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Driving in interactive dynamic traffic is a huge challenge for autonomous vehicles, especially for motion planning. The autonomous vehicle not only needs to predict the future states of the social vehicles to avoid a collision but also realizes the comfort and continuous plan. To deal with the problem, a spatio‐temporal decision‐making and motion planning framework with flexible constraints based on previous work is proposed. Improvements can be highlighted in three aspects. First, a neural network for trajectory prediction is trained and it is integrated into the framework, which can learn the social vehicles' reactions in the negotiation better and give more accurate predictions. Second, instead of setting the fixed local targets, the flexible‐constraint mechanism is introduced to increase the success rate of continuous planning, specifically, the local planning target is determined conditioned on the situation with an elastic factor. Third, two popular simulators with randomly disposed traffic participants are exploited, where vehicles are mutually influenced, more similar to real‐world traffic. The closed‐loop tests with zero crashes and the comparative experiments with traditional models demonstrate the superior performance and practical significance of the proposed framework. [ABSTRACT FROM AUTHOR]

As the vital factor of vehicle behavior understanding and prediction, vehicle taillight recognition is an important technology for autonomous driving, especially in diverse actual traffic scenes full of dynamic interactive traffic participants. However, in practical application, it always faces many challenges, such as ‘variable lighting conditions’, ‘non-uniform taillight standards’ and ‘random relative observation pose’, which lead to few mature solutions in current common autopilot systems. This work proposes an action-state joint learning-based vehicle taillight recognition method on the basis of vehicles detection and tracking, which takes both taillight state features and time series features into account, consequently getting practicable results even in complex actual scenes. In detail, vehicle tracking sequence is used as input and split into pieces through a sliding window. Then, a CNN-LSTM model is applied to simultaneously identify the action features of brake lights and turn signals, dividing taillight actions into five categories: None, Brake_on, Brake_off, Left_turn, Right_turn. Next, the brightness of high-position brake light is extracted through semantic segmentation and combined with taillight actions to form higher-level features for taillight state sequence analysis. Finally, an undirected graph model is used to establish the long-term dependence between successive pieces by analysing the higher-level features, thus inferring the continuous taillight state into: $off$ , $brake$ , $left$ , $right$. Datasets including daytime, nighttime, congested road, highway, etc. were collected, tested and published in our work to demonstrate its effectiveness and practicability. [ABSTRACT FROM AUTHOR]

Intelligent decision making and efficient trajectory planning are closely related in autonomous driving technology, especially in highway environment full of dynamic interactive traffic participants. This work integrates them into a unified hierarchical framework with long-term behavior planning (LTBP) and short-term dynamic planning (STDP) running in two parallel threads with different horizon, consequently forming a closed-loop maneuver and trajectory planning system that can react to the dynamic environment effectively and efficiently. In LTBP, a novel voxel structure and the ‘voxel expansion’ algorithm are proposed for the generation of driving corridors in 3D configuration, which involves the prediction states of surrounding vehicles. By using Dijkstra search, the maneuver with minimal cost is determined in form of voxel sequences, then a quadratic programming (QP) problem is constructed for solving the optimal trajectory. And in STDP, another small-scaled QP problem is performed to track or adjust the reference trajectory from LTBP in response to the dynamic obstacles. Meanwhile, a Responsibility-Sensitive Safety (RSS) Checker keeps running at high frequency for real-time feedback to ensure security. Experiments on real data collected in different highway scenarios demonstrate the effectiveness and efficiency of our work. [ABSTRACT FROM AUTHOR]

Autonomous driving, including intelligent decision-making and path planning, in dynamic environments (like highway) is significantly more difficult than the navigation in static scenarios because of the additional time dimension. Therefore, correlating the time dimension and the space dimension through prediction to create a spatio-temporal navigation map can make decision-making and path planning in such kinds of environment much easier. In this article, NGSIM data is analysed and processed from the perspective of the ego-vehicle (using the data as an ego-vehicle’s perception results). Based on the data, we develop an LSTM (Long-Short Term Memory)-based framework to predict possible trajectories of multiple surrounding vehicles within a certain range of the ego-vehicle. Then, the multiple predicted trajectories in a series of continuous dynamic highway scenes are projected into a spatio-temporal domain to create an octree map. Thus, dynamic targets and static obstacles can be unified into the same domain or map so that the dynamic disturbance problem for autonomous driving in highway environments can be resolved. Experimental results show that the proposed model is capable of predicting all the future trajectories around the ego-vehicle efficiently and the corresponding spatio-temporal map can be generated accurately in different dynamic scenarios. [ABSTRACT FROM AUTHOR]

