose an adaptive initiating method for the birth intensity initiating. In our method, we utilize the estimated target set to select the most possible partition. Then, we remove cells associated with the estimated targets from the selected partition. The left cells are used to initiate the birth intensity. With the help of the above implementations, the birth intensity can be adaptively initiated. Using such a method, our approach can solve the cases that the prior information of birth intensity is rather little. The simulation results demonstrate the effectiveness of our approach.

Kalman filter (KF) is a widely used technique to obtain health condition in aero engine health management, and each kind of measurement is commonly assumed to be collected and tackled simultaneously from one sensor in the KF for state tracking in previous studies. However, there are redundant sensor measurements of the same kind employed with different sampling and transmission rate, and it is hard to achieve state estimation using multiple sensors at one time, especially, in the advanced distributed architecture. For these purposes, a multi-sensor asynchronous fusion approach is proposed to track engine health state using distributed cubature information filter (CIF) in this paper. The sequential and distributed integration structures are designed for asynchronous computation, and the square-root cubature rule are combined with the CIF to complete state estimation. The global state estimate of distributed square-root CIF (DSCIF) is obtained from the fusion of local estimates using multi-scale sampling strategy. The performance comparisons of the examined methodologies are conducted to state estimation with asynchronous measurements in aero engine gas path health tracking applications. The results illustrate the superiority of the DSCIF with regards to estimation accuracy and computational efforts, and confirm our viewpoints in the asynchronous filtering estimation for the multi-sensor system.

• This paper studies the distributed fusion estimation problem for nonlinear systems. • A modified progressive cubature information filter (PCIF) is proposed to reduce the impact of linearization errors. • The proposed PCIF method is employed as local estimators for nonlinear distributed fusion systems. • The estimation errors in the progressive process is proved to be bounded by stability analysis. This paper studies the distributed fusion estimation problem for nonlinear systems. A common method for nonlinear filtering is to linearize the nonlinear state and measurement functions, and the linearization error is a major factor that affects the filtering accuracy. To reduce the impact of linearization error on the distributed fusion, a progressive information filter (PIF) is designed as local estimators by improving the working points of Taylor series expansion, thus the distributed fusion results of linear systems can be extended to nonlinear systems. Moreover, by introducing a termination condition for the progressive measurement update, the designed PIF can avoid over-estimation effectively. Finally, simulations of a target tracking example are presented to demonstrate the advantage and effectiveness of the proposed results. [ABSTRACT FROM AUTHOR]

Visual–inertial odometry is an effective system for mobile robot navigation. This article presents an egomotion estimation method for a dual-sensor system consisting of a camera and an inertial measurement unit (IMU) based on the cubature information filter and H∞ filter. The intensity of the image was used as the measurement directly. The measurements from the two sensors were fused with a hybrid information filter in a tightly coupled way. The hybrid filter used the third-degree spherical-radial cubature rule in the time-update phase and the fifth-degree spherical simplex-radial cubature rule in the measurement-update phase for numerical stability. The robust H∞ filter was combined into the measurement-update phase of the cubature information filter framework for robustness toward non-Gaussian noises in the intensity measurements. The algorithm was evaluated on a common public dataset and compared to other visual navigation systems in terms of absolute and relative accuracy. [ABSTRACT FROM AUTHOR]

This paper is concerned with the state estimation of the jump Markov system with the unknown measurement noise. The proposed algorithm is derived under the framework of Interacting Multiple Model approach, and the recently reported Kullback-Leibler (KL) divergence-based scheme is used for estimation fusion. To facilitate KL divergence-based scheme, the information state and Wishart distribution are, respectively, used to describe the state and the unknown precision matrix of the measurement noise. Specifically, at both the mixing and estimation fusion stages, the KL divergence-based fusion scheme is adopted to fuse the information matrices, information state vectors, and parameters of the Wishart distribution from all the modes. At mode-conditioned filtering stage, parallel noise adaptive cubature information filters are designed to recursively estimate the information states, information matrices, and the Wishart distributed noise parameters. Simulation results prove the efficacy of the proposed approach. [ABSTRACT FROM AUTHOR]

