Vehicle Localization Using Sensors Data Fusion Via Integration of Covariance Intersection and Interval Analysis

Achieving an innovative integrated sensor fusion architecture with a robust vehicle navigation and localization using an extended Kalman filter, interval analysis and covariance intersection that can overcome the uncertainty in the system model and sensor noise statistics. There are various approaches to the problem, but here the focus is on an approach which can guaranteed performance of sensor-based navigation. The guaranteed performance is quantified by explicit bounds of position estimate of a ground vehicle. Ground vehicles generally carry dead reckoning sensors such as wheel encoders and inertial sensors, to measure acceleration and angle rate, while obstacle detection and mapmaking is done with time-of-flight ultrasonic sensors. Most of these sensors give overlapping or complementary information and sometimes are redundant as well, which offers scope for exploiting data fusion. The purpose here is to achieve data fusion for ground vehicles with low-cost sensors by forming an intelligent sensor system. This is accomplished by combining the sensors' measurements and processing these measurements with data fusion algorithms. The algorithms are complementary in the sense that they compensate for each other's limitations, so that the resulting performance of the sensor system is better than its individual components.     

基于球杆仪和光栅尺的工作台精度调整

用球杆仪和光栅尺同时测量了两轴联动精密工作台的走圆运动．结果显示,光栅尺的主要误差源是测量噪声和定位误差,球杆仪的主要误差源是定位误差．尽管对于单轴实时位置反馈来说,光栅尺的测量精度已经足够,但是两个方向光栅尺的测量数据不能反映两轴间的相对精度．通过对光栅尺和球杆仪测量的工作台走圆运动测量数据的分析,建立了测量系统的数学模型,在此基础上解耦并识别出了球杆仪和光栅尺的定位误差．提出了根据光栅尺倾角误差实现工作台精度调整的策略．     

Optimal fusion of multiple nonlinear sensor data

A framework for the detection of bandlimited signals by optimally fusing the multinonlinear sensor data is developed. Though most sensors used are assumed to be linear, none of them individually or in series gives the truly linear relationship, and errors are inevitable as a result of the assumption of linearity. A new approach, which takes the actual nonlinear characteristics of sensors into account, is advocated. Though the fusion of redundant information can reduce the overall uncertainty and, thus, serves to increase the accuracy of the process measurements, identifying the faulty readings and fusing only the reliable data are very difficult and challenging. An optimal multiple nonlinear sensor data fusion scheme in which multisensor data fusion is done by scheduling the sensor measurements is proposed. The main idea of the multisensor fusion schemes proposed in this paper is to pick only the reliable data for the fusion and disregard the rest. The proposed theoretical framework is supported by illustrative examples and simulation data.     

Extended Kalman filter-based sensor fusion for operational space control of a robot arm

Accurate measurements of positions, velocities, and accelerations in both joint and operational space are required for achieving accurate operational space motion control of robots. Servomotors used for joint actuation are normally equipped with position sensors and optionally with velocity sensors for interlink motion measurements. Further improvements in measurement accuracy can be obtained by equipping the robot arm with accelerometers for absolute acceleration measurement. In this paper, an extended Kalman filter is used for multisensor fusion. The real-time control algorithm was previously based on the assumption of a jerk represented as a processed white noise with the zero mean. In reality, the accelerations are varying in time during the arm motion, and the zero mean assumption is not valid, particularly during fast accelerating periods. In this paper, a model predictive control approach is used for predetermining next-time-step jerk such that the remaining term can be modeled as Gaussian white noise. Experimental results illustrate the effectiveness of the proposed sensor fusion approach.     

3-D Modeling of Pyroelectric Sensor Arrays Part I: Multiphysics Finite-Element Simulation

When modeling pyroelectric sensors, not all relevant and coupled physical phenomena could be considered contemporaneously up to now. We describe a method to examine problems with coupled thermal, electrical, and mechanical fields using finite-element software. Modeling the complex interaction between the physical fields is realized by implementing a two-step approach that takes advantage of the internal computational routines of finite-element programs. The method will be exemplified on a standard pyroelectric sensor array, whose efficient 3-D modeling and simulation is demonstrated using a public accessible commercial finite element software.      

