Lorenzo Lo Monte

Imaging Below Irregular Terrain Using RF Tomography

Radio-frequency (RF) tomography is extended for imaging underground structures and tunnels assuming rough terrain. The theory of RF tomography described in an earlier paper remains applicable, provided that a numerical Greens function is computed. An FFT-based and intrinsically parallel method for obtaining numerical Greens functions is described. This method is corroborated with explicit formulas and implemented for RF tomography. Simulated data computed using a finite-difference time-domain code are used to demonstrate performance.

RF tomography for Ground Penetrating Radar: Simulation and experimentation

Ground Penetrating Radar (GPR) is used for below ground imaging of shallow buried objects. In this paper, a new radio frequency (RF) tomography approach to signal processing is presented. Reconstructed 2D and 3D images of shallowly buried objects are computed via numerical discretization and inversion. Results from simulation analysis and experimentation are used to validate this method.

A dyadic target model for multistatic SAR/ISAR imaging

Current SAR/ISAR imaging algorithms rely upon the assumption that the area under observation consists of a superposition of infinitesimally small isotropic scatterers (i.e., the point scatterer model). This approximation fails to capture the real-world scattering mechanisms occurring within the targets under illumination. This paper proposes an imaging technique based upon the assumption that targets may be modeled as a superposition of infinitesimally small dipoles. The orientation of each dipole is encapsulated in a dyadic contrast function. The image reconstruction, i.e., retrieval of the dyadic reflectivity function from measured data, will provide information describing the shape and the direction of predominant edges of the target.

Exploitation of dominant scatterers for sidelobe suppression in radar tomography

Multistatic SAR/ISAR can be described and generalized using the principles of radar (or radio frequency) tomography. In radar tomography, distributed transmitters and receivers sense a region of interest using suitable waveforms. Using the principles of linear scattering (Born approximation), a (linear) relation exists between the measured returns and the shape of targets, and an image can be formed by inverting such relation. Due to the limited illumination and observation points, each point target will exhibit a spatially-varying point spread function (psf). A dominant scatterer having a large psf will inevitably mask the return of weak scatterers nearby. To mitigate this masking effect, the image is analyzed to identify dominant scatters. Then, these scatterers are modelled as dipole sources and included in the imaging formation as part of illumination points (i.e., new transmitters). When dominant scatterers are modelled as transmitters, their respective sidelobes are removed from the image, so that weak targets can be identified. Simulations and results demonstrate this concept.

Spectral and spatial diversity measurements in the Mumma Radar Lab

Due to rapid advancement in algorithms, high performance computing, digital electronics, and RF (radio frequency) hardware, the design of modern radar now incorporates both spectral and spatial diversity .Driving the state-of-the-art is shared-spectrum waveforms for the co-design of radar, communication and navigation systems, as well as thin spectrum and multiband signals. Spatially diverse multi-static radar, including RF Tomography [1], MIMO (multiple-input multiple-output) and DAR (distributed aperture radar) [2] are already emerging in the field. Calibration is challenging in traditional radar, and even more so in spectrally and spatially diverse systems. Instrumentation radar measurements are time consuming and costly. This paper proposes an alternative approach to the traditional sensor measurements that demand a quite zone in an anechoic chamber or an isolated outdoor radar range. Research in the MRL (Mumma Radar Lab) is currently focused on ISAR (inverse synthetic aperture radar), GPR (ground penetrating radar) and RF Tomography, all under calibrated conditions.

Range-Doppler-angle ambiguity function analysis in modern radar

Summary form only given. In this research, the impact of signal bandwidth on the ambiguity function is explored. While this effort focuses on monostatic radar, the concepts developed are fundamentally applicable to distributed sensor geometries. Consider an array of N antenna elements with non-uniform spacing, energized by a doublet, i.e., a single cycle of RF energy approximating the derivative of an impulse. Alternatively, an impulse may be considered, but no radiating element supports propagation of a DC current. In the simplest case, identical broadband signals, W(t), are radiated from each of the uniformly spaced elements. Broadside, these signals sum coherently, while they are dispersed off-broadside (angle θ), with the dispersion proportional to Nd sinθ / c, where d is the element spacing. For broadband signals W(t) of duration T, the far field signal is of duration T + Nd sinθ / c. In the computation of the ambiguity function, the effect of the cross correlation of the radiated waveform with a Doppler shifted replica of itself is dispersed by a factor of two. However, in the case of broad band radiation, the “pulse” duration is itself a function of angle. As such, the ambiguity function is severely distorted as a function of steering angle. In this paper, the impact of signal bandwidth on range-Doppler discrimination as a function of viewing angle will be discussed, as well as implications in MIMO radar.

