Presence Of Sensor Biases Research Articles

Convex Combination for Wireless Localization Using Biased RSS Measurements

Received signal strength- (RSS-) based localization in wireless sensor networks (WSNs) has attracted significant attention due to its advantages of low cost and simple implementation. In practice, RSS measurements may suffer from sensor biases, which deteriorates the localization accuracy. However, most of the existing localization methods are designed for bias-free measurements. In this paper, we propose a convex combination method for RSS localization in the presence of sensor biases. The parameter vector composed of unknown location and sensor biases is estimated simultaneously by using a convex combination of some virtual points. These virtual points form a convex hull, into which the parameter vector falls with large probability. By this, the original nonconvex estimation problem is converted to be convex. Numerical examples demonstrate the superiority of the proposed method in terms of localization accuracy, compared to the existing semidefinite programming (SDP) methods.

Core temperature modelling and monitoring of lithium-ion battery in the presence of sensor bias

Lithium-ion battery is now considered as an enabling technology for modern civilization and sustainability, initiating wireless revolution and efficient energy storage. In spite of the significant progress made in materials and manufacturing, the growing awareness on the operational safety and reliability requires more efficient battery management systems. Core temperature, an underlying variable for battery management, is unfortunately unmeasurable and has to be online estimated via other measurable variables. The core temperature monitoring becomes even more challenging when the measurable variables encounter sensor bias as well as model inaccuracy and sensor noise. To this end, this paper improves the thermal model accuracy by introducing a radiation term into the conventional linear lumped model. The unknown parameters of the new nonlinear model are identified based on multi-objective optimization, of which the results confirm the superiority of the proposed nonlinear model. The sensor bias is treated as an extended state to be estimated together with other states. An extended unscented Kalman filter is accordingly developed to handle the nonlinearity, measurement noise and sensor bias. Both simulation and experimental results are given to demonstrate the efficacy of the proposed method, showing that the core temperature can be accurately estimated in spite of the sensor biases in the surface temperature or electric current measurements.

Fault Tolerant Control of a Nuclear Steam Generator in the presence of Sensor Biases

Steam Generator (SG) is one of the crucial components in a nuclear power plant where nuclear reactors like pressurized water reactors (PWR) and pressurized heavy water reactors (PHWR) are used. For safe operation of SG in power plants, the control of water level becomes very crucial. Poor control of the water level in SG can lead to frequent reactor shutdowns. The occurrence of biases in the measurement of level and pressure sensors used in the SG control system can result in significant degradation of the closed-loop performance. This work aims to introduce a fault tolerant control framework to arrest the performance degradation of conventional controllers in the presence of sensor bias. Initially, the bias present in the measurements is modeled as a time varying parameter and estimated using a suitably modified version of RNK based state and parameter estimator approach. Using the sensor bias estimates, the measurements are corrected and further used in the controller. The effectiveness of the proposed framework of FTC in the presence of sensor bias is demonstrated by carrying out simulation studies on a benchmark SG system. Analysis of simulation results reveals that the proposed FTC framework gives similar results to that of fault free case.

Radar/ESM anti‐bias track association algorithm based on track distance vector detection

To address radar/ESM track association problem in the presence of sensor biases and different targets reported by different sensors, an anti-bias track association algorithm based on track distance vectors detection is proposed according to the statistical characteristics of Gaussian random vectors. The state estimation decomposition equation is first derived in MPC. The track distance vectors are obtained by the real state cancellation method. Second, in order to eliminate most non-homologous target tracks, the rough association is performed according to the features of the azimuthal rate and inverse-time-to-go (ITG). Finally, the track-to-track association of radar and ESM is extracted based on track distance vectors chi-square distribution. The effectiveness of the proposed algorithm are verified by Monte–Carlo simulation experiments in the presence of sensor biases, targets densities, and detection probabilities.