Safe and efficient decision-making and path planning is a challenging problem for autonomous driving in highway because of numerous dynamic vehicles around the ego-vehicle. For ego-vehicle driving on structured roads, behavioral decisions tend to pay more attention to the relative distribution of its surrounding vehicles, while path planning based on the decision requires more detailed consideration of vehicle model and lane curvature. Therefore, these two modules are separated in our article and executed successively in different coordinate systems. Specifically, we create a decision topology for lane changing or following decision based on spatio-temporal lane-change gaps in relative moving coordinate, which consists of the node representing the gap and the edges representing the connectivity between two gaps. Combining the decision topology with the behaviors of surrounding dynamic vehicles, a local cost map is generated and the target decision gap is selected. Once the decision is done, a smooth and safe trajectory from the ego-vehicle to the target decision gap, that satisfies the constraints of acceleration and curvature, is obtained in the geodetic coordinate through polynomial trajectory generation. The real-time performance and effectiveness of this method were verified in the dynamic high-speed interaction scenarios. [ABSTRACT FROM AUTHOR]

Trajectory planning for the unmanned vehicle in the complex environment has always been a challenging task. Planned trajectory with the corresponding target velocity or acceleration sequence must be collision-free guaranteed and as comfortable as possible on the premise of obeying the traffic rules and interaction with other dynamic social vehicles. To meet this requirement, this paper proposes a framework for trajectory planning based on spatio-temporal map. Due to the time layer architecture in the map, the trajectory can be generated with velocity and acceleration simultaneously, and the whole trajectory is constrained within a ‘safety strip’, resulting in an efficient and safety guaranteed trajectory. The framework is composed of three sections: rough search, fine optimization and safety strip-based collision avoidance. For rough search, we propose an improved A* algorithm implemented in the discrete time layer to find out the suboptimal states efficiently. In fine optimization, the B-spline curve is exploited to connect the searched states into a continuous trajectory. And the optimal control points of B-spline are further grouped into several segments, forming the safety strip which is actually the distribution space of the planned trajectory. If necessary, an adjustment will be applied to keep the strip away from the collision zone, making the entire trajectory completely collision-free. Experiments on both public dataset and self-driving simulator show that the proposed framework can adapt to different kinds of complex traffic scenes well. [ABSTRACT FROM AUTHOR]

Regulator stations are widely used in gas transmission and distribution systems. Their state monitoring is of great significance to the safe operation of gas pipe networks. Due to the complexity of the working environment and the limitation of sensors, the acquired information is uncertain, which makes the state monitoring result prone to errors. The evidence theory has the ability to solve the uncertainty problem effectively. Most of the improvement methods of the evidence theory are in the spatial domain. These methods are not applicable to the fusion of the time-domain information. In this article, an improved method of the evidence theory is proposed for the state monitoring of a gas regulator station. It can meet the requirement of the dynamic fusion of the time-domain information. First, the back-propagation neural network is used to judge whether the evidence conflicts with each other. The simulation results demonstrate that it can judge the conflicts well. On this basis, the relative conflict factor is proposed to modify the evidence, and the calculation method of the adaptive time attenuation factor is proposed to reduce the accumulated error. The dynamic fusion of the time-domain information is realized by combining the time attenuation factor and the relative conflict factor. Finally, the proposed method is applied to the state monitoring of the gas regulator station. The feasibility and effectiveness of the method are verified by experiments. It verifies that the variation of the support degree of the proposed method for the correct proposition is 0.1478 higher than that of the temporal evidence combination based on relative reliability factor when the evidence is strongly conflicting. [ABSTRACT FROM AUTHOR]