Integrated approach for automatic target recognition using a network of collaborative sensors

We introduce what is believed to be a novel concept by which several sensors with automatic target recognition (ATR) capability collaborate to recognize objects. Such an approach would be suitable for netted systems in which the sensors and platforms can coordinate to optimize end-to-end performance. We use correlation filtering techniques to facilitate the development of the concept, although other ATR algorithms may be easily substituted. Essentially, a self-configuring geometry of netted platforms is proposed that positions the sensors optimally with respect to each other, and takes into account the interactions among the sensor, the recognition algorithms, and the classes of the objects to be recognized. We show how such a paradigm optimizes overall performance, and illustrate the collaborative ATR scheme for recognizing targets in synthetic aperture radar imagery by using viewing position as a sensor parameter.     

Multi-task Joint Sparse Representation Classification Based on Fisher Discrimination Dictionary Learning

Recently, sparse representation classification (SRC) and fisher discrimination dictionary learning (FDDL) methods have emerged as important methods for vehicle classification. In this paper, inspired by recent breakthroughs of discrimination dictionary learning approach and multi-task joint covariate selection, we focus on the problem of vehicle classification in real-world applications by formulating it as a multi-task joint sparse representation model based on fisher discrimination dictionary learning to merge the strength of multiple features among multiple sensors. To improve the classification accuracy in complex scenes, we develop a new method, called multi-task joint sparse representation classification based on fisher discrimination dictionary learning, for vehicle classification. In our proposed method, the acoustic and seismic sensor data sets are captured to measure the same physical event simultaneously by multiple heterogeneous sensors and the multi-dimensional frequency spectrum features of sensors data are extracted using Mel frequency cepstral coefficients (MFCC). Moreover, we extend our model to handle sparse environmental noise. We experimentally demonstrate the benefits of joint information fusion based on fisher discrimination dictionary learning from different sensors in vehicle classification tasks.     

Sensor Fusion Methodology to Overcome Cross-Axis Problem for Micromachined Thermal Gas Inertial Sensor

Micromachined thermal gas inertial sensors are novel devices that take advantages of simple configuration, large working range, high shock resistance, and good reliability in virtue of using gaseous medium instead of mechanical proof mass as key moving and sensing elements. Basing on multidimentional movements of gas flow in a small chamber, the sensor generally undergoes a cross-axis problem. In this paper, a study on the cross-axis sensitivity of the thermal gas rotation sensor is reported. The cross-axis problem of the sensor is resulted from the multidimensional coupling movement of the convection flow in the sensor chamber and possibly be diminished by a tailored structural design. Unlike using a complex scheme on the mechanical structure, combining more than two sensors to form an integrated compensation system and using a fusion methodology to decouple cross rotations are proposed in this paper. The method helps to enhance practical applications for thermal rotation sensors.      

A Method for Judicious Fusion of Inconsistent Multiple Sensor Data

One of the major problems in sensor fusion is that sensors frequently provide spurious observations which are difficult to predict and model. The spurious measurements from sensors must be identified and eliminated since their incorporation in the fusion pool might lead to inaccurate estimation. This paper presents a unified sensor fusion strategy based on a modified Bayesian approach that can automatically identify the inconsistency in sensor measurements so that the spurious measurements can be eliminated from the data fusion process. The proposed method adds a term to the commonly used Bayesian formulation. This term is an estimate of the probability that the data is not spurious, based upon the measured data and the unknown value of the true state. In fusing two measurements, it has the effect of increasing the variance of the posterior distribution when measurement from one of the sensors is inconsistent with respect to the other. The increase or decrease in variance can be estimated using the information theoretic measure "entropy." The proposed strategy was verified with the help of extensive computations performed on simulated data from three sensors. A comparison was made between two different fusion schemes: centralized fusion in which data obtained from all sensors were fused simultaneously, and a decentralized or sequential Bayesian scheme that proved useful for identifying and eliminating spurious data from the fusion process. The simulations verified that the proposed strategy was able to identify spurious sensor measurements and eliminate them from the fusion process, thus leading to a better overall estimate of the true state. The proposed strategy was also validated with the help of experiments performed using stereo vision cameras, one infrared proximity sensor, and one laser proximity sensor. The information from these three sensing sources was fused to obtain an occupancy profile of the robotic workspace     

Estimation of attitudes from a low-cost miniaturized inertial platform using Kalman Filter-based sensor fusion algorithm

