Sensor Validation and Fusion for Automated Vehicle Control Using Fuzzy Techniques

For longitudinal control the automated vehicles in intelligent vehicle highway system (IVHS) require sensors to estimate the relative distance and velocity between vehicles. High data fidelity of these sensors is required to maintain the reliability and safety of the IVHS. In this paper, the authors develop a methodology for validation and fusion of sensory readings obtained from multiple sensors used for tracking automated vehicles and for avoiding objects in its path. The authors introduce tracking models for the various operating states of the automated vehicle, namely vehicle following, maneuvering, i.e. split, merge, lane change, emergencies, and for the lead vehicle in a platoon. The Kalman filtering approach is proposed for the formation of real time validation gates. This along with the algorithmic sensor validation filter is used for sensory data validation. The validated data are then fused by using a Bayesian method called the probabilistic data association filter. The procedure is demonstrated by two examples using simulated data, data obtained from a platooning test set-up.

Multi-sensor data fusion has found widespread applications in industrial and research sectors. The purpose of real time multi-sensor data fusion is to dynamically estimate an improved system model from a set of different data sources, i.e., sensors. This paper presented a systematic and unified real time sensor validation and fusion framework (RTSVFF) based on distributed autonomous sensors. The RTSVFF is an open architecture which consists of four layers - the transaction layer, the process fusion layer, the control layer, and the planning layer. This paradigm facilitates distribution of intelligence to the sensor level and sharing of information among sensors, controllers, and other devices in the system. The openness of the architecture also provides a platform to test different sensor validation and fusion algorithms and thus facilitates the selection of near optimal algorithms for specific sensor fusion application. In the version of the model presented in this paper, confidence weighted averaging is employed to address the dynamic system state issue noted above. The state is computed using an adaptive estimator and dynamic validation curve for numeric data fusion and a robust diagnostic map for decision level qualitative fusion. The framework is then applied to automatic monitoring of a gas-turbine engine, including a performance comparison of the proposed real-time sensor fusion algorithms and a traditional numerical weighted average.

Research in the area of vehicle control in intelligent vehicle highway systems (IVHS) has primarily focussed on either purely longitudinal or purely lateral control of vehicles. This paper introduces transition manoeuvres in such systems. These include vehicle merge, split and lane change procedures. A combined longitudinal and lateral vehicle model has been developed to study this problem. The paper discusses the development of control algorithms for controlling vehicles in such situations. The system does not have a well defined relative degree and hence the traditional I/O linearized approach is not possible without further modifications. The paper presents a method for using the structure of our outputs to solve the control problem. >

Miniaturized, distributed, networked sensors — called motes — promise to be smaller, less expensive and more versatile than other sensing alternatives. While these motes may have less individual reliability, high accuracy for the overall system is still desirable. Sensor validation and fusion algorithms provide a mechanism to extract pertinent information from massively sensed data and identify incipient sensor failures. Fuzzy approaches have proven to be effective and robust in challenging sensor validation and fusion applications. The algorithm developed in this paper — called mote-FVF (fuzzy validation and fusion) — uses a fuzzy approach to define the correlation among sensor readings, assign a confidence value to each of them, and perform a fused weighted average. A sensor network implementing mote-FVF for monitoring the illuminance in a dimmable fluorescent lighting environment empirically demonstrates the timely response of the algorithm to sudden changes in normal operating conditions while correctly isolating faulty sensor readings.

Accurate estimation of attitude, line of sight (LOS), and carrier-phase ambiguity is essential for the performance of Guidance, Navigation, and Control (GNC) systems operating under highly dynamic and uncertain conditions. Traditional sensor fusion and filtering methods, although effective, often require precise modeling and high-grade sensors to maintain robustness. This paper investigates a deep learning-based estimation framework for attitude, LOS, and GNSS ambiguity through the fusion of onboard sensors-GNSS, IMU, and semi-active laser (SAL)-and remote sensing information. Two neural network estimators are developed to address the most critical components of the navigation chain: GNSS carrier-phase ambiguity and gravity-vector reconstruction in the body frame, which are integrated into a hybrid guidance and navigation scheme for attitude and LOS determination. These learning-based estimators capture nonlinear relationships between sensor measurements and physical states, improving generalization under degraded conditions. The proposed system is validated in a six-degree-of-freedom (6-DoF) simulation environment that includes full aerodynamic modeling of artillery guided rockets. Comparative analyses demonstrate that the learning-based ambiguity and gravity estimators reduce overall latency, enhance estimation accuracy, and improve guidance precision compared to conventional networks. The results suggest that deep learning-based sensor fusion can serve as a practical foundation for next-generation low-cost GNC systems, enabling precise and reliable operation in scenarios with limited observability or sensor degradation.

