Intra-pulse modulation phase calibration is necessary in inverse synthetic aperture radar (ISAR) imaging of high-speed targets. Traditional intra-pulse phase error compensation strategies rarely handle the high-order and slow-time-variant phase components induced during the coherent processing interval. In this paper, a novel intra-pulse modulation phase calibration with a two-dimensional (2-D) parametric phase model is proposed. It models the intra-pulse phase errors as a 2-D time-variant polynomial with accommodation of both fast-time and slow-time modulation. Entropy minimization of high-resolution range profiles (HRRPs) is developed to retrieve the phase error parameters. Improved coordinate descent optimization solver is established by Levenberg-Marquardt (LM) algorithm in order to find the global optimum of entropy efficiently. Comparative experiments using both simulated and real measured data are performed to demonstrate the enhancements of the proposed algorithm. INDEX TERMS Entropy minimization, intra-pulse modulation, inverse synthetic aperture radar (ISAR), phase error compensation.

Low altitude, small radar cross-section (RCS), and slow speed (LSS) targets, for example small unmanned aerial vehicles (UAVs), have become increasingly significant. In this paper, we propose a new automatic target recognition (ATR) system and a complete ATR chain based on multi-dimensional features and multi-layer classifier system using L-band holographic staring radar. We consider all steps of the processing required to make a classification decision out of the raw radar data, mainly including preprocessing for the raw measured Doppler data including regularization and main frequency alignment, selection, and extraction of effective features in three dimensions of RCS, micro-Doppler, and motion, and multi-layer classifier system design. We design creatively a multi-layer classifier system based on directed acyclic graph. Helicopters, small fixed-wing, and rotary-wing UAVs, as well as birds are considered for classification, and the measured data collected by L-band radar demonstrates the effectiveness of the proposed complete ATR classification system. The results show that the ATR classification system based on multi-dimensional features and k-nearest neighbors (KNN) classifier is the best, compared with support vector machine (SVM) and back propagation (BP) neural networks, providing the capability of correct classification with a probability of around 97.62%.

Range alignment is an essential procedure in the translation motion compensation of inverse synthetic aperture radar imaging. Global optimization or maximum-correlation-based algorithms have been used to realize range alignment. However, it is still challenging to achieve range alignment in low signal-to-noise ratio scenarios, which are common in inverse synthetic aperture radar imaging. In this paper, a novel anti-noise range alignment approach is proposed. In this new method, the target motion is modeled as a uniformly accelerated motion during a short sub-aperture time. Minimum entropy optimization is implemented to estimate the motion parameters in each sub-aperture. These estimated parameters can be used to align the profiles of the current sub-aperture. Once the range profiles of each sub-aperture are aligned, the non-coherent accumulation gain is obtained by averaging all profiles in each sub-aperture, which can be used as valuable information. The accumulation and correlation method is applied to align the average range profiles of each sub-aperture because the former step focuses mainly on alignment within the sub-apertures. Experimental results based on simulated and real measured data demonstrate the effectiveness of the proposed algorithm in low signal-to-noise ratio scenarios.

Abstract Range alignment is an essential procedure in translation motion compensation of inverse synthetic aperture radar imaging. Global optimization or maximum correlation-based algorithms have been used to realize range alignment. However, it is still challenging to achieve range alignment in low signal-to-noise ratio scenarios, which are common in inverse synthetic aperture radar imaging. In this paper, a novel anti-noise range alignment approach is proposed. In this new method, the target's motion is modelled as a uniformly accelerated motion during a short sub-aperture time. Minimum entropy optimization is implemented to estimate the motion parameters in each sub-aperture. These estimated parameters can be used to align the profiles of the current sub-aperture. Once each sub-aperture's range profiles are aligned, the noncoherent accumulation gain is obtained by averaging all profiles in each sub-aperture, which can be used as valuable information. The accumulation and correlation method is applied to align the average range profiles of each sub-aperture for the reason that the former