Centering the phase-center of an antenna onto the rotational axis used to measure its radiation pattern is an iterative and time consuming process. To facilitate this process, an algorithm has been developed to calculate the phase-center offset from the axis of rotation of a 2D antenna pattern. The hybrid algorithm is comprised of a combination of the two-point method to calculate the offset along the antenna mainbeam, and an antisymmetry method is used to calculate offset perpendicular to the mainbeam direction. The algorithm is tested on the E-plane radiation pattern of a cylindrical horn antenna calculated using the HFSS electromagnetic simulation engine, radiating at 5GHz. The algorithm calculates the phase-center offset to within 15%. Because the algorithm analyzes the unwrapped phase of the radiation pattern, which it converts to offset distance, no ambiguity due to offsets greater than a wavelength exist. Using this algorithm, the phase-center of the antenna can be placed coincident to the axis of rotation after the first antenna pattern is measured and analyzed.
The purpose of this paper is to perform an analysis of RF (Radio Frequency) communication systems in a large electromagnetic environment to identify its susceptibility to jamming systems. We propose a new method that incorporates the use of reciprocity and superposition of the far-field radiation pattern of the RF system and the far-field radiation pattern of the jammer system. By using this method we can find the susceptibility pattern of RF systems with respect to the elevation and azimuth angles. A scenario was modeled with HFSS (High Frequency Structural Simulator) where the radiation pattern of the jammer was simulated as a cylindrical horn antenna. The RF jamming entry point used was a half-wave dipole inside a cavity with apertures that approximates a land-mobile vehicle, the dipole approximates a leaky coax cable. Because of the limitation of the simulation method, electrically large electromagnetic environments cannot be quickly simulated using HFSS’s finite element method (FEM). Therefore, the combination of the transmit antenna radiation pattern (horn) superimposed onto the receive antenna pattern (dipole) was performed in MATLAB. A 2D or 3D susceptibility pattern is obtained with respect to the azimuth and elevation angles. In addition, by incorporating the jamming equation into this algorithm, the received jamming power as a function of distance at the RF receiver Pr(Φr, θr) can be calculated. The received power depends on antenna properties, propagation factor and system losses. Test cases include: a cavity with four apertures, a cavity above an infinite ground plane, and a land-mobile vehicle approximation. By using the proposed algorithm a susceptibility analysis of RF systems in electromagnetic environments can be performed.
We propose a scheme for bistatic radar that uses a chaotic system to generate a wideband FM signal that is reconstructed at the receiver via a conventional phase lock loop. The setup for the bistatic radar includes a 3 state variable drive oscillator at the transmitter and a response oscillator at the receiver. The challenge is in synchronizing the response oscillator of the radar receiver utilizing a scaled version of the transmitted signal sr(t, x) = αst(t, x) where x is one of three driver oscillator state variables and α is the scaling factor that accounts for antenna gain, system losses, and space propagation. For FM, we also assume that the instantaneous frequency of the received signal, xs, is a scaled version of the Lorenz variable x. Since this additional scaling factor may not be known a priori, the response oscillator must be able to accept the scaled version of x as an input. Thus, to achieve synchronization we utilize a generalized projective synchronization technique that introduces a controller term –μe where μ is a control factor and e is the difference between the response state variable xs and a scaled x. Since demodulation of sr(t) is required to reconstruct the chaotic state variable x, the phase lock loop imposes a limit on the minimum error e. We verify through simulations that, once synchronization is achieved, the short-time correlation of x and xs is high and that the self-noise in the correlation is negligible over long periods of time.
In prior work, we showed that any one of the state variables of the Lorenz chaotic flow can be used effectively as the
instantaneous frequency of an FM signal. We further investigated a method to improve chaotic-wideband FM
signals for high resolution radar applications by introducing a compression factor to the Lorenz flow equations and
by varying two control parameters, namely ρ and β, to substantially increase the bandwidth of the signal. In this
paper, we obtain an empirical quadratic relationship between these two control parameters that yields a high
Lyapunov exponent which allows the Lorenz flow to quickly diverge from its initial state. This, in turn, results in
an FM signal with an agile center frequency that is also chaotic. A time-frequency analysis of the FM signal shows
that variable time-bandwidth products of the order of 105 and wide bandwidths of approximately 10 GHz are
achievable over short segments of the signal. Next, we compute the average ambiguity function for a large number
of short segments of the signal with positive range-Doppler coupling. The resulting ambiguity surface is shaped as a
set of mountain ridges that align with multiple range-Doppler coupling lines with low self-noise surrounding the
peak response. Similar results are achieved for segments of the signal with negative range-Doppler coupling. The
characteristics of the ambiguity surface are directly attributed to the frequency agility of the FM signal which could
be potentially used to counteract electronic counter measures aimed at traditional chirp radars.
KEYWORDS: Target detection, Detection and tracking algorithms, General packet radio service, Wavelets, Ground penetrating radar, Radio propagation, Signal processing, Wave propagation, Data modeling, Dielectrics
Ground Penetrating Radars (GPR) process electromagnetic reflections from subsurface interfaces
to characterize the subsurface and detect buried targets. Our objective is to test an inversion
algorithm that calculates the intrinsic impedance of subsurface media when the signal transmitted
is modeled as the first or second derivative of a large bandwidth Gaussian pulse. For this
purpose we model the subsurface as a transmission line with multiple segments, each having
different propagating velocities and characteristic impedances. We simulate the propagation and
reflection of the pulse from multilayered lossless and lossy media, and process the received
signal with a rectifier and filter subsystem to estimate the impulse response. We then run the
impulse response through the inversion algorithm in order to calculate the relative permittivity of
each subsurface layer. We show that the algorithm is able to detect targets using the primary
reflections, even though secondary reflections are sometimes required to maintain inversion
stability. We also demonstrate the importance of compensating for geometric spreading losses
and conductivity losses to accurately characterize each substrate layer and target. Such
compensation is not trivial in experimental data where electronic range delays can be arbitrary,
transmitted pulses often deviate from the theoretical models, and limited resolution can cause
ambiguity in the range of the targets.