Detection Of Stationary Targets Research Articles

Evaluating the effects of material reflectivity and atmospheric attenuation on photonic radar performance in free space optical channels

Abstract The automotive industry is on the verge of embracing autonomous vehicles in the near future. In the pursuit of developing self-driving cars, photonic radars have emerged as a reliable sensor technology. These radars operate based on free space optical channels, but they are vulnerable to various atmospheric challenges. In this research, the objective is to examine the effect of material reflectivity on target recognition by utilizing photonic radar under various atmospheric conditions within free space optical channels. We explore four distinct scenarios representing targets with reflectivity ranging from 90 % to 20 %. The findings of this study indicate a decrease in received signal strength as reflectivity decreases across all analysed atmospheric conditions. The key findings include successful detection of stationary targets at a 1000-m range, the impact of target reflectivity on echo signal intensity and resolution, and the system’s effective performance in detecting targets even in dense fog conditions of up to 50 dB/km and are further validated through theoretical analysis.

Clutter Removal in Through-the-Wall Radar Imaging Using Sparse Autoencoder With Low-Rank Projection

Through-the-wall radar imaging is a sensing technology that can be used by first responders to see through obscure barriers during search-and-rescue missions or deployed by law enforcement and military personnel to maintain situational awareness during tactical operations. However, the strong reflections from the front wall and other obstacles render the detection of stationary targets very difficult. In this article, a learning-based approach is proposed to mitigate the effect of the wall and background clutter. A sparse autoencoder with a low-rank projection is developed to mitigate the wall clutter and recover the target signal. The weights of the proposed autoencoder are determined by solving an augmented Lagrange multiplier optimization problem, and the regularization parameters are estimated using the Bayesian optimization technique. Experiments using real data from a stepped-frequency radar were conducted to illustrate its effectiveness for wall clutter removal. The results show that the proposed method achieves superior performance compared with the existing approaches.

Single-Side Two-Location Spotlight Imaging for Building Based on MIMO Through-Wall-Radar.

Through-wall-radar imaging is of interest for mapping the wall layout of buildings and for the detection of stationary targets within buildings. In this paper, we present an easy single-side two-location spotlight imaging method for both wall layout mapping and stationary target detection by utilizing multiple-input multiple-output (MIMO) through-wall-radar. Rather than imaging for building walls directly, the images of all building corners are generated to speculate wall layout indirectly by successively deploying the MIMO through-wall-radar at two appropriate locations on only one side of the building and then carrying out spotlight imaging with two different squint-views. In addition to the ease of implementation, the single-side two-location squint-view detection also has two other advantages for stationary target imaging. The first one is the fewer multi-path ghosts, and the second one is the smaller region of side-lobe interferences from the corner images in comparison to the wall images. Based on Computer Simulation Technology (CST) electromagnetic simulation software, we provide multiple sets of validation results where multiple binary panorama images with clear images of all corners and stationary targets are obtained by combining two single-location images with the use of incoherent additive fusion and two-dimensional cell-averaging constant-false-alarm-rate (2D CA-CFAR) detection.

Through-The-Wall Detection With Gated FMCW Signals Using Optimized Patch-Like and Vivaldi Antennas

This paper presents the design and optimization of patch-like antennas for through-the-wall imaging (TTWI) radar applications in the frequency range 0.5–2 GHz. A basic antenna configuration is analyzed and modified through an optimization aiming at reducing the size of the antenna and focusing the radiated power in a single lobe to be directed toward the wall and the targets to be detected. Both the basic and the optimized antennas have been manufactured and tested. The optimized patch antennas and a conventional Vivaldi antenna have been successfully used in the frequency-modulated interrupted continuous wave (FMICW) radar system developed at Durham University. Results of a novel wall-removal technique for TTWI using numerical simulations and measurements aimed at the detection of stationary targets and people are presented.

