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A lot of performance evaluation metrics exist for nonlinear filters. At present, the most commonly used one is a single and incomprehensive metric of performance. This metric can continuously and quantitatively describe the performance of the nonlinear filters. But in many cases, we need to rank the performance of the filters. It is in general very hard to rank the filters just using a single metric...
This paper investigates the robust weighted fusion estimation problem of multi-sensor systems with mixed uncertainties, including stochastic parameter uncertainties, missing measurements and uncertain noise variances. The stochastic parameter uncertainties are described by multiplicative noise. Especially, the variances of both the multiplicative and additive noises are uncertain. By introducing two...
This paper investigates stochastic containment control problems of multi-agent systems with measured noise in mean square sense. First, based on the Kalman-Bucy filtering theory, we design a proposed protocol for stochastic containment control problems based on the neighbors' information, and give a proof to check that Kalman-Bucy filtering estimation is an asymptotically unbiased estimation. Second,...
Fault estimation with performance analysis is investigated in the least squares sense for a class of time-varying systems with event-triggered measurement transmission and biased process noise covariances. In most other work, process noise covariance is assumed as either completely known. In this paper, we relax the assumption of knowing the exact process noise covariance and apply an event-triggered...
In this paper, a bias-eliminated subspace identification method is proposed for application under unexpected disturbance with deterministic dynamics. The linear superposition principle is adopted to decompose the output response into deterministic, disturbed and stochastic components, such that an LQ decomposition is developed to eliminate the disturbance and noise effect for consistent estimation...
Our newly proposed approach to extended object tracking (EOT) using extension deformation is simple and effective. This approach assumes that the extension of an object is deformed from an ellipsoidal reference extension, which unfortunately restricts its use for complex extensions. To overcome this weakness, this paper proposes that the current object extension be modeled as deformed from the one...
Target tracking is a hot topic for unmanned aerial vehicle surveillance. Recently, the novel random sample consensus (RANSAC) algorithm shows a good tracking performance in dense clutter environment. However, the heavy computational burden limits the usage for unmanned aerial vehicle (UAV). In this paper, a density-based recursive random sample consensus (DBR-RANSAC) algorithm is proposed, which utilizes...
This paper proposes a method for estimating the process noise covariance matrix, using multiple Kalman filters. The basic idea is to employ the difference between the expected prediction error covariance, calculated in the Kalman filters, and the measured prediction error covariance. The required estimate of the process noise covariance is obtained by solving a least squares problem. One simulated...
In this paper we present and compare four methods which address attitude estimation problem using Inertial Measurement Unit (IMU). The result of these algorithms is attitude estimate in the form of attitude quaternion. Two of these methods are authors original propositions based on expanding attitude determination algorithms by additional complementary filtering. It is shown, that this expansion significantly...
In this work, the modulating functions method is proposed for estimating coefficients in higher-order nonlinear partial differential equation which is the fifth order Kortewegde Vries (KdV) equation. The proposed method transforms the problem into a system of linear algebraic equations of the unknowns. The statistical properties of the modulating functions solution are described in this paper. In...
We study optimal input design and bias-compensating parameter estimation methods for continuous-time models applied on a mechanical laboratory experiment. Within this task we compare two online estimation methods that are based on Poisson moment functions with focus on quantized system outputs due to an angular encoder: The standard recursive least-squares (RLS) approach and a bias-compensating recursive...
In this study, we add on to our previous researches for non-traditional filtering the investigation of measurement and process noise covariance adaptation and propose an Adaptive Unscented Kalman Filter (AUKF) for nanosatellite attitude estimation. Singular Value Decomposition (SVD) method runs using the magnetometer and sun sensor measurements as the first stage of the algorithm and estimates the...
We consider the remote source coding setting in which a source realization is estimated from a lossy compressed sequence of noisy observations. Unlike in the optimal remote source coding problem, however, the encoder is bound to use good codes with respect to the observation sequence, i.e., codes that are optimal for the lossy reconstruction of the observation, rather than the remote source. This...
This paper deals with the bearing angle estimation problem associated with a 1-axis gimballed ultrasonic seeker which can be used as a low-cost sensor for mobile robot applications. Under the assumptions that the target has a ultrasonic transmitter, and the gimbal control loops work properly, the moving target tracking reduces to the estimation problem of a piecewise constant bearing angle using real...
This paper presents two approaches considering a distributed framework for joint optimization of sensor coverage for target detection and target tracking for maximizing estimation performance for multi-agent systems. The first algorithm is based on the Lloyd algorithm, which uses a centroid of Voronoi partitions, one of the workarounds of sensor coverage problems. The other algorithm is based on the...
Cross-Technology Interference affects the operation of low-power ZigBee networks, especially under severe WiFi interference. Accurate corruption estimation is very important to improve the resilience of ZigBee transmissions. However, there are many limitations in existing approaches such as low accuracy, high overhead, and requiring hardware modification. In this paper, we propose an accurate corruption...
Due to the limited bandwidth of underwater communication links, underwater cooperative localization usually adopts a distributed processing architecture. Members of the team estimate positions using their local sensor data, and fuse the information communicated by other members for cooperation. It is common practice to naïvely assume independency during information fusion between cooperative members...
The sensitivity of a recently proposed spike detector exploiting the volatile properties of memristive device is optimised. A 200 nm × 200 nm TiOx memristive device in volatile region is biased with sub-threshold, unipolar and bipolar neural events. The input neural signal is pre-processed using different amplification settings. The resistive state response of the test device in response to the input...
With the rapid growth of smart devices, mobile crowdsensing is becoming an important paradigm to acquire information from physical environments. Considering that the sensing data collected by mobile users are normally noisy and imprecise, one of the pressing problems in mobile crowdsensing is to evaluate the data quality in real time and to steer users to acquire data with high quality. However, it...
An approach to detection of anomalous measurements (fault detection), which occur at solving guaranteed estimation issue is outlined. It is based on statistical processing the innovation sequence in the Kalman filter. A special case of the linear dynamical system, which state is observed only within a short-time interval, is considered; statistical characteristics of the innovation sequence are shown...
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