Summary: To address the difficulties of under‐actuation, nonlinearity, strong coupling, disturbances and measurement noise in four‐wheel steering (4WS) vehicle tracking control, this paper presents an innovative control strategy based on differential flatness and linear active disturbance rejection controller (LADRC). The flatness property of 4WS vehicle is derived from a simplified dynamic model that ignores some complicated dynamics and external disturbances. The impact of these neglected components on the system is treated as part of the total‐disturbance, which is estimated by the observer and compensated by LADRC. Three flatness‐based control schemes, including feedback linearization controller with PID corrective term, traditional LADRC and LADRC with robust generalized proportional integral observer (RGPIO), are designed and investigated in this paper. Moreover, an overtaking scenario is selected as an application example for the tracking controllers, and the corresponding trajectory planner is designed. The tracking performances of these schemes for the overtaking trajectory are simulated on a three‐degree‐of‐freedom dynamic model and a more complete dynamic model of the 4WS vehicle, respectively. Results show the effectiveness of these schemes, especially the excellent tracking performance of LADRC with RGPIO in the presence of strong external disturbances and measurement noise. [ABSTRACT FROM AUTHOR]

Gravity-matching algorithm is a key to the gravity-aided inertial navigation system (INS). The traditional particle filter-based matching algorithm with a gravity sample vector is efficient. However, the range of particle filter and the probability model of the actual location are not specified in the algorithm. An improved particle filter-based matching algorithm with a gravity sample vector is proposed. Because of the high short-term accuracy of INS, the error range of INS in a short period is analyzed in a polar coordinate system in this algorithm. First, the attitude error angle model of INS is established. Relative angle error is proposed to calculate latitudes and longitudes of particles in the fan area at any position. Then a particle filter embedded in a particle filter is proposed to calculate the error range of the real position and establish the probability model of this position. Finally, in order to reduce the matching error, the relative displacements of the positions of the particles and the upper matching positions are added to the weights of the particles. Simulation results show that the proposed method has higher accuracy and better robustness. [ABSTRACT FROM AUTHOR]

The matching area selection algorithm is one of the key technologies for underwater gravity-aided inertial navigation system. Positioning accuracy and matching rate of gravity matching can be improved in high quality matching area. The traditional matching area selection algorithms usually use the statistical characteristic parameters of gravity field, which is difficult to reflect the spatial characteristics of gravity field. In order to explore more vector characteristics and spatial relation characteristics of gravity field and find more potential matching areas, the matching area selection algorithm based on Delaunay triangulation is proposed in this article. The proposed algorithm establishes the three-dimensional model and extracts spatial feature parameters to analysis gravity field. The comprehensive characteristics matrix is used to select matching area by feature extraction and cluster analysis. The experimental results show that the proposed algorithm can extract more characteristics of gravity field, and select more effective matching areas than the traditional algorithms. [ABSTRACT FROM AUTHOR]

The matching area selection algorithm is one of the key technologies for underwater gravity‐aided inertial navigation system, which directly affects the positioning accuracy and matching rate of underwater navigation. The traditional matching area selection algorithms usually use the statistical characteristic parameters of gravity field. However, the traditional algorithms are difficult to reflect the spatial relation characteristic of gravity field, which always miss some latent matching areas with obvious change of gravity field. In order to solve this problem, the matching area selection algorithm based on co‐occurrence matrix is proposed. The proposed algorithm establishes gravity anomaly co‐occurrence matrix and extracts spatial relation characteristic parameters to reflect the gravity field. The comprehensive spatial characteristic parameter is built by entropy and is used to select the matching area by maximization of inter‐class variance. The experimental results show that the proposed algorithm can select more effective matching areas than the traditional algorithms. [ABSTRACT FROM AUTHOR]

This paper presents a search-based global motion planning method, called the two-phase A*, with an adaptive heuristic weight. This method is suitable for planning a global path in real time for a car-like vehicle in both indoor and outdoor environments. In each planning cycle, the method first estimates a proper heuristic weight based on the hardness of the planning query. Then, it finds a nearly optimal path subject to the non-holonomic constraints using an improved A* with a weighted heuristic function. By estimating the heuristic weight dynamically, the two-phase A* is able to adjust the optimality level of its path based on the hardness of the planning query. Therefore, the two-phase A* sacrifices little planning optimality, and its computation time is acceptable in most situations. The two-phase A* has been implemented and tested in the simulations and real-world experiments over various task environments. The results show that the two-phase A* can generate a nearly optimal global path dynamically, which satisfies the non-holonomic constraints of a car-like vehicle and reduces the total navigation time. [ABSTRACT FROM AUTHOR]

