KEYWORDS: Radar, Doppler effect, Convolution, Matrices, Silicon, Analog electronics, Signal to noise ratio, Fourier transforms, Electrochemical etching, Active remote sensing
In this paper, the problem of adaptively selecting radar waveforms from a pre-dened library of waveforms is
addressed from an information theoretic perspective. Typically, radars transmit specic waveforms periodically,
to obtain for example, the range and Doppler of a target. Although modern radars are capable of transmitting
dierent waveforms during each consecutive period of transmission, it is hitherto unclear as to how these
waveforms must be scheduled to best understand the dynamic radar scene. In this paper, a new information
theoretic metric - directed information - is employed for waveform scheduling, and is shown to incorporate
the past radar returns to eectively schedule waveforms. We formulate this waveform scheduling problem in a
Gaussian framework, derive the corresponding maximization problem, and illustrate several special cases.
Detection of stationary targets in urban sensing and through-the-wall radar images using likelihood ratio test (LRT)
detectors has recently been considered in the literature. A shortcoming of the LRT detectors is that appropriate
probability density functions of target and clutter images need to be predefined. In most practical scenarios, this
information is not available a priori, and the mismatch of the assumed distribution functions degrades the performance of
the LRT. In this paper, we apply image segmentation techniques to radar images of scenes associated with urban
sensing. More specifically, the Otsu's method and maximum entropy segmentation are considered to aid in removing the
clutter, resulting in enhanced radar images with target regions only. Performance of the segmentation schemes is
evaluated and compared to that of the assumed LRT detector using real-data collected with Defence Research and
Development Canada's vehicle-borne through-the-wall radar imaging system. The results show that, although the
principles of segmentation and detection are different and serve disparate objectives, the segmentation techniques
outperform the LRT detector for the considered cases.
In urban sensing and through-the-wall radar, the existence of targets in proximity to walls or buildings results in
multipath returns. In this paper, we exploit the multipath from the walls to achieve target localization with a single
sensor. We deal with sparse scenes of single targets. A time-of-arrival wall association algorithm is derived to relate
target multipath returns to the respective walls, followed by a nonlinear least squares optimization to determine the target
location. Simulated and experimental data are used to validate the proposed algorithms.
KEYWORDS: Doppler effect, Radar, Data modeling, Target detection, Commercial off the shelf technology, Monte Carlo methods, Buildings, Inverse problems, Mathematical modeling, Ray tracing
In through-the-wall radar and urban sensing applications, detection, localization, and classification of targets are highly
desirable. The presence of the targets inside buildings, and in close proximity of walls, floors, and ceilings, leads to a
rich multipath environment. Multipaths can introduce false targets, thereby degrading target detection, localization, and
classification performances. A multipath model based on the principles of ray tracing is advocated in this paper. We
consider a diffused target moving in an enclosed urban structure being observed by a stationary Doppler radar. The
model is verified using both simulated and experimental data. Further, we address the inverse problem of identifying the
true Doppler peak given the Doppler spectrum, and show that the solution exists under partial knowledge of the angles
of arrival.
In this paper, we show that objects of interest, like pipes and cylinders, reminiscent of guns and rifles, can be classified
based on their acoustic vibration signatures. That is, if the acoustic returns are measurable, one can indeed classify
objects based on the physical principle of resonance. We consider classifiers which are both training independent and
those that are training dependent. The statistical classifier belongs to the former category, whereas, the neural network
classifier belongs to the latter. Comparisons between the two approaches are shown to render both classifiers as suitable
classifiers with small classification errors. We use the probability of correct classification as a measure of performance.
We demonstrate experimentally that unique features for classification are the resonant frequencies. The measured data
are obtained by exciting mechanical vibrations in pipes of different lengths and of different metals, for example, copper,
aluminum, and steel, and the measuring of the acoustic returns, using a simple microphone. Autoregressive modeling is
applied to the data to extract the respective object features, namely, the vibration frequencies and damping values. We
consider two classification problems, 1) Classifying objects comprised of different metals, and 2) Classifying objects of
the same material, but made of different lengths. It is shown that classification performance can be improved by
incorporating additional features such as the damping coefficients.
In this paper, we consider moving target detection and ranging for indoor sensing applications in urban environments. A
simple method is used to determine the unambiguous range of an indoor moving target using dual frequency continuous
wave (CW) radars. The dual-frequency radar employs two different carrier frequencies and simultaneously measures the
phase change with respect to time, for each of the two frequencies. It uses phase comparison of the radar returns at the
two frequencies to provide an estimate of the target range. The dual-frequency approach offers the benefit of reduced
complexity, fast computation time, real time target tracking, and localization in highly cluttered indoor scenes. We
present experimental results showing the effectiveness of the proposed method for indoor range estimation. Targets
undergoing different motions, such as constant Doppler, micro-Doppler and accelerating/decelerating translation profiles
are considered. The Doppler and the micro-Doppler signatures of the moving targets are also provided for each
experiment, which demonstrate the utility of such signatures for indoor target classification.
A simple through-the-wall radar system for moving target localization is proposed. This scheme is based on trilateration and range estimation from three independent dual-frequency CW radar units. The dual-frequency technique uses phase comparison of the transmitted and received CW signals to provide an estimate of the range-to-motion. The difference in frequency of the two CW carriers determines the maximum unambiguous range of the target. The range estimates from the three independent CW radar units are then combined using trilateration for target localization. The composition and thickness of the wall, its dielectric constant, and the angle of incidence all affect the characteristics of the signal propagating through the wall. The propagating signal slows down, encounters refraction, and is attenuated as it passes through the wall. If unaccounted for, the non-line-of-sight propagation due to refraction and the slowing down of the waves will introduce a bias in the estimated target location. Our scheme takes into account the presence of the wall and corrects for its refraction and speed of propagation effects. Proof of concept is provided using simulated data. The results show that the proposed dual-frequency CW radar system is able to correctly locate and track moving targets behind walls.
Micro-Doppler is generated from targets with simple harmonic motions, characterized by a sinusoidal instantaneous frequency in the time-frequency plane. This type of micro-Doppler arises from vibrating or rotating targets, which are commonly present in indoor settings. It is shown that the use of basis functions matched to the sinusoidal micro-Doppler signatures proves effective in identifying the micro-Doppler components in indoor imaging. These functions are optimum in the maximum likelihood (ML) sense. Asymptotic properties of the proposed linear decomposition are derived. The basis decomposition provides enhanced phase and frequency resolutions and is robust to noise. It is strongly dependent on the Bessel function (of the first kind) characteristics. Simulation results are presented to demonstrate the effect of non-orthogonality of the basis functions and the respective frequency and phase resolution properties of the decomposition.
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