This paper describes a parametric representation of ultra-wideband radar signatures and its physical interpretation. Under the scattering theory of electromagnetic waves, a transfer function of radar scattering is factorized into three elementary parts and a radar signature with three parameters is derived. To use these parameters for radar target classification and identification, the relation between them and the response waveform is analytically revealed and numerically checked. The result indicates that distortion of the response waveform is sensitive to these parameters, and thus they can be expected to be used as features for radar target classification and identification.

Wireless sensor networks (WSNs) are ubiquitous in a wide range of applications requiring the monitoring of physical and environmental variables, such as target localization and identification. One of these applications is the sensing of ferromagnetic objects. In typical applications, the area to be monitored is typically large compared to the sensing radius of each magnetic sensor. On the other hand, the RF communication radii of WSN nodes are invariably larger than the sensing radii. This makes it economical and efficient to design and implement a sparse network in terms of sensor coverage, in which each point in the monitored area is likely to be covered by at most one sensor. This work aims at investigating the sensing potential and limitations (e.g. in terms of localization accuracy on the order of centimeters) of the Honeywell HMC 1002 2-axis magnetometer used in the context of a sparse magnetic WSN. The effect of environmental variations, such as temperature and power supply fluctuations, magnetic noise, and sensor sensitivity, on the target localization and identification performance of a magnetic WSN is examined based on experimental tests. Signal processing strategies that could enable an alternative to the typical “target present/absent” mode of using magnetic sensors, such as providing successive localization information in time, are discussed.

A method of calibration for GPR responses is introduced in order to extract a target response from GPR data. This calibration procedure eliminates undesirable waveform distortion that is caused by antenna characteristics and multiple scattering effects between the antennas and the ground surface. An application result to measured GPR data shows that undesirable late-time responses caused by the antenna characteristics and multiple scattering effects are removed, and that the target response is clearly reconstructed. This result demonstrates that the waveform calibration of GPR data is significant and essential for reliable target identification.

This brief paper proposes a method for calibration of GPR pulse waveforms that is effective for identification of buried objects in the ground and/or in concrete structures. This approach is based on the inverse filtering operation that eliminates the influence of GPR antenna characteristics, and a response from a flat metal plate is employed as a reference data for calibration. In order to evaluate the effectiveness of this approach, it is applied to actual experimental data measured by the UWB-GPR antennas. The results show the validity of the method and importance of the waveform calibration for target identification.

Identification of targets using sequential high range-resolution (HRR) radar signatures is studied. Classifiers are designed by using hidden Markov models (HMMs) to characterize the sequential information in multi-aspect HRR signatures. The higher-order moments together with the target dimension and the number of dominant wavefronts are used as features of the transient HRR waveforms. Classification results are presented for the ten-target MSTAR data set. The example results show that good classification performance and robustness are obtained, although the target features used here are very simple and compact compared with the complex HRR signatures.