Integrated navigation of unmanned ground vehicles (UGV) is significant for many advanced intelligent transportation system applications. Adaptive information fusion technique based on observability analysis has a great potential to enhance UGV integrated navigation systems for the capability of high-precision positioning and navigation. In an integrated navigation system, the tolerance against unknown and time-varying observation conditions is a key factor to satisfy the specific requirements of high-precision, self-adaption, and high reliability. Thus, a novel adaptive federated Kalman filter (FKF) is proposed with time-varying information sharing factors based on the criteria for the degree of observability. In addition, an error-state cascaded integration architecture is designed for UGV integrated navigation. Simulation with real datasets gathered from road tests in urban areas showed that the new adaptive integrated navigation system can autonomously update FKF information sharing factors according to the measurement quality and the observability of each navigation error-state. Therefore, the accuracy, robustness, and fault-tolerance ability of the whole system can be effectively improved in a high dynamic environment. [ABSTRACT FROM AUTHOR]

When Doppler velocity log (DVL) works in a complex underwater environment, it has the possibility of malfunction at any time, which will affect the positioning accuracy of underwater integrated navigation system (INS). In this work, the INS/DVL integrated navigation system model is established to deal with DVL malfunctions, and the support vector regression (SVR) algorithm is used to establish the velocity regression prediction model of DVL. An optimized grid search-genetic algorithm is used to select the best parameters of SVR. Simulations are designed to compare the results of SVR prediction model and isolating DVL during DVL failure. The semi-physical experiment is carried out to verify the validity and applicability of DVL velocity prediction model. The experimental results show that the INS/DVL integrated navigation system with the proposed model based on SVR performs better than the original integrated navigation system during DVL malfunction. [ABSTRACT FROM AUTHOR]

Deep reinforcement learning (DRL) has been successfully applied to end-to-end autonomous driving, especially in simulation environments. However, common DRL approaches used in complex autonomous driving scenarios sometimes are unstable or difficult to converge. This paper proposes two approaches to improve the stability of the policy model training with as few manual data as possible. For the first approach, reinforcement learning is combined with imitation learning to train a feature network with a small amount of manual data for parameters initialization. For the second approach, an auxiliary network is added to the reinforcement learning framework, which can leverage the real-time measurement information to deepen the understanding of environment, without any guide of demonstrators. To verify the effectiveness of these two approaches, simulations in image information-based and lidar information-based end-to-end autonomous driving systems are conducted, respectively. These approaches are not only tested in the virtual game world, but also applied in Gazebo, in which we build a 3D world based on the real vehicle model of Ranger XP900 platform, the real 3D obstacle model, and the real motion constraints with inertial characteristics, so as to ensure that the trained end-to-end autonomous driving model is more suitable for the real world. Experimental results show that the performance is increased by over 45% in the virtual game world, and can converge quickly and stably in Gazebo in which previous methods can hardly converge. [ABSTRACT FROM AUTHOR]

Summary: In this paper, we propose a simultaneous state estimation and fault estimation approach for a class of first‐order hyperbolic partial integral differential equation systems. Specifically, we consider the multiplicative boundary actuator and sensor faults, ie, unknown fault parameters multiplying by the boundary input or boundary state (ie, output). As a consequence, two difficulties arise immediately: (1) simultaneous estimation of both plant state and faults is a nonlinear problem due to the multiplication between fault parameters and plant signals; (2) no prior information is available to determine the type (actuator or sensor) of faults. To overcome these difficulties, this paper develops adaptive fault parameter update laws and embeds the resulting laws into the plant state observer design. First, we propose new approaches to estimate actuator fault and sensor fault, respectively. Next, we develop a novel method to simultaneously estimate actuator and sensor faults. The proposed observer and update laws, designed using only one boundary measurement, ensure both state estimation and fault parameter estimation. By choosing appropriate Lyapunov functions, we prove that the estimates of state and fault parameters converge to an arbitrarily small neighborhood of their true values. Numerical simulations are used to demonstrate the effectiveness of the proposed estimation approaches. [ABSTRACT FROM AUTHOR]