Bdot probes and Rogowski coils are used in the measurement of transient magnetic fields and currents, respectively. They both share the mechanism of creating an induced electromotive force response via Faraday's law, which scales linearly with the pulsed magnetic field. High power capacitor direct current (DC) discharge systems release a single pulse of current that is both very high and very fast (≲1 ms). To capture these transient data and characterize these systems, high current tolerant and fast response time sensors are required. While these measuring devices have been well studied and utilized for almost 100 years, a comprehensive and detailed description of the custom design, calibration, and sensor fusion application of these tools for use in various pulsed DC capacitor value discharges is largely missing in the literature. Using robust analytical calculations, finite element analyses, and empirical methods, we have developed a sensor fusion protocol for current and magnetic field probes (with relative errors of ±13% and ±15%, respectively) for use in any geometry of high speed pulsed DC current calibrated capacitor discharge systems. This paper comprehensively outlines the design and sensor fusion methodologies that allow for the deployment of in-house built Bdot probes and Rogowski coils to a wide range of pulsed DC systems and demonstrates their use in a characteristic plasma environment.

The evolution of intelligent embedded systems has significantly transformed real-time surveillance and security applications by offering low-power, high-efficiency, and autonomous capabilities. Integrating machine learning algorithms, edge computing, and sensor fusion, these systems enable real-time threat detection, facial recognition, and event classification without reliance on cloud infrastructures. This article discusses the core architecture, capabilities, and applications of intelligent embedded systems in security surveillance, including recent developments in vision-based analytics and intelligent control systems. Moreover, it evaluates two fundamental implementation strategies: edge AI processing and multi-sensor integration, illustrating how embedded intelligence enhances surveillance accuracy, reduces latency, and ensures data privacy in sensitive environments.

An intelligent control system for thermal processing of natural biomaterials, based on machine vision, sensor fusion and neural network was developed. Experiments with ginseng drying showed advantages of machine vision for real-time imaging of morphological, colour and texture attributes, providing sufficient discriminatory information about biomaterial moisture and quality in the range of 3.2-0.1 g/g and temperatures from 30 to 50degC. Both moisture and quality was estimated by using neural network models: moisture with 6-8% error and quality with 10-16% error. Online estimates of moisture and quality were used for temperature control in pilot batch dryer. Testing of the intelligent control system with embedded machine-vision observer (IMAQ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TM</sup> Vision Builder) and controller (Lab View 7.0) showed stability and robustness, combined with high accuracy of temperature control. Multi-stage optimization of temperature with respect to quality allowed decrease of drying time from 240 to 90-110 hours with appropriate final quality.

The successful design and operation of autonomous or partially autonomous vehicles which are capable of traversing uncertain terrains requires the application of multiple sensors for tasks such as: local navigation, terrain evaluation, and feature recognition. In applications which include a teleoperation mode, there remains a serious need for local data reduction and decision-making to avoid the costly or impractical transmission of vast quantities of sensory data to a remote operator. There are several reasons to include multi-sensor fusion in a system design: (i) it allows the designer to combine intrinsically dissimilar data from several sensors to infer some property or properties of the environment, which no single sensor could otherwise obtain; and (ii) it allows the system designer to build a robust system by using partially redundant sources of noisy or otherwise uncertain information. At present, the epistemology of multi-sensor fusion is incomplete. Basic research topics include the following taskrelated issues: (i) the value of a sensor suite; (ii) the layout, positioning, and control of sensors (as agents); (iii) the marginal value of sensor information; the value of sensing-time versus some measure of error reduction, e.g., statistical efficiency; (iv) the role of sensor models, as well as a priori models of the environment; and (v) the calculus or calculi by which consistent sensor data are determined and combined. In our research on multi-sensor fusion, we have focused our attention on several of these issues. Specifically, we have studied the theory and application of robust fixed-size confidence intervals as a methodology for robust multi-sensor fusion. This work has been delineated and summarized in Kamberova and Mintz (1990) and McKendall and Mintz (1990a, 1990b). As we noted, this previous research focused on confidence intervals as opposed to the more general paradigm of confidence sets. The basic distinction here is between fusing data characterized by an uncertain scalar parameter versus fusing data characterized by an uncertain vector parameter, of known dimension. While the confidence set paradigm is more widely applicable, we initially chose to address the confidence interval paradigm, since we were simultaneously interested in addressing the issues of: (i) robustness to nonparametric uncertainty in the sampling distribution; and (ii) decision procedures for small sample sizes. Recently, we have begun to investigate the multivariate (confidence set) paradigm. The delineation of optimal confidence sets with fixed geometry is a very challenging problem when: (i) the a priori knowledge of the uncertain parameter vector is not modeled by a Cartesian product of intervals (a hyper-rectangle); and/or (ii) the noise components in the multivariate observations are not statistically independent. Although it may be difficult to obtain optimal fixed-geometry confidence sets, we have obtained some very promising approximation techniques. These approximation techniques provide: (i) statistically efficient fixed-size hyper-rectangular confidence sets for decision models with hyper-ellipsoidal parameter sets; and (ii) tight upper and lower bounds to the optimal confidence coefficients in the presence of both Gaussian and non-Gaussian sampling distributions. In both the univariate and multivariate paradigms, it is assumed that the a priori uncertainty in the parameter value can be delineated by a fixed set in an n-dimensional Euclidean space. It is further assumed, that while the sampling distribution is uncertain, the uncertainty class description for this distribution can be delineated by a given class of neighborhoods in the space of all n-dimensional probability distributions. The following sections of this paper: (i) present a paradigm for multi-sensor fusion based on position data; (ii) introduce statistical and set-valued models for sensor errors and a priori environmental uncertainty; (iii) explain the role of confidence sets in statistical decision theory and sensor fusion; (iv) relate fixed-size confidence intervals to fixedgeometry confidence sets; and (v) examine the performance of fixed-size hyper-cubic confidence sets for decision models with spherical parameter sets in the presence of both Gaussian and non-Gaussian sampling distributions