Frequency-Modulated Interrupted Continuous Wave as Wall Removal Technique in Through-the-Wall Imaging

Undesired wall reflections in through-the-wall imaging can mask the return from actual targets and saturate and block the receiver. We propose frequency-modulated interrupted continuous wave (FMICW) signals as a novel wall removal technique and validate its effectiveness through numerical simulations and experiments performed using a radar system built for the purpose. FMICW waveforms appear to mitigate wall reflections and benefit the through-wall detection of stationary targets and of people moving or breathing behind different kinds of wall.

Invariant triangle-based stationary oil platform detection from multitemporal synthetic aperture radar data

An automatic algorithm for stationary oil platform detection from multitemporal syn- thetic aperture radar data is proposed. The proposed algorithm consists of the following two parts. (1) A two-parameter constant false-alarm rate (CFAR) algorithm is used to extract targets from the Environment Satellite (ENVISAT) advanced synthetic aperture radar (ASAR), in which the focus is to determine the appropriate parameters of CFAR, thus ensuring as few as possible false-alarm targets when sea-surface targets are effectively extracted. (2) A simple point cluster matching pattern is proposed based on an invariant triangle rule, by which targets extracted from multitemporal ENVISAT ASAR images are automatically matched for detection of stationary targets (e.g., oil platforms). This invariant triangle rule is that any three moving targets have an extremely low probability of maintaining a relative position in multitemporal images, whereas stationary targets can always maintain a fixed relative position. Even under high noise, this invariant triangle rule can be used to realize the target data matching with high robustness. The experiment shows that the false-alarm rate and the missing rate are relatively low when all the targets are detected. The proposed invariant-triangle-based point cluster matching pattern can conduct effective detection and monitoring of stationary oil platforms. © 2013 Society of Photo-Optical Instrumentation Engineers (SPIE) (DOI: 10.1117/1.JRS.7.073537)

Image Segmentations for Through-the-Wall Radar Target Detection

Detection of stationary targets using pixel-wise likelihood ratio test (LRT) detectors has been recently proposed for through-the-wall radar imaging (TWRI) applications. We employ image segmentation techniques, in lieu of LRT, for target detection in TWRI. More specifically the widely used between-class variance (BCV) thresholding, maximum entropy segmentation, and K-means clustering are considered to aid in removing the clutter, resulting in enhanced radar images with target regions only. For the case when multiple images of the same scene are available through diversity in polarization and/or vantage points around a building structure, we propose to use image fusion, following the image segmentation step, to generate an enhanced composite image. In particular, additive, multiplicative, and fuzzy logic fusion techniques are considered. The performance of the segmentation and fusion schemes is evaluated and compared with that of the assumed LRT detector using both electromagnetic (EM) modeling and real data collected in a laboratory environment. The results show that, although the principles of segmentation and detection are different, the segmentation techniques provide either comparable or improved performance over the LRT detector. Specifically, in the cases considered, the maximum entropy segmentation produces the best results for detection of targets inside building structures. For fusion of multiple segmented images of the same scene, the fuzzy logic fusion outperforms the other methods.

Impact of mobile node density on detection performance measures in a hybrid sensor network

We investigate the impact of mobile node density on several detection performance measures for stationary target detection by a hybrid sensor network consisting of both static and mobile nodes. Such hybrid sensor networks are becoming attractive with the recent advances in sensor nodes equipped with mobile platforms. However, adding a large number of mobile nodes to a sensor network for continuous coverage improvement might be expensive due to mobile node's higher energy consumptions compared to that with static nodes. Motivated by these, we investigate the trade-off between the density of mobile nodes and the network performance in a hybrid sensor network with respect to several performance measures of interest, when mobile nodes perform random mobility. We derive analytical (exact and/or approximate) formulae for detection probability, detection latency and mean first contact distance, by applying the theory of coverage processes and use them to evaluate the tradeoff between the fraction of mobile nodes and these performance measures. Analytical results presented in this paper give insights on how to select optimal network parameters in designing hybrid sensor networks to achieve desired performance requirements. Validity of the derived analytical results is verified via Monte-Carlo simulations.

