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Cooperative Estimation of Agile Target Acceleration Using Extended Kalman Filter for Use in Advanced Guidance Laws
, M.Sc. Thesis Sharif University of Technology ; Saghafi, Fariborz (Supervisor)
Abstract
In this document, the cooperative estimation of states between two interceptors and one target is expressed in order to evaluate the effect of information sharing in different situations on the estimation performance. Due to the importance of the proper estimation of the target acceleration in advanced navigation rules, the main focus of the document is on a more precise estimation of the absolute states of the target. For this purpose, a filter based on the extended Kalman filter was used as an estimator, and to evaluate the promotion of the estimation performance, the combination of measured data was used in two different ways. Matlab Simulink is the simulation software used. The dynamics...
Change Point Detection in Molecular Carrier Based Nano Networks
,
M.Sc. Thesis
Sharif University of Technology
;
Nasiri Kenari, Masoumeh
(Supervisor)
;
Aminzadeh Gohari, Amin
(Co-Advisor)
Abstract
Molecular communication (MC) is an emerging communication paradigm, whereas molecules are used as information carriers to establish communication among elements in nano-meter to meter scales. In this thesis, we investigate the problem of detecting and monitoring changes (abnormality) based on molecular communication, using quickest change point detection scheme. We assume the distributions and parameters of the system are known. To this end, we consider a network of multiple sensors, each sensing its surrounding and employing On-Off-keying modulation for data transmission toward a fusion center (FC). An abnormality initiates randomly in time and location, and further propagates in the...
Multi-Sensor Data Fusion with Deep Learning in Semantic Segmentation
, M.Sc. Thesis Sharif University of Technology ; Kasaei, Shohreh (Supervisor)
Abstract
In image processing applications, sensors (Camera, LiDAR and Stereo) are essential for scene perception and Deep learning methods outperform most of the image processing tasks like 3D and 2D object detection and semantic segmentation. Different sensors are used in image processing tasks. Sensor fusion is using multiple sensors data to get better performance. Each sensor captures different data (e.g, color, texture, and depth). Some of them are distorted in inclement weather, intense illuminance changes, and dark environments which multi-sensor data fusion is used to overcome sensor weaknesses. One of the most important fields that sensor fusion used is Auto Driving cars (AD). Different...
Abnormality detection and monitoring in multi-sensor molecular communication
, Article IEEE Transactions on Molecular, Biological, and Multi-Scale Communications ; Volume 5, Issue 2 , 2019 , Pages 68-83 ; 23327804 (ISSN) ; Mirmohseni, M ; Nasiri Kenari, M ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2019
Abstract
In this paper, we investigate the problem of detecting and monitoring changes (abnormality) in molecular communication (MC), using the quickest change detection (QCD) schemes. The objective is to watch an environment using a sensor network and make decisions on the time and location of changes based on the received signals from sensors in the fusion center (FC). Such assumptions call for considering spatial and temporal correlations among sensors' transmitting signals. We use the framework of Partially Observable Markov Decision Processes (POMDPs) based on non-homogeneous Markov models. The metric in detection (stopping-time) scenario is to minimize the delay of announcing an abnormality...
A robust short-circuit fault diagnosis for high voltage DC power supply based on multisensor data fusion
, Article 10th International Power Electronics, Drive Systems and Technologies Conference, PEDSTC 2019, 12 February 2019 through 14 February 2019 ; 2019 , Pages 659-664 ; 9781538692547 (ISBN) ; Kaboli, S ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2019
Abstract
Short-circuit fault (SCF) detection is mandatory in a high voltage DC power supply (HVPS) to prevent fatal damage. The majority of converters employ a single sensor to detect the SCF. This attribute increases the interference vulnerability of the fault detection (FD) system in the presence of noise. Therefore, miss detections and false alarms are possible to occur. Miss detections and false alarms are harmful catastrophes in most applications. A commonly used method to suppress the noise impacts is using a low-bandwidth low-pass filter. However, the use of the low-bandwidth low-pass filter reduces the speed of FD due to the filter delay. This paper proposes a fast FD algorithm based on...
