Ozgen, Selim
Sarıtaş, Elif
Orguner, Umut
Acar, Duygu
An algorithm to estimate the delay between two track files without time stamps in a distributed track fusion architecture is proposed. The main aim in delay estimation is to make the proceeding track fusion more accurately. The performance of the proposed algorithm is illustrated on an example in which two radars observe a common surveillance area with one radar sending the track information with a deterministic delay. Moreover, a method is proposed if the delay estimation algorithm presents a bad performance under deterministic delay assumption.


Delay and Peak-Age Violation Probability in Short-Packet Transmissions
Devassy, Rahul; Durisi, Giuseppe; Ferrante, Guido Carlo; Simeone, Osvaldo; Uysal, Elif (2018-06-22)
This paper investigates the distribution of delay and peak age of information in a communication system where packets, generated according to an independent and identically distributed Bernoulli process, are placed in a single-server queue with first-come first-served discipline and transmitted over an additive white Gaussian noise (AWGN) channel. When a packet is correctly decoded, the sender receives an instantaneous error-free positive acknowledgment, upon which it removes the packet from the buffer. In ...
Distributed Target Tracking with Propagation Delayed Measurements
Orguner, Umut (2009-07-09)
This paper presents a framework for making distributed target tracking under significant signal propagation delays between the target and the sensors. Each sensor considered makes estimation using its own measurements compensating for the involved signal propagation delay using a deterministic sampling based algorithm proposed previously. Since the individual sensor readings might not be enough to localize the target, the sensors have to share their estimates with each other at specific time instants and co...
Attitude estimation and magnetometer calibration using reconfigurable TRIAD plus filtering approach
Söken, Halil Ersin (Elsevier BV, 2020-04-01)
This paper proposes using TRIAD and Unscented Kalman Filter (UKF) algorithms in a sequential architecture as a part of a small satellite attitude estimation algorithm. This TRIAD+UKF approach can both provide accurate attitude estimates for the satellite and calibrate the magnetometers in real-time. A complete calibration model for the magnetometers, considering bias, scale factor, soft iron and nonorthogonality errors, is assumed. In the algorithm's first stage, the TRIAD uses the available vector measurem...
Neural network method for direction of arrival estimation with uniform cylindrical microstrip patch array
Caylar, S.; Dural, G.; Leblebicioğlu, Mehmet Kemal (Institution of Engineering and Technology (IET), 2010-02-01)
In this study, a new neural network algorithm is proposed for real-time multiple source tracking problem with cylindrical patch antenna array based on a previously reported Modified Neural Multiple Source Tracking (MN-MUST) algorithm. The proposed algorithm, namely cylindrical microstrip patch array modified neural multiple source tracking (CMN-MUST) algorithm implements MN-MUST algorithm on a cylindrical microstrip patch array structure. CMN-MUST algorithm uses the advantage of directive pattern of microst...
Residual based Adaptive Unscented Kalman filter for satellite attitude estimation
Söken, Halil Ersin (2012-12-01)
Determining the process noise covariance matrix in Kalman filtering applications is a difficult task especially for estimation problems of the high-dimensional states where states like biases or system parameters are included. This study introduces a simplistic residual based adaptation method for the Unscented Kalman Filter (UKF), which is used for small satellite attitude estimation. For a satellite with gyros and magnetometers onboard, the proposed adaptive UKF algorithm estimates the attitude as well as...
Citation Formats
S. Ozgen, E. Sarıtaş, U. Orguner, and D. Acar, “DELAY ESTIMATION BASED ON KINEMATIC TRACK INFORMATION WITHOUT TIME STAMPS,” 2014, Accessed: 00, 2020. [Online]. Available: