Jean E. Piou

A Robust State Space Model for the Characterization of Extended Returns in Radar Target Signatures

Analysis of radar scattering from targets with curved boundaries, such as objects comprising cylindrical and conical shapes, is important to many aerospace applications. The radar return is composed of a well-characterized physical optics response in the illuminated region where the transmitter and receiver are not shadowed by the object, and a combination of modal responses (e.g., creeping waves and edge-diffracted fields) in the shadow region. The modal responses have longer down-range than scattering centers located on the object, and therefore, produce extended (or off-body) returns in ISAR images, which are not well-understood. However, these returns are strongly dependent on local features of the object, and thus contain valuable information with regard to the targets geometrical and physical composition. Multiple reflections from illuminated facets, as well as multiply diffracted waves, can also add coherently in the direction of the receiver and produce such returns. This paper applies a robust, coherent-processing system identification technique, originally developed for radar sensor fusion, to estimate amplitude and phase of the scatterers that characterize extended returns in the target signature. Examples are presented that highlight the extraction of creeping waves using measured data on a cone-sphere.

State-space spectral estimation of characteristic electromagnetic responses in wideband data

This letter utilizes a robust spectral estimation method, based on state-space control theory, to coherently process wideband frequency domain field data of any object and extract specific modal (or characteristic) responses associated with wave propagation along the object. The estimation problem is formulated in terms of well-known range processing used in radar imaging. Thus, the data is modeled in terms of complex sinusoids, whose amplitude is scaled by the decay constants of the modes and whose phase yields the range associated with scattering centers pertinent to modal propagation. Unlike other approaches that require the system poles to be inside the unit circle, the complex poles yielding the decay constants in the proposed approach can be located anywhere in the z-plane, and can vary with frequency, in order to capture the dynamic wideband behavior of the scattering mechanism. The method is illustrated by application to mode extraction for a cylindrically stratified dielectric scatterer.

Multipath height finding in the presence of interference

A new method is proposed for multipath target height finding with a non-coherent, narrowband radar in the presence of interference. The technique is based on an ultra-wideband fusion technique for sparse-band frequency processing originally developed for wideband imaging. It is illustrated with data collected during the mountaintop program.

Estimation of Cardiopulmonary Parameters From Ultra Wideband Radar Measurements Using the State Space Method

Ultra wideband (UWB) Doppler radar has many biomedical applications, including remote diagnosis of cardiovascular disease, triage and real-time personnel tracking in rescue missions. It uses narrow pulses to probe the human body and detect tiny cardiopulmonary movements by spectral analysis of the backscattered electromagnetic (EM) field. With the help of super-resolution spectral algorithms, UWB radar is capable of increased accuracy for estimating vital signs such as heart and respiration rates in adverse signal-to-noise conditions. A major challenge for biomedical radar systems is detecting the heartbeat of a subject with high accuracy, because of minute thorax motion (less than 0.5 mm) caused by the heartbeat. The problem becomes compounded by EM clutter and noise in the environment. In this paper, we introduce a new algorithm based on the state space method (SSM) for the extraction of cardiac and respiration rates from UWB radar measurements. SSM produces range-dependent system poles that can be classified parametrically with spectral peaks at the cardiac and respiratory frequencies. It is shown that SSM produces accurate estimates of the vital signs without producing harmonics and inter-modulation products that plague signal resolution in widely used FFT spectrograms.

Signal model to extract intrinsic parameters of high-Q dielectric resonators from noisy measurements

We report characterization of an open dielectric resonator with Q of 4000 to 7000 in the 25-40 GHz frequency range. Because of its small size, the measured parameters are very sensitive to background noise contributed by the coupling mechanism, package modes, radiation, etc. Therefore, it becomes important to properly calibrate out this parasitic influence in order to accurately measure the unloaded Q factor of the resonator. We report an accurate software calibration procedure based on a state-space spectral estimation algorithm, which effectively filters out the background noise and facilitates linear extraction of the unloaded Q

A super-resolution method for extraction of modal responses in wideband data

The paper presents a spectral estimation method to process coherently the wideband frequency domain field data of any object, and extract specific modal responses associated with wave propagation along the object. The problem is formulated in terms of well-known range processing used in radar waveform analysis. Thus, the data is modeled in terms of complex sinusoids, whose phase yields the decay/growth constants and the range associated with discrete scattering centers. Unlike previous approaches, the resulting amplitude and decay/growth constants are made frequency-dependent to capture the dynamic wideband behavior of the scattering mechanism. The method is illustrated by application to mode extraction for a cylindrically stratified scatterer.

Overview of vital sign detection-simulation and measurements

Commercial EM software packages are not suitable to detect cardiac and respiratory motion of the chest cavity, crucial to vital sign detection, in the study of electromagnetic (EM) wave interaction with tissues using utilized phantom models. The tissue constitutive parameters are time dependent, and are strong function of the volume and physical composition of the tissue layers near the surface due to the associated physiological motion. In this paper, we consider a human body phantom model that includes physiological motion, and a state-space method to extract cardiac and respiration rates. The method is applied to simulated data as well as 3 GHz UWB radar measurements on a sedentary subject. It is shown that the state-space method accurately estimates vital signs without producing harmonics and inter-modulation products that plague signal resolution in commonly used auto-correlated FFT spectrograms relying on peak detection with errors less than 2%.

Representation of Electromagnetic Responses in Time Domain Using State-Space System Identification Method

This paper presents a control system methodology based on system identification (SI) to derive state matrices and system dynamics for the extrapolation of simulated time-domain electromagnetic (EM) responses using both input and output data. The method is applied to model transient responses for a dielectric resonator and a wideband slot antenna computed by the finite-integration method using non-Gaussian excitation pulses typically employed in digital signals. Using state-space SI formulation, a compact representation of the output time-domain signal is derived, including the early-time transient responses, and its numerical implementation depicts no instability in late-time responses plotted well beyond the steady state. Furthermore, all the system poles are found to lie within the unit circle in the complex plane. Excellent model corroboration is achieved with independently computed frequency domain data or measured results using modest computer resources.

Parametric extraction of cardiac and respiratory rates from radar measurements of the human body

Cardiac and respiratory motion of the chest cavity, crucial to vital sign detection, has not been considered in the study of electromagnetic (EM) wave interaction with tissues using phantom models commonly found in commercial EM software packages. The volume and physical composition of the tissue layers near the surface change with physiological motion, thereby imparting time dependence to the constitutive parameters. In this paper, we consider a human body phantom model that includes physiological motion and develop a new algorithm based on the state-space method to extract cardiac and respiration rates. The method is applied to simulated data as well as 24 GHz radar measurements on a sedentary subject. It is shown that the state-space method accurately estimates vital signs without producing harmonics and inter-modulation products that plague signal resolution in commonly used auto-correlated FFT spectrograms relying on peak detection.

A robust state space model encompassing early-time transients for electromagnetic signal extrapolation

This paper presents a control systems methodology, based on the state space method (SSM), to derive rational transfer function models from time-domain simulated data. Unlike existing methods such as Pronys and pencil of functions, which discard a significant part of the early-time transient response due to instability in model extrapolation, the proposed SSM produces causal, stable, low-order parametric representations for the entire signal using a sequence that includes early-time transients. SSM is applied to model time-domain signatures of highly resonant circuits, such as notch filters and dielectric resonators, and it is shown that excellent model corroboration can be achieved using modest resources.