The gravity matching algorithm is a key of the gravity-aided inertial navigation system (INS). The traditional matching algorithm connects only the measured gravity anomaly data to the position of the carrier according to a certain method to correct the inertial error. The gravitational field characteristic parameters should also be considered in the matching algorithm to improve the matching accuracy and reduce the number of mismatching. Therefore, based on the vector matching algorithm, a characteristic parameter matching algorithm is proposed. This paper presents new methods to calculate the characteristic parameters and the range of particle filters to increase the accuracy of matching. The gravity anomaly value of each particle is calculated more accurately by the proposed method of continuous gravity anomaly. Due to the high short-time accuracy of inertial navigation, the final trajectory is obtained by rigid transformation of a series of positions indicated by the INS corresponding to the trajectory. Simulation results prove that when compared with the vector matching algorithm, the proposed method makes the matching results more accurate and more reliable. [ABSTRACT FROM AUTHOR]

The effects of the tube wall on the fluid flow, permeability and streaming potential are reported for a porous transducer for use in a liquid circular angular accelerometer. Fluid flow and pressure near the surface of the transducer are modeled and validated numerically. We show that the differential pressure across the transducer is not affected by the tube wall. A capillary bundle model is employed to represent the transducer to obtain the radial porosity, permeability and streaming potential distributions due to wall effects. A simulated spherical packing is generated from measured particle parameters to specify the model by calculating the radial porosity distribution. The theoretical permeability and streaming potential coupling coefficient are located within one standard deviation from the mean of measurements. Due to the radial distribution of streaming potential, the electrode can be configured into a large streaming potential region to measure the signal more efficiently. Three electrode configurations are described based on these results, which can be applied to increase the signal of the sensor. [ABSTRACT FROM AUTHOR]

This paper presents a lightweight stereo vision-based driving lane detection and classification system to achieve the ego-car’s lateral positioning and forward collision warning to aid advanced driver assistance systems (ADAS). For lane detection, we design a self-adaptive traffic lanes model in Hough Space with a maximum likelihood angle and dynamic pole detection region of interests (ROIs), which is robust to road bumpiness, lane structure changing while the ego-car’s driving and interferential markings on the ground. What’s more, this model can be improved with geographic information system or electronic map to achieve more accurate results. Besides, the 3-D information acquired by stereo matching is used to generate an obstacle mask to reduce irrelevant objects’ interfere and detect forward collision distance. For lane classification, a convolutional neural network is trained by using manually labeled ROI from KITTI data set to classify the left/right-side line of host lane so that we can provide significant information for lane changing strategy making in ADAS. Quantitative experimental evaluation shows good true positive rate on lane detection and classification with a real-time (15Hz) working speed. Experimental results also demonstrate a certain level of system robustness on variation of the environment. [ABSTRACT FROM PUBLISHER]

An optimal distributed fusion estimation problem is concerned in this study for a kind of linear dynamic multirate sensors systems with correlated noise and stochastic unreliable measurements. The system is formulated at the finest scale with multiple sensors at different scales observing a common target independently with different sampling rates. The noise between different sensors is relevant, moreover, is also correlated with the system noise. The authors derive the local state estimation algorithms under the circumstance of total reliable measurements and stochastic unreliable measurements occur occasions, and the optimal distributed Kalman filter fusion algorithm, respectively. The authors provide a simulation example to illustrate the effectiveness and feasibility of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, a complete mathematical model for a half-tracked vehicle with eight degrees of freedom is established. It is difficult to control a suspension system owing to complexity of system itself and irregular excitations from road surfaces. A road excitation model is analyzed in which a relationship between the vehicle body and the gun is taken into account. Active disturbance rejection controllers are proposed in all channels of six independent subsystems by decoupling the original system. With switch extended state observers, total disturbances are eliminated for the half-tracked vehicle. Simulation results are presented to illustrate effectiveness of the proposed method in this paper. [ABSTRACT FROM PUBLISHER]

In the application of gravity-aided navigation, the matching algorithm is the key technique. A matching algorithm combined with filter recursive process and trajectory similarity transformation is proposed, which deals with a gravity anomaly database tabulated in the form of a digital model. Due to the difficulty of establishing a specific observation model and the low observability of system models derived from inertial navigation system (INS) error equations, a filter model based on vehicle position is established and one of the parameters is estimated by INS short-term output. The matching algorithm is a two-stage matching process. The first stage is based on point mass filter to achieve a preliminary matching result. The second stage implements precise matching via similarity transformation. Simulation and experimental tests show that compared to traditional algorithms, the proposed algorithm can achieve a more precise and reliable location result. [ABSTRACT FROM PUBLISHER]