The global increase in life expectancy, driven by increased nutrition, healthcare, and living conditions, has resulted in a significant growth in the senior population, notably in Kazakhstan, where the number of people aged 60 and more currently exceeds 2.7 million. This demographic transition poses considerable public health problems, particularly the high prevalence and severity of falls in older persons. Falls are currently the second largest cause of unintentional mortality for more than 87% of the elderly, with 28-34% falling at least once per year. As the worldwide population of people aged 65 and more is predicted to exceed 1.5 billion by 2050, there is an urgent need for precise, real-time fall detection systems. This work uses standardized datasets to conduct a complete evaluation of three fall detection methodologies: posture recognition, YOLOv3-based detection, and deep learning. Deep learning models attained the best accuracy of 92.0% by utilizing their capacity to learn complex spatial-temporal information, but at the cost of increased computing burden and slower inference times (40 ms). YOLOv3 provided competitive accuracy (90.2%) and quicker processing (25 ms), making it suitable for real-time deployment, although with a larger false positive rate. Pose identification, while highly interpretable due to its emphasis on skeletal key points, performed less well in crowded or obscured settings. The findings highlight the possibility for combining the capabilities of each technique to create hybrid systems with adaptive, resource-efficient architectures. Future research should focus on sensor fusion and optimization methodologies to improve accuracy and scalability across a variety of scenarios.

An integration of deep learning and classical filtering techniques has been developed for achieving an accurate and robust positioning system. Despite significant technological advances, positioning systems often face fundamental challenges such as signal obstruction, multipath interference, and error drift. Although 3D map matching techniques using LiDAR sensors can mitigate these challenges, their practical implementation is limited by high costs, computational burdens, and dependency on pre-modeled environments. In response, recent research has increasingly focused on enhancing positioning systems through deep learning–based sensor fusion and adaptive filtering methods, emphasizing improvements in accuracy and operational robustness. In this paper, a seamless hierarchical context-aware intelligent positioning system (SH-CAIPS) is proposed by integrating sensor fusion, two-stage outlier mitigation gates, and deep reinforcement learning (DRL)-based noise scaling with an innovation-based adaptive Kalman filter (IAKF). As a result, the proposed positioning system can enhance robustness by adaptively adjusting measurement noise and dynamically updating the position based on the confidence score between model predictions and sensor measurements. From simulation results, it is confirmed that the positioning performances can be significantly improved based on the proposed SH-CAIPS compared with conventional positioning systems.

Time-synchronization technology can provide a common notion of time among the participating nodes on a network. This is essential not only for protocol operation for time-critical services but also for the time stamp for information included in the message. Precise time information can be very crucial for such things as autonomous driving because there are various sensor measurements from multiple cameras, and radio detection and ranging (radar) and light detection and ranging (LiDAR) are used for perceiving the current situation via sensor fusion. A well-known synchronization method, IEEE 1588, denoted as the precision time protocol (PTP), can be used for various applications. For in-vehicle networks of autonomous cars, we have to consider that the network may be comprised of subnetworks based on different protocols such as controller area network (CAN) and Ethernet. However, implementing PTPs on such heterogeneous vehicle networks causes several problems. First, the PTP procedure must be modified to be implement on a CAN network. Second, to calculate the delay and offset for PTP, the processing delay that occurs during message conversion must be considered. In this paper, we propose a synchronization method for CAN–Ethernet networks to solve these problems. The performance of the proposed synchronization method is evaluated by experiments on a real CAN–Ethernet network.

