A BCS microwave imaging algorithm for object detection and shape reconstruction tested with experimental data

Abstract

An approach based on the Green function and the Born approximation is used for impulsive radio ultra‐wideband microwave imaging, in which a permittivity map of the illuminated scenario is estimated using the scattered fields measured at several positions. Two algorithms are applied to this model and compared: the first one solves the inversion problem using a linear operator. The second one is based on the Bayesian compressive sensing technique, where the sparseness of the contrast function is introduced as a priori knowledge in order to improve the inverse mapping. In order to compare both methods, measurements in real scenarios are taken using an ultra‐wideband radar prototype. The results with real measurements illustrate that, for the considered scenarios, the Bayesian compressive sensing imaging algorithm has a better performance in terms of range and cross‐range resolution allowing object detection and shape reconstruction, with a reduced computational burden, and fewer space and frequency measurements, as compared to the linear operator.

Robust Target Classification Using UWB Sensing*

“…The set B in which the residues lie, may be associated with different look angles for the target. Suppose we have P families that satisfy (2), where the p-th family is characterized by N p natural frequencies z 1,p , • • • , z N p ,p . To simplify the notation, we denote…”

“…Additionally, we observed a significant improvement as the SNR increased for the NF classifier, which did not occur under the TD approach. The TD strategy relies strongly on the values of the residues associated with each natural frequency in model (2). Variations in residues, which are associated to experimental conditions such as incidence angle and polarization of the incident pulse, lead to sensible disparities in the time-domain signals within the same family, as shown in Fig.…”

“…The use of electromagnetic waves for noninvasive sensing has long been considered. Applications include radar and ground-penetrating radar (GPR) [1], [2], medical imaging [3], [4], disaster relief [5], bulk-material sensing [6], and through-the-wall imaging [7], [8]. An interesting application is the classification of targets according to their shape, position, or composition.…”

Real-Time Recognition Method of Video Basketball Technical Action Based on Target Detection Algorithm

“…Basketball Video Target Detection Algorithm. Before constructing target detection, a classifier for basketball video target detection is trained to form a detection and tracking process [11]. The training process can be expressed as follows:…”

An Enhanced Light Object Detection for Indiscernible Object in the Special Scene

“…The main function of PAN is to complement FPN and enhance the positioning information of the object. PAN adopts bilinear interpolation (Zilberstein et al,2021) instead of pooling for up-down sampling, which can effectively reduce the computation. SAT samples backbone layers to output FPN2, FPN3 and FPN4 to construct Pyramid Attention Network (PAN).…”