In inertial navigation system (INS)/doppler velocity log (DVL) integrated navigation, DVL commonly provides the 3-D velocity of the vehicle to the navigation filter. Theoretically, this INS/DVL fusion is the loosely coupled approach, in which DVL requires sufficient beam measurements (at least three) to calculate the 3-D velocity. However, in cases that DVL only has limited (fewer than three) beam measurements, which is possible in the underwater environment, the loosely coupled navigation system can no longer work, and only leaves the INS to work alone. Therefore, the navigation error will accumulate with time. In contrast to the loosely coupled approach, the tightly coupled approach directly uses DVL beam measurements without transforming them to 3-D velocity. In this paper, a method based on the tightly coupled approach for INS/DVL integrated navigation with limited DVL beam measurements is proposed, in which depth updates from a pressure sensor generally equipped by most autonomous underwater vehicles (AUVs) are applied. Simulation results verify that the proposed method is efficient in most kinds of tasks of the AUV. [ABSTRACT FROM PUBLISHER]

This paper presents real-time obstacles detection and their status classification method for collision warning in the vehicle active safety system. Specifically, stereo cameras and millimeter wave (mmw)-radar are fused to help the driving ego-vehicle to find “Danger” or “Potential Danger” in a timely way through combining with the vehicle kinematic model. The proposed method makes full use of the unique advantages of stereo cameras and mmw-radar to sense the environment through several modules. Cameras are mainly used to detect the near or lateral dynamic objects and to obtain the obstacles region of interest (ROI) considering its rich information and high sensitivity to the lateral displacement, while far or longitudinal relative dynamic objects are detected by mmw-radar according to its observational ability to make up for the disadvantage of cameras. In detail, a cameras detector utilizes ”error vectors” rather than the optical flow to obtain dynamic classes through two times clustering. Mmw-radar mainly detects relative dynamic objects, whose absolute speed can be computed according to the ego-vehicle’s state. Then, the detected objects of these two detectors are integrated in an obstacles ROI map, which is obtained through an UV-disparity obstacles detection algorithm to get the final dynamic and relative dynamic objects. Finally, they are classified by comparing them with a dangerous area that is acquired according to the vehicle kinematic model in a special vehicle coordinate system, which is fixed to the ground temporarily. This method is tested on our mobile platforms and the results prove that it can work effectively even though the ego-vehicle drives quickly. [ABSTRACT FROM PUBLISHER]

This paper is concerned with fault detection of a kind of nonlinear dynamic system. Based on the framework of hierarchy information processing, the scope of the fault is first located by use of the presented windowing cubature Kalman filter (WCKF), followed by point-by-point fault detection to locate the fault by use of the residuals of each moment within the suspected windows. Theoretical analysis and experiments show that the presented algorithm is more effective than the traditional point-by-point fault detection method that only uses the moment residuals. The presented algorithm has potential value in many application fields, such as fault detection, reliability evaluation, fault tolerant control, etc. [ABSTRACT FROM AUTHOR]

A matching algorithm is a key technique in the gravity-aided inertial navigation system (INS). A matching algorithm combined with an iterated closest contour point (ICCP) algorithm and a point mass filter (PMF) is proposed. The algorithm involves a two-step matching process. First, the PMF based on vehicle position variable can obtain in real-time an instructional position given in a large initial position error. In particular, since the gravity anomaly database is tabulated in the form of a digital model, the parameter in the filter model is estimated by INS short-term displacement. Then, the ICCP algorithm with a sliding window can be employed for further matching. Simulation tests indicate that compared with the conventional ICCP algorithm, the proposed algorithm can achieve better results. [ABSTRACT FROM PUBLISHER]

In this paper, the problem of optimal state estimation is studied for fusion of asynchronous multirate multiscale sensors with unreliable measurements and correlated noise. The noise of different sensors is cross-correlated and coupled with the system noise of the previous step and the same time step. The system is described at the highest sampling rate with different sensors observing a single target independently with multiple sampling rates. An optimal state estimation algorithm based on iterative estimation of the white noise estimator is presented, which makes full use of the observation information effectively, overcomes the packet loss, data fault, unreliable factors, and improves the precision and the robustness of the system state estimation. A numerical example is used to illustrate the effectiveness of the presented algorithm. [ABSTRACT FROM PUBLISHER]