Matched-Illumination Waveform Design for a Multistatic Through-the-Wall Radar System

We present the matched illumination waveform design for improved target detection in through-the-wall radar imaging and sensing applications. We consider a multistatic radar system for detection of stationary targets with known impulse responses behind walls. The stationary and slowly moving nature of typical indoor targets relaxes the orthogonality requirement on the waveforms, thereby allowing sequential transmissions from each transmitter with simultaneous reception at multiple receivers. The generalization of the matched illumination waveform design concept from a monostatic to a multistatic setting casts the indoor radar sensing problem in terms of multiple-input multiple-output (MIMO) operations and puts in context the offering of MIMO to urban sensing and imaging of targets in enclosed structures. Numerical electromagnetic modeling is used to provide the impulse response of typical behind-the-wall stationary targets, namely tables and humans, for different target orientations and at various incident and reflection angles. Simulation results depict an improvement in the signal-to-clutter-and-noise-ratio (SCNR) at the output of the matched filter receiver for multistatic radar as compared to monostatic operation.

Effect of inherent location uncertainty on detection of stationary targets in noisy image sequences.

The effect of inherent location uncertainty on the detection of stationary targets was determined in noisy image sequences. Targets were thick and thin projected cylinders mimicking arteries, catheters, and guide wires in medical imaging x-ray fluoroscopy. With the use of an adaptive forced-choice method, detection contrast sensitivity (the inverse of contrast) was measured both with and without marker cues that directed the attention of observers to the target location. With the probability correct clamped at 80%, contrast sensitivity increased an average of 77% when the marker was added to the thin-cylinder target. There was an insignificant effect on the thick cylinder. The large enhancement with the thin cylinder was obtained even though the target was located exactly in the center of a small panel, giving observers the impression that it was well localized. Psychometric functions consisting of d' plotted as a function of the square root of the signal-energy-to-noise-ratio gave a positive x intercept for the case of the thin cylinder without a marker. This x intercept, characteristic of uncertainty in other types of detection experiments, disappeared when the marker was added or when the thick cylinder was used. Inherent location uncertainty was further characterized by using four different markers with varying proximity to the target. Visual detection by human observers increased monotonically as the markers better localized the target. Human performance was modeled as a matched-filter detector with an uncertainty in the placement of the template. The removal of a location cue was modeled by introducing a location uncertainty of approximately equals 0.4 mm on the display device or only 7 microm on the retina, a size on the order of a single photoreceptor field. We conclude that detection is affected by target location uncertainty on the order of cellular dimensions, an observation with important implications for detection mechanisms in humans. In medical imaging, the results argue strongly for inclusion of high-contrast visualization markers on catheters and other interventional devices.

Coherent spatial filtering for SAR detection of stationary targets

Analysis of UHF synthetic aperture radar (SAR) images has revealed a spatially correlated phase structure over a resolved target such as a long wire or a large truck. This phase variation, approximated by a simple, range-dependent factor 4/spl pi/r//spl lambda/, results in a line-shaped image spatial spectrum. Such image phase and spectral features can be exploited by coherent spatial filtering to improve target-to-clutter ratios of large targets and stationary target detectability in a strong clutter environment.

Fractal interpolation of radar signatures for detecting stationary targets in ground clutter

The fundamentals of fractal geometry are reviewed, and its application to the millimeter-wave radar detection of stationary targets in a clutter background is described. First, high-range-resolution (HRR) profiles are used to determine the fractal interpolation functions needed to create fractal signatures. The fractal dimension is then determined for these signatures. On the basis of the value of the fractal dimension, the signature is declared to represent either a target of interest or clutter. The results of a CFAR (constant false alarm rate) simulation are presented to illustrate the performance of the method. They indicate that the fractal dimension feature used seems to be independent of amplitude. Thus, the fractal dimension information combined with traditional amplitude processing techniques will improve probabilities of detection.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>