Sedat Camlica

Kalman filter based sinusoidal Pulse Repetition Interval tracking

Pulse Repetition Interval (PRI) tracking has particular interest and importance in Electronic Warfare. In this work, a specific part of PRI tracking, Sinusoidal PRI is studied. Extended Kalman Filter and Unscented Kalman Filter based algorithms have been developed. Moreover, simulations for performance comparison and comments are given at the end of the text.

UWB 3D near-field imaging with a sparse MIMO antenna array for concealed weapon detection

An ultra-wide-band (UWB) multiple-input multiple-output (MIMO) radar with a sparse array is designed and manufactured for three-dimensional near-field imaging applications such as concealed weapon detection. Contrary to existing mmW imaging radars, UWB components working in the lower microwave frequencies are more cost effective and yield images with resolutions satisfactory for contraband detection while not raising concerns related to personal privacy. A UWB sparse array provides resolution values equivalent to a fully populated array with a similar aperture size albeit with much fewer antenna elements, while yielding lower sidelobe levels compared to a narrowband sparse array. Performance of the proposed system is studied using a full-wave electromagnetic simulation environment which is capable of modelling the antenna array, the environment and the target in 3D while allowing modifications in mechanical and electrical properties of the materials. For image reconstruction, Kirchhoff-migration and back-projection algorithms are used and performances of these algorithms are compared. The effects of the spatial and temporal frequency response of the antenna array as well as array calibration on the image quality are also studied. A prototype of UWB MIMO sparse antenna array in Archimedean spiral configuration with an RF switch matrix is manufactured. Measurements are performed using a stepped-frequency continuous waveform (SFCW) transceiver for various metallic and non-metallic targets. It is observed that these targets are identifiable in the images formed based on measurement data. Consequently, promising concealed weapon detection performance is demonstrated with full-wave electromagnetic simulation and experimental results.

Sparsity based off-grid blind sensor calibration

Abstract Compressive Sensing (CS) based techniques generally discretize the signal space and assume that the signal has a sparse support restricted on the discretized grid points. This restriction of representing the signal on a discretized grid results in the off-grid problem which causes performance degradation in the reconstruction of signals. Sensor calibration is another issue which can cause performance degradation if not properly addressed. Calibration aims to reduce the disruptive effects of the phase and the gain biases. In this paper, a CS based blind calibration technique is proposed for the reconstruction of multiple off-grid signals. The proposed technique is capable of estimating the off-grid signals and correcting the gain and the phase biases due to insufficient calibration simultaneously. It is applied to off-grid frequency estimation and direction finding applications using blind calibration. Extensive simulation analyses are performed for both applications. Results show that the proposed technique has superior reconstruction performance.

PRI tracking using adaptive FIR filter

Pulse Repetition Interval tracking methods generally depend on the knowlegde of the PRI(Pulse Repetition Interval) type. In this work, adaptive FIR (Finite Inpulse Response) fitler based PRI trackers are developed which do not reqiure PRI type. Least Mean Squares (LMS) and Recursive Least Squares filter (RLS) based trackers are proposed. Solutions of some the practical problems of the PRI tracking are considered and performances of the filters are compared with simulations.

Performance of the interacting multiple model filter under high measurement rates

Interacting Multiple Model (IMM) is one of the most successful techniques proposed for maneuvering target tracking. The method works as a filter bank where each model represents a possible target motion and knows how the other models perform. One of the performance parameters, perhaps the least attended, is the predetermined Markov chain transition matrix. In this paper we present a study on how to determine the elements of the matrix and support it with simulation results.

Emitter localization with kalman filter using TDOA

Most of the closed form Time Differences of Arrival (TDOA) location estimators depends on the range between the emitter and the sensors. In this work, the variation of the range is expressed by a motion model. Using the result, a Kalman Filter based recursive method has been developed. Location estimation is updated using TDOA measurement sets from moving receivers. The Cramer-Rao Lower Bound (CRLB) is also derived. Simulation results are drawn along with the CRLB and comments are given at the end of the text.

Recursive geolocation with Time Differences of Arrival

Geolocation has particular interest and importance in Electronic Warfare. In this work, Time Difference of Arrival (TDOA) Geolocation is studied. A recursive least squares filter based algorithm has been developed. Location estimation is updated using TDOA measurement sets from moving receivers under the assumption of fixed emitter location. Moreover, simulation results and comments are given at the end of the text.