Due to costs, size and mass, commercially available inertial navigation systems are not suitable for small, autonomous flying vehicles like ALEX and other UAVs. In contrast, by using modern MEMS (or of similar class) sensors, hardware costs, size and mass can be reduced substantially. However, low-cost sensors often suffer from inaccuracy and are influenced greatly by temperature variation. In this work, such inaccuracies and dependence on temperature variations have been studied, modelled and compensated in order to reach an adequate quality of measurements for the estimation of attitudes. This has been done applying a Kaiman Filter-based sensor fusion algorithm that combines sensor models, error parameters and estimation scheme. Attitude estimation from low-cost sensors is first realized in a Matlab/Simulink platform and then implemented on hardware by programming the micro controller and validated. The accuracies of the estimated roll and pitch attitudes are well within the stipulated accuracy level of ±5‡ for the ALEX. However, the estimation of heading, which is mainly derived from the magnetometer readings, seems to be influenced greatly by the variation in local magnetic field     

陆地靶场声定位系统布站图形优化方法

提出了一种度量布站阵形结构优劣的方法.介绍了声源定位原理及解算模型,并给出了布站阵形结构优劣的度量计算公式,最后,通过计算仿真数据和试验数据验证所提方法的有效性和可行性.结果表明:图形结构的优劣程度受布站阵形和探测器数量两方面影响,文中的方法可以准确度量布站阵形结构的优劣,可为布站方案设计提供理论依据.     

Instrumentation architecture and sensor fusion for systems control

This paper presents a systematic approach for the design of an instrumentation architecture and a sensor data fusion concept which together enable the robust control of complex electromechanical systems. The development is based on the theory of hyperstability. Earlier results are generalized and previous restrictions on sensors are relaxed to broaden the applicability of the proposed method. Experimental results validate the methodology and confirm its efficacy in practical applications     

Kalman filtering for adaptive nonuniformity correction in infrared focal-plane arrays

A novel statistical approach is undertaken for the adaptive estimation of the gain and bias nonuniformity in infrared focal-plane array sensors from scene data. The gain and the bias of each detector are regarded as random state variables modeled by a discrete-time Gauss-Markov process. The proposed Gauss-Markov framework provides a mechanism for capturing the slow and random drift in the fixed-pattern noise as the operational conditions of the sensor vary in time. With a temporal stochastic model for each detector's gain and bias at hand, a Kalman filter is derived that uses scene data, comprising the detector's readout values sampled over a short period of time, to optimally update the detector's gain and bias estimates as these parameters drift. The proposed technique relies on a certain spatiotemporal diversity condition in the data, which is satisfied when all detectors see approximately the same range of temperatures within the periods between successive estimation epochs. The performance of the proposed technique is thoroughly studied, and its utility in mitigating fixed-pattern noise is demonstrated with both real infrared and simulated imagery.     

Theoretical analysis and measurement of the temperature dependence of a micromachined Fabry-Perot pressure sensor

In this study an analytical model that takes into account the coupled photoelastic and thermo-optical effects is established to evaluate the temperature dependence of a single-chip silicon micromachined Fabry-Perot pressure sensor. The results show that temperature variation has a significant effect on the performance of a micromachined Fabry-Perot pressure sensor with a conventional flat diaphragm. A new membrane-type silicon micromachined Fabry-Perot pressure sensor with a novel deeply corrugated diaphragm is then proposed. The sensor is fabricated on a single-chip by use of both surface- and bulk-micromachining techniques. Both analytical and experimental results show that the cross sensitivity of Fabry-Perot pressure sensors to temperature can be substantially alleviated by use of the proposed single deeply corrugated diaphragm.     

基于长信号谱自相关的光纤光栅传感解调方法

针对光纤光栅传感领域波长高精度探测和传感复用光栅数量增多的需要,提出一种基于长信号相关谱的新型光纤光栅数字解调技术.该技术在可调谐滤波法的基础上,通过传感光栅与调制光栅反射谱卷积后的信号,即光电探测器的光强时间信号,进行自相关分析以实现对波长漂移的测量以及对传感光栅反射谱形状的识别,增强系统的复用能力,提高了性价比.模拟仿真表明,光纤光栅长信号相关数字解调方法可以准确测量光纤光栅波长的漂移,能更好实现传感复用.等强度悬臂梁实验验证该解调方法能实现光栅应变的高精度测量,优于传统的应变片测试.     

Ranking sensors using an adaptive fuzzy logic algorithm

A procedure to rank sensors according to their noise rates was developed based on an adaptive fuzzy logic algorithm for sensor fusion. No a priori knowledge of the sensors performance is assumed. Simulation analysis indicated 83.33% successful ranking with noise rates up to 50%. In an indoor experiment with a mobile robot equipped with three logical sensors, 88% of the rankings were correct. The ranking procedure also indicates the ranking results success probability.     

Synthesis of feedback-based design concepts for sensors

We explore an approach to synthesize concepts of a class of sensors, where a quantity is sensed indirectly after nullifying its effect by using negative feedback. These sensors use negative feedback to increase the dynamic range of operation without compromising the sensitivity and resolution. The synthesis technique uses knowledge about existing phenomena to come up with an approach to synthesize concepts of sensors and also study their interactions with their surroundings, so as to generate robust designs. The approach uses a database of building blocks which are based on physical laws and effects that capture the transduction rules underlying the working principles of sensors. A simplified variant of the SAPPhIRE model of causality, which also uses physical laws and effects, has been adapted to represent the building blocks. SAPPhIRE model had been used earlier to understand analysis and synthesis of conceptual designs. We have adapted it here for automated generation of concepts. The novelty of the approach lies in the way and the ease with which it constructs a graph which is a super-set of the concept-space. The individual concepts are extracted out of the graph at a later point in time. The extraction of the concepts is done by using a modified breadth-first search algorithm which detects loops in the graph. The usage of breadth-first search algorithm for loop detection is novel, as we have demonstrated that it performs better than depth-first search algorithm for the specific problem. The technique has been implemented as a web-based application. For the sensor problems attempted, a number of existing patents were found that were based on the concepts that were generated by the synthesis algorithm, thus emphasizing the usefulness of the designs produced. The tool generated 35 concepts for accelerometers, out of which 2 concepts were found in patents. The synthesis approach also proposed new, feasible sensor concepts, thereby indicating its potential as a stimulator for enhancing creativity of designers. Automated generation of feedback-based sensor designs is a novel outcome of this approach.     

Temperature feedback control for improving the stability of a semiconductor-metal-oxide (SMO) gas sensor

Stability is a major concern of semiconductor-metal-oxide (SMO) gas sensors in practical applications, as they may cause false alarm problems. Ambient temperature is a major factor affecting the SMO gas sensor's stability. In this paper, we use a novel way to improve temperature stability of SMO (tin oxide) gas sensors by applying a temperature feedback control circuits which are compatible with our microelectromechanical systems sensor fabrication. A built-in platinum temperature sensor can precisely detect the sensor's working temperature. It provides feedback information to compensate the microheater's current to maintain the sensor's working temperature constant, regardless of ambient temperature change. Test results showed that, with this approach, significant improvement of stability has been achieved compared to SMO gas sensors without temperature compensation under the same ambient variation. The algorithm is realized through a hardware circuit, whose advantages include real time, large feedback gain, and low cost.     

Location information-aided task-oriented self-organization of ad-hoc sensor systems

A novel task-oriented self-organization algorithm that accounts for mostly location-dependent tasks and heterogeneous sensors inherent in dense ad-hoc sensor systems is proposed. It forms a sensor group for an announced task by sequentially selecting the best matched sensors using a leader election algorithm and a residual task calculation algorithm. To improve the associated communication overhead, the sensor node location information is used in task broadcasting, thus confining the algorithm implementation to a dynamically maintained contributor group which comprises of those sensors which may contribute to the task. Sensor localization is based on a refinement of an algorithm in which utilizes only the neighborhood information of each sensor node corresponding to its each preset radio transmission power level. The proposed self-organization algorithm and how various system parameters affect its performance are examined via extensive simulations. In a densely deployed sensor system, when the refined localization scheme is demonstrated to achieve very good localization, the proposed self-organization algorithm consistently yields a sensor group that covers the announced task.     

Multilayer sensing and aggregation approach to environmentalperception with one multifunctional sensor

This paper presents a novel approach to perception of a specified environment for intelligent system or robotics applications in which high-level information must be extracted from multi-sensors data. A CdS and Fe₃O₄ material based multifunction sensor has been developed to measure temperature, humidity and brightness. The sensor focuses on the processing of the multifunctional information in a multilayer framework, which is more attractive in terms of system simplicity, performance, and compact structure. Further along, quantity creditability tactics (QCT), one multisensing data fusion method, is approached, with which quantities are sequentially aggregated to generate a general perception about the sensed environment. Different from the popular fusion strategies, the proposed algorithm also works in a step-by-step framework, and proves to be more practical and more effective when there are more variables for calculation