Observability Analysis and State Estimation of Lithium-Ion Batteries in the Presence of Sensor Biases

This paper investigates the observability of one of the most commonly used equivalent circuit models (ECMs) for lithium-ion batteries and presents a method to estimate the state of charge (SOC) in the presence of sensor biases, highlighting the importance of observability analysis for choosing appropriate state estimation algorithms. Using a differential geometric approach, necessary and sufficient conditions for the nonlinear ECM to be observable are derived and are shown to be different from the conditions for the observability of the linearised model. It is then demonstrated that biases in the measurements, due to sensor ageing or calibration errors, can be estimated by applying a nonlinear Kalman filter to an augmented model where the biases are incorporated into the state vector. Experiments are carried out on a lithium-ion pouch cell and three types of nonlinear filters, the first-order extended Kalman filter (EKF), the second-order EKF and the unscented Kalman filter (UKF) are applied using experimental data. The different performances of the filters are explained from the point of view of observability.

Robust track-to-track association in the presence of sensor biases and missed detections

The paper addresses the problem of robust track-to-track association in the presence of sensor biases and missed detections. Under the condition of large range biases with sensors, it is validated that the structural difference between two sets of local tracks from different sensors can be described by a non-rigid transformation. After that, we turn the robust track-to-track association problem into the non-rigid point matching problem in the framework of TPS-RPM (Thin Plate Spline-Robust Point Matching). Further, to improve the performance of the track-to-track association, the structural feature is introduced for each local track, and the structural similarity is incorporated by regularizing the energy function of the TPS-RPM algorithm. Simulation results demonstrate the effectiveness of the proposed approaches compared with competing algorithms.

Track fusion in the presence of sensor biases

A computationally effective approach is developed in this study to deal with the problem of track fusion in the presence of sensor biases. Aiming at the case that sensor biases are implicitly included in the local estimates, a pseudo-measurement equation is derived based on the Taylor series expansion firstly, which reveals the relationship explicitly between local estimates and the sensor biases; and then, the bias estimates can be obtained in the rule of recursive least squares; finally, based on the derived pseudo-measurement equation, the sensor biases can be removed from the original local estimates and track fusion can be carried out directly and easily. Monte Carlo simulations demonstrate the efficiency and effectiveness of the proposed approach compared with the competing algorithms.

Formation Track Association Algorithm in the Presence of Sensor Bias Based on Double Fuzzy Topology

Aiming to solve the track association problem of the formation targets in the presence of sensor bias, a formation track association algorithm based on double fuzzy topology (DFT-FTA) is proposed according to the topology characteristics of the formation tracks. The formations are identified among sensors tracks based on the circulatory threshold model firstly. Then the formation tracks are replaced by their center track so that the first fuzzy topology model is established to realize preparatory association of formation tracks and common tracks. At last, the second fuzzy topology model based on the associated formation tracks and associated common tracks is established to realize formation inner track association. The simulation result shows that the proposed algorithm outperforms the fuzzy track alignment-association algorithm based on target invariable information (TII-FTA) and modified weight track association algorithm (MW-TA).

Track-to-Track Association Based on Structural Similarity in the Presence of Sensor Biases

The paper addresses the problem of track-to-track association in the presence of sensor biases. In some challenging scenarios, it may be infeasible to implement bias estimation and compensation in time due to the computational intractability or weak observability about sensor biases. In this paper, we introduce the structural feature for each local track, which describes the spatial relationship with its neighboring targets. Although the absolute coordinates of local tracks from the same target are severely different in the presence of sensor biases, their structural features may be similar. As a result, instead of using the absolute kinematic states only, we employee the structural similarity to define the association cost. When there are missed detections, the structural similarity between local tracks is evaluated by solving another 2D assignment subproblem. Simulation results demonstrated the power of the proposed approach.

From data to diagnosis and control using generalized orthonormal basis filters. Part II: Model predictive and fault tolerant control

