Distributed estimation over a low-cost sensor network: a review of state-of-the-art

Proliferation of low-cost, lightweight, and power efficient sensors and advances in networked systems enable the employment of multiple sensors. Distributed estimation provides a scalable and fault-robust fusion framework with a peer-to-peer communication architecture. For this reason, there seems to be a real need for a critical review of existing and, more importantly, recent advances in the domain of distributed estimation over a low-cost sensor network. This paper presents a comprehensive review of the state-of-the-art solutions in this research area, exploring their characteristics, advantages, and challenging issues. Additionally, several open problems and future avenues of research are highlighted

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Finite Impulse Response Filtering Algorithm with Adaptive Horizon Size Selection and Its Applications

It is known, that unlike the Kalman filter (KF) finite impulse response (FIR) filters allow to avoid the divergence and unsatisfactory object tracking connected with temporary perturbations and abrupt object changes. The main challenge is to provide the appropriate choice of a sliding window size for them. In this paper, the new finite impulse response (FIR) filtering algorithm with the adaptive horizon size selection is proposed. The algorithm uses the receding horizon optimal (RHOFIR) filter which receives estimates, an abrupt change detector and an adaptive recurrent mechanism for choosing the window size. Monotonicity and asymptotic properties of the estimation error covariance matrix and the RHOFIR filter gain are established. These results form a solid foundation for justifying the principal possibility to tune the filter gain using them and the developed adaptation mechanism. The proposed algorithm (the ARHOFIR filter) allows reducing the impact of disturbances by varying adaptively the sliding window size. The possibility of this follows from the fact that the window size affects the filter characteristics in different ways. The ARHOFIR filter chooses a large horizon size in the absence of abrupt disturbances and a little during the time intervals of their action. Due to this, it has better transient characteristics compared to the KF and RHOFIR filter at intervals where there is temporary uncertainty and may provide the same accuracy of estimates as the KF in their absence. By simulation, it is shown that the ARHOFIR filter is more robust than the KF and RHOFIR filter for the temporarily uncertain systems

Secure Distributed Dynamic State Estimation in Wide-Area Smart Grids

Smart grid is a large complex network with a myriad of vulnerabilities, usually operated in adversarial settings and regulated based on estimated system states. In this study, we propose a novel highly secure distributed dynamic state estimation mechanism for wide-area (multi-area) smart grids, composed of geographically separated subregions, each supervised by a local control center. We firstly propose a distributed state estimator assuming regular system operation, that achieves near-optimal performance based on the local Kalman filters and with the exchange of necessary information between local centers. To enhance the security, we further propose to (i) protect the network database and the network communication channels against attacks and data manipulations via a blockchain (BC)-based system design, where the BC operates on the peer-to-peer network of local centers, (ii) locally detect the measurement anomalies in real-time to eliminate their effects on the state estimation process, and (iii) detect misbehaving (hacked/faulty) local centers in real-time via a distributed trust management scheme over the network. We provide theoretical guarantees regarding the false alarm rates of the proposed detection schemes, where the false alarms can be easily controlled. Numerical studies illustrate that the proposed mechanism offers reliable state estimation under regular system operation, timely and accurate detection of anomalies, and good state recovery performance in case of anomalies

Approximate Gaussian conjugacy: parametric recursive filtering under nonlinearity, multimodality, uncertainty, and constraint, and beyond

Since the landmark work of R. E. Kalman in the 1960s, considerable efforts have been devoted to time series state space models for a large variety of dynamic estimation problems. In particular, parametric filters that seek analytical estimates based on a closed-form Markov–Bayes recursion, e.g., recursion from a Gaussian or Gaussian mixture (GM) prior to a Gaussian/GM posterior (termed ‘Gaussian conjugacy’ in this paper), form the backbone for a general time series filter design. Due to challenges arising from nonlinearity, multimodality (including target maneuver), intractable uncertainties (such as unknown inputs and/or non-Gaussian noises) and constraints (including circular quantities), etc., new theories, algorithms, and technologies have been developed continuously to maintain such a conjugacy, or to approximate it as close as possible. They had contributed in large part to the prospective developments of time series parametric filters in the last six decades. In this paper, we review the state of the art in distinctive categories and highlight some insights that may otherwise be easily overlooked. In particular, specific attention is paid to nonlinear systems with an informative observation, multimodal systems including Gaussian mixture posterior and maneuvers, and intractable unknown inputs and constraints, to fill some gaps in existing reviews and surveys. In addition, we provide some new thoughts on alternatives to the first-order Markov transition model and on filter evaluation with regard to computing complexity

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Computationally-efficient visual inertial odometry for autonomous vehicle