Visual–inertial odometry is an effective system for mobile robot navigation. This article presents an egomotion estimation method for a dual-sensor system consisting of a camera and an inertial measurement unit (IMU) based on the cubature information filter and H∞ filter. The intensity of the image was used as the measurement directly. The measurements from the two sensors were fused with a hybrid information filter in a tightly coupled way. The hybrid filter used the third-degree spherical-radial cubature rule in the time-update phase and the fifth-degree spherical simplex-radial cubature rule in the measurement-update phase for numerical stability. The robust H∞ filter was combined into the measurement-update phase of the cubature information filter framework for robustness toward non-Gaussian noises in the intensity measurements. The algorithm was evaluated on a common public dataset and compared to other visual navigation systems in terms of absolute and relative accuracy.

Standard Bayesian filtering algorithms only work well when the statistical properties of system noises are exactly known. However, this assumption is not always plausible in real target tracking applications. In this paper, we present a new estimation approach named adaptive fifth-degree cubature information filter (AFCIF) for multi-sensor bearings-only tracking (BOT) under the condition that the process noise follows zero-mean Gaussian distribution with unknown covariance. The novel algorithm is based on the fifth-degree cubature Kalman filter and it is constructed within the information filtering framework. With a sensor selection strategy developed using observability theory and a recursive process noise covariance estimation procedure derived using the covariance matching principle, the proposed filtering algorithm demonstrates better estimation accuracy and filtering stability. Simulation results validate the superiority of the AFCIF.

In multi-target tracking, the key problem lies in estimating the number and states of individual targets, in which the challenge is the time-varying multi-target numbers and states. Recently, several multi-target tracking approaches, based on the sequential Monte Carlo probability hypothesis density (SMC-PHD) filter, have been presented to solve such a problem. However, most of these approaches select the transition density as the importance sampling (IS) function, which is inefficient in a nonlinear scenario. To enhance the performance of the conventional SMC-PHD filter, we propose in this paper two approaches using the cubature information filter (CIF) for multi-target tracking. More specifically, we first apply the posterior intensity as the IS function. Then, we propose to utilize the CIF algorithm with a gating method to calculate the IS function, namely CISMC-PHD approach. Meanwhile, a fast implementation of the CISMC-PHD approach is proposed, which clusters the particles into several groups according to the Gaussian mixture components. With the constructed components, the IS function is approximated instead of particles. As a result, the computational complexity of the CISMC-PHD approach can be significantly reduced. The simulation results demonstrate the effectiveness of our approaches. [ABSTRACT FROM AUTHOR]

Standard Bayesian filtering algorithms only work well when the statistical properties of system noises are exactly known. However, this assumption is not always plausible in real target tracking applications. In this paper, we present a new estimation approach named adaptive fifth-degree cubature information filter (AFCIF) for multi-sensor bearings-only tracking (BOT) under the condition that the process noise follows zero-mean Gaussian distribution with unknown covariance. The novel algorithm is based on the fifth-degree cubature Kalman filter and it is constructed within the information filtering framework. With a sensor selection strategy developed using observability theory and a recursive process noise covariance estimation procedure derived using the covariance matching principle, the proposed filtering algorithm demonstrates better estimation accuracy and filtering stability. Simulation results validate the superiority of the AFCIF. [ABSTRACT FROM AUTHOR]

In multi-target tracking, the key problem lies in estimating the number and states of individual targets, in which the challenge is the time-varying multi-target numbers and states. Recently, several multi-target tracking approaches, based on the sequential Monte Carlo probability hypothesis density (SMC-PHD) filter, have been presented to solve such a problem. However, most of these approaches select the transition density as the importance sampling (IS) function, which is inefficient in a nonlinear scenario. To enhance the performance of the conventional SMC-PHD filter, we propose in this paper two approaches using the cubature information filter (CIF) for multi-target tracking. More specifically, we first apply the posterior intensity as the IS function. Then, we propose to utilize the CIF algorithm with a gating method to calculate the IS function, namely CISMC-PHD approach. Meanwhile, a fast implementation of the CISMC-PHD approach is proposed, which clusters the particles into several groups according to the Gaussian mixture components. With the constructed components, the IS function is approximated instead of particles. As a result, the computational complexity of the CISMC-PHD approach can be significantly reduced. The simulation results demonstrate the effectiveness of our approaches.