Extraction of weak target features from radar tomographic imagery

Radar Tomography is the process of 3D reconstruction of a measurement domain using a multistatic distribution of transmitters and receivers. Geometric diversity of these elements increases the information contained in the measurements. The process of determining the permittivity and conductivity profile of the measurement domain, and therefore the shape of the target, from the scattered field measurements is an inverse problem. This is solved using principles of linear scattering (Born approximation), which lead to a linear relationship between the measured returns and the target shape. One limitation of radar tomography is that strong scatterer sidelobes in the measurement domain can interfere with the echoes from weak scatterers, decreasing the systems ability to detect certain target feature. In this paper, we propose a method to increase overall image quality by modelling the strong scatterers in the measurement domain as dipoles which behave as secondary transmitters. The purpose of this model is to reduce the effects of the sidelobes from the strong scatterers. We estimate the electromagnetic characteristics for each dipole in the model by representing the cells in the measurement domains image as dyadic functions. The eigenvalue and eigenvector for each cell represents phase and magnitude for the modelled dipole. The process of modelling targets as dipoles can be repeatedly applied, addressing one strong scatterer at a time, to decrease uncertainty in the measurement domain. Simulations and results demonstrate this concept.

Rediscovering monopulse radar with digital sum-difference beamforming

Classically, radar signals received from the monopulse quadrants are processed analogically to obtain sum (Σ) and difference (Δ) patterns. Exact Σ/Δ patterns are obtained only at the particular frequency in which the monopulse comparator is designed. In this paper, Σ/Δ patterns are computed via direct digizitazion of the signal coming from the quadrants, paving the way to the development of novel signal processing algorithms. Performance and limitations of digital monopulse processing will be discussed.

Extraction of Weak Scatterer Features Based on Multipath Exploitation in Radar Imagery

We proposed an improved solution to two problems. The first problem is caused by the sidelobe of the dominant scatterer masking a weak scatterer. The proposed solution is to suppress the dominant scatterer by modeling its electromagnetic effects as a secondary source or “extra dependent transmitter” in the measurement domain. The suppression of the domain scatterer reveals the presence of the weak scatterer based on exploitation of multipath effects. The second problem is linearizing the mathematical forward model in the measurement domain. Improving the quantity of the prediction, including multipath scattering effects (neglected under the Born approximation), allows us to solve the inverse problem. The multiple bounce (multipath) scattering effect is the interaction of more than one target in the scene. Modeling reflections from one target towards another as a transmitting dipole will add the multiple scattering effects to the scattering field and permit us to solve a linear inverse problem without sophisticated solutions of a nonlinear matrix in the forward model. Simulation results are presented to validate the concept.

A fast matched-filtered approach for GPR

This paper propose a fast, matched-filtered based imaging algorithm to detect below ground object To image below ground objects, a set of distributed transmitters and receivers are placed above the ground, or slightly buried. These transmitters radiate waveforms into the subsurface. The resulting wavefront impinges upon underground objects, scattering electromagnetic energy in all directions. Receivers collects the reflected electromagnetic signal, retrieve the phasor of the scattered signals, and transmit this information to systems for post-processing. After applying adaptive signal processing algorithms to collected data, an image of the buried objects can be reconstructed. Reconstructed 2D of buried objects are computed via numerical discretization and match filtering techniques. Match filtering technique is faster and it reduces computational power that required to process the collected data. The matched-filtered approach is easier to implement as compared to matrix inversion. Results from simulation analysis are used to validate this method.