This thesis presents the design, implementation, and validation of a novel nonlinearfiltering based Visual Inertial Odometry (VIO) framework for robotic navigation in GPSdenied environments. The system attempts to track the vehicle’s ego-motion at each time instant while capturing the benefits of both the camera information and the Inertial Measurement Unit (IMU). VIO demands considerable computational resources and processing time, and this makes the hardware implementation quite challenging for micro- and nanorobotic systems. In many cases, the VIO process selects a small subset of tracked features to reduce the computational cost. VIO estimation also suffers from the inevitable accumulation of error. This limitation makes the estimation gradually diverge and even fail to track the vehicle trajectory over long-term operation. Deploying optimization for the entire trajectory helps to minimize the accumulative errors, but increases the computational cost significantly. The VIO hardware implementation can utilize a more powerful processor and specialized hardware computing platforms, such as Field Programmable Gate Arrays, Graphics Processing Units and Application-Specific Integrated Circuits, to accelerate the execution. However, the computation still needs to perform identical computational steps with similar complexity. Processing data at a higher frequency increases energy consumption significantly. The development of advanced hardware systems is also expensive and time-consuming. Consequently, the approach of developing an efficient algorithm will be beneficial with or without hardware acceleration. The research described in this thesis proposes multiple solutions to accelerate the visual inertial odometry computation while maintaining a comparative estimation accuracy over long-term operation among state-ofthe- art algorithms. This research has resulted in three significant contributions. First, this research involved the design and validation of a novel nonlinear filtering sensor-fusion algorithm using trifocal tensor geometry and a cubature Kalman filter. The combination has handled the system nonlinearity effectively, while reducing the computational cost and system complexity significantly. Second, this research develops two solutions to address the error accumulation issue. For standalone self-localization projects, the first solution applies a local optimization procedure for the measurement update, which performs multiple corrections on a single measurement to optimize the latest filter state and covariance. For larger navigation projects, the second solution integrates VIO with additional pseudo-ranging measurements between the vehicle and multiple beacons in order to bound the accumulative errors. Third, this research develops a novel parallel-processing VIO algorithm to speed up the execution using a multi-core CPU. This allows the distribution of the filtering computation on each core to process and optimize each feature measurement update independently. The performance of the proposed visual inertial odometry framework is evaluated using publicly-available self-localization datasets, for comparison with some other open-source algorithms. The results illustrate that a proposed VIO framework is able to improve the VIO’s computational efficiency without the installation of specialized hardware computing platforms and advanced software libraries

Distributed joint probabilistic data association filter with hybrid fusion strategy

This paper investigates the problem of distributed multitarget tracking (MTT) over a large-scale sensor network, consisting of low-cost sensors. Each local sensor runs a joint probabilistic data association filter to obtain local estimates and communicates with its neighbors for information fusion. The conventional fusion strategies, i.e., consensus on measurement (CM) and consensus on information (CI), are extended to MTT scenarios. This means that data association uncertainty and sensor fusion problems are solved simultaneously. Motivated by the complementary characteristics of these two different fusion approaches, a novel distributed MTT algorithm using a hybrid fusion strategy, e.g., a mix of CM and CI, is proposed. A distributed counting algorithm is incorporated into the tracker to provide the knowledge of the total number of sensor nodes. The new algorithm developed shows advantages in preserving boundedness of local estimates, guaranteeing global convergence to the optimal centralized version and being implemented without requiring no global information, compared with other fusion approaches. Simulations clearly demonstrate the characteristics and tracking performance of the proposed algorithm

Distributed fusion filter over lossy wireless sensor networks with the presence of non-Gaussian noise

The information transmission between nodes in a wireless sensor networks (WSNs) often causes packet loss due to denial-of-service (DoS) attack, energy limitations, and environmental factors, and the information that is successfully transmitted can also be contaminated by non-Gaussian noise. The presence of these two factors poses a challenge for distributed state estimation (DSE) over WSNs. In this paper, a generalized packet drop model is proposed to describe the packet loss phenomenon caused by DoS attacks and other factors. Moreover, a modified maximum correntropy Kalman filter is given, and it is extended to distributed form (DM-MCKF). In addition, a distributed modified maximum correntropy Kalman filter incorporating the generalized data packet drop (DM-MCKF-DPD) algorithm is provided to implement DSE with the presence of both non-Gaussian noise pollution and packet drop. A sufficient condition to ensure the convergence of the fixed-point iterative process of the DM-MCKF-DPD algorithm is presented and the computational complexity of the DM-MCKF-DPD algorithm is analyzed. Finally, the effectiveness and feasibility of the proposed algorithms are verified by simulations