Given a state space model together with the state noise and measurement noise characteristics, there are well established procedures to design a Kalman filter based model predictive control (MPC) and fault diagnosis scheme. In practice, however, such disturbance models relating the true root cause of the unmeasured disturbances with the states/outputs are difficult to develop. To alleviate this difficulty, we reformulate the MPC scheme proposed by K.R. Muske and J.B. Rawlings [Model predictive control with linear models, AIChE J. 39 (1993) 262–287] and the fault tolerant control scheme (FTCS) proposed by J. Prakash, S.C. Patwardhan, and S. Narasimhan [A supervisory approach to fault tolerant control of linear multivariable systems, Ind. Eng. Chem. Res. 41 (2002) 2270–2281] starting from the innovations form of state space model identified using generalized orthonormal basis function (GOBF) parameterization. The efficacy of the proposed MPC scheme and the on-line FTCS is demonstrated by conducting simulation studies on the benchmark shell control problem (SCP) and experimental studies on a laboratory scale continuous stirred tank heater (CSTH) system. The analysis of the simulation and experimental results reveals that the MPC scheme formulated using the identified observers produces superior regulatory performance when compared to the regulatory performance of conventional MPC controller even in the presence of significant plant model mismatch. The FTCS reformulated using the innovations form of state space model is able to isolate sensor as well as actuator faults occurring sequentially in time. In particular, the proposed FTCS is able to eliminate offset between the true value of the measured variable and the setpoint in the presence of sensor biases. Thus, the simulation and experimental study clearly demonstrate the advantages of formulating MPC and generalized likelihood ratio (GLR) based fault diagnosis schemes using the innovations form of state space model identified from input output data.

Decentralized estimation of sensor systematic error and target state vector

An accurate estimation of the sensor systematic error is significant for improving the performance of target tracking system. The existing methods usually append the bias states directly to the variable states to form augmented state vectors and utilize the conventional Kalman estimator to achieve state vectors estimate. So doing is expensive in computation, and much work is devoted to decoupling variable states and systematic error. But the decentralied estimation of systematic errors and reduction of the amount of computation as well as decentralied track fusion are far from being realized. This paper addresses distributed track fusion problem in multi-sensor tracking system in the presence of sensor bias. By this method, variable states and systematic error is decoupled. Decentralized systematic error estimation and track fusion are achieved. Simulation results verify that this method can get accurate estimation of systematic error and state vector.

Frequency-weighted state estimation with application to estimation in the presence of sensor bias

This paper introduces a general state-estimation methodology, frequency-weighted linear quadratic Gaussian estimation (FWLQGE) that enables a control designer to modify a standard LQG estimator to account for sensor imperfections. Using FWLQGE, a designer can account explicitly for colored sensor noise (i.e., the noise spectrum varies as a function of frequency). Alternatively, the designer can use the frequency dependent degree of freedom as a mechanism to specify the relative confidence in a sensor's output. Presented is a technique for solving the FWLQGE problem with existing software tools by simply augmenting the system matrices. Also presented is an example demonstrating how the procedure can be used to account for sensor biases. Here, FWLQGE is compared with two classic methods: state augmentation and post-sensor filtering, It is shown that while all methods can be made to yield equivalent estimators, FWLQGE incorporates a design degree of freedom that is intuitively appealing allowing an explicit trade off between bias rejection and estimator performance. Simulation and experimental results are presented.

The Role of Navigation in the Precision Registration of Sensor Data at Sea

The registration of data received from remote units or other facilities with surveillance data derived from a unit's own organic sensors (principally radars) is called gridlocking. This function is fundamental to the successful employment of the external data for coordinating a ship's activities with other units or forces and cueing the unit to possible threats. Accurate registration requires precise knowledge of the relationship between the ownship-derived data and the remote data. This relationship depends partially on knowing positions for the sensors which developed the various data. Because of the presence of sensor biases, the positions which provide the most accurate registration often do not correspond to the true geodetic position of the receiving unit and/or the remote sensors. Depending on the overlap between the remote sensor coverage volume and local ownship coverage volume, it can be shown that the role of navigation in developing this registration varies. Recent investigations, utilizing various data bases collected simultaneously from multiple physically separated naval radar suites, have shown that precise alignments can be achieved with minimal or, in some cases, no navigational input as long as common targets are held by the radar suites (i.e., relative gridlocking). Extension of these results to cases where no common coverage region exists (i.e., geodetic gridlocking) indicates that navigational positions from cooperative units within the local unit coverage volume could provide a possible standard for defining the relationship between ownship sensor coordinates and the absolute navigational coordinates. If the remote sensor data are also aligned to absolute coordinates, precise registration can then be achieved even in these cases.