A Vacuum arc diagnosis method for the high voltage power supply of vacuum tubes
, Article 2019 International Vacuum Electronics Conference, IVEC 2019, 28 April 2019 through 1 May 2019 ; 2019 ; 9781538675342 (ISBN) ; Rahmanian, M ; Kaboli, S ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2019
Abstract
Vacuum tubes are widely used for various applications. These vacuum tubes are supplied by high voltage power supplies. The amount of delivered energy from the high voltage power supply to the vacuum tube is an important issue during the vacuum arc in the tube. The protection mechanism consists of a shunt crowbar which diverts the fault current from the tube to itself as a parallel path. Detection of the vacuum arc is crucial and only one sensor is usually employed to detect the vacuum arc. This characteristic intensifies the interference susceptibility of the vacuum arc diagnosis system in a noisy environment. As a result of the noise, the arc detection system can report false alarms. False...
Inertial motion capture accuracy improvement by kalman smoothing and dynamic networks
, Article IEEE Sensors Journal ; Volume 21, Issue 3 , 2021 , Pages 3722-3729 ; 1530437X (ISSN) ; Salarieh, H ; Alasty, A ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2021
Abstract
Localization-capable inertial motion capture algorithms rely on zero-velocity updates (ZUPT), usually as measurements in a Kalman filtering scheme, for position and attitude error control. As ZUPTs are only applicable during the static phases a link goes through, estimation errors grow during dynamic ones. This error growth may somewhat be mitigated by imposing biomechanical constraints in multi-sensor systems. Error reduction is also possible by optimization-based methods that incorporate the dynamic and static constraints governing the system behavior over a period of time (e.g. the dynamic network algorithm); when this period includes multiple static phases for a link, its estimation...
Practical distributed maneuvering target tracking using delayed information of heterogeneous unregistered sensors
, Article Signal Processing ; Volume 193 , 2022 ; 01651684 (ISSN) ; Haeri, M ; Sharif University of Technology
Elsevier B.V
2022
Abstract
Registration is the most consequential topic to be dealt with in a multi-sensor tracking system. On the other hand, providing satisfactory target acceleration estimation would enhance the performance of a ground-based air defense system encountering a maneuvering target. The main novelty of the present work is addressing a new scheme to solve the registration problem in a distributed network along with estimating accurate target acceleration, simultaneously. The details of coping with three common kinds of measurement, attitude, and location biases are explored concentrating on the effects of attitude bias as the main error source from the practical viewpoint. A modified iterated extended...
Analytical figures of merit for multisensor arrays
, Article ACS Sensors ; Volume 5, Issue 2 , 2020 , Pages 580-587 ; Kirsanov, D ; Sharif University of Technology
American Chemical Society
2020
Abstract
Multisensor arrays employing various sensing principles are a rapidly developing field of research as they allow simple and inexpensive quantification of various parameters in complex samples. Quantitative analysis with such systems is based on multivariate regression techniques, and deriving of traditional analytical figures of merit (e.g., sensitivity, selectivity, limit of detection, and limit of quantitation) for such systems is not obvious and straightforward. Nevertheless, it is absolutely needed for further development of the multisensor research field and for introducing these instruments into the general context of analytical chemistry. Here, we report on the protocol for...
How to synchronize and register an optical-inertial tracking system
, Article Applied Mechanics and Materials ; Volume 332 , 2013 , Pages 130-136 ; 16609336 (ISSN) ; 9783037857335 (ISBN) ; Akbar, M ; Farahmand, F ; Sharif University of Technology
2013
Abstract
Multi-sensor tracking is widely used for augmentation of tracking accuracy using data fusion. A basic requirement for such applications is the real time temporal synchronization and spatial registration of two sensory data. In this study a new method for time and space coordination of two tracking sensor measurements has been presented. For spatial registration we used a body coordinate system and then applied the effect of the level arm. The time synchronization was done based on least mean square (LMS) error method. This method was implemented to synchronize the position and orientation of an object using Inertial (1IMU) and Optical (Optotrak) tracking systems. The results of synchronized...
Adaptive neuro-fuzzy inference system in fuzzy measurement to track association
, Article Journal of Dynamic Systems, Measurement and Control, Transactions of the ASME ; Volume 132, Issue 2 , 2010 , Pages 1-8 ; 00220434 (ISSN) ; Sadati, N ; Sharif University of Technology
2010
Abstract
The main issue in a surveillance environment is the target tracking. The most important concern in this problem is the association of the various measurements with the existing target tracks. The fuzzy c-means data association (FCMDA) algorithm, based on the fuzzy c-means (FCM) algorithm, is an efficient solution for the problem of measurement to track association in a multisensor multitarget environment. It has a high accuracy in measurement to track association when targets are far from each other. However, its accuracy remains low when targets are close to one another. The FCMDA algorithm performance is usually lost in this environment, especially when measurement noise is high. In the...