In this work, simple, generic models of chemical sensing are used to simulate sensor array data and to illustrate the impact on overall system performance that specific design choices impart. The ability of multisensor systems to perform multianalyte detection (i.e., distinguish multiple targets) is explored by examining the distinction between fundamental design-related limitations stemming from mismatching of mixture composition to fused sensor measurement spaces, and limitations that arise from measurement uncertainty. Insight on the limits and potential of sensor fusion to robustly address detection tasks in realistic field conditions can be gained through an examination of a) the underlying geometry of both the composition space of sources one hopes to elucidate and the measurement space a fused sensor system is capable of generating, and b) the informational impact of uncertainty on both of these spaces. For instance, what is the potential impact on sensor fusion in an open world scenario where unknown interferants may contaminate target signals? Under complex and dynamic backgrounds, decision rules may implicitly become non-optimal and adding sensors may increase the amount of conflicting information observed. This suggests that the manner in which a decision rule handles sensor conflict can be critical in leveraging sensor fusion for effective open world sensing, and becomes exponentially more important as more sensors are added. Results and design considerations for handling conflicting evidence in Bayes and Dempster-Shafer fusion frameworks are presented. Bayesian decision theory is used to provide an upper limit on detector performance of simulated sensor systems.

No rapid (airborne) reconnaissance and survey capability exists to detect, localize, and discriminate buried and partially buried unexploded ordnance and ordnance and explosive waste. In the last decade, several factors point to the need for a new approach in site survey and cleanup methodology including an increasing number of areas being surplused by the Department of Defense (DOD), the continual public awareness of the location and status of formerly used defense sites, and the reduction in DOD budgets. The need for a safe, accurate, economical, and time-expedient land survey and detection capability has never been so great. Fused Airborne Sensor Technology (FAST) is derived from a similar proven multi-sensor fusion approach for mine reconnaissance against buried sea mines, the most difficult mines to detect in underwater environments. The FAST concept will use a single helicopter platform for spatial and temporal co-registration and fusion of sensor data. Sensors will include a superconducting magnetic field gradiometer, a two-color infrared camera, ground-penetrating radar, and a visible spectrum camera. The combination of these sensors allows for detection, identification, and localization of a wide spectrum of targets. Multiple `looks' at the target and associated clutter by sensors detecting different physical phenomena will allow lowering individual sensor thresholds to ensure each sensor detects all targets and clutter of interest. Sensor fusion will then be used to reject the clutter. Since sensor characteristic information is derivable through testing for both targets of interest and known clutter, a `fingerprinting' process can be used to pull the targets from the typically clutter-rich background. The FAST concept will be presented with sensor and fusion methodology for application to a wide variety of buried and partially buried targets.© (1996) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

A significant problem in physiological state monitoring systems with single data channels is high rates of false alarm. In order to reduce false alarm probability, several data channels can be integrated to enhance system performance. In this work, we have investigated a sensor fusion methodology applicable to physiological state monitoring, which combines local decisions made from dispersed detectors. Difficulties in biophysical signal processing are associated with nonstationary signal patterns and individual characteristics of human physiology resulting in nonidentical observation statistics. Thus a two compartment design, a modified version of well established fusion theory in communication systems, is presented and applied to biological signal processing where we combine discrete wavelet transforms (DWT) with sensor fusion theory. The signals were decomposed in time-frequency domain by discrete wavelet transform (DWT) to capture localized transient features. Local decisions by wavelet power analysis are followed by global decisions at the data fusion center operating under an optimization criterion, i.e., minimum error criterion (MEC). We used three signals acquired from human volunteers exposed to high-G forces at the human centrifuge/dynamic flight simulator facility in Warminster, PA. The subjects performed anti-G straining maneuvers to protect them from the adverse effects of high-G forces. These maneuvers require muscular tensing and altered breathing patterns. We attempted to determine the subject's state by detecting the presence or absence of the voluntary anti-G straining maneuvers (AGSM). During the exposure to high G force the respiratory patterns, blood pressure and electroencephalogram (EEG) were measured to determine changes in the subject's state. Experimental results show that the probability of false alarm under MEC can be significantly reduced by applying the same rule found at local thresholds to all subjects, and MEC can be employed as a robust system to the case of defective/jammed local sensors. This implies the feasibility of our system for physiological state monitoring under a unifying criterion by biological information fusion, and provides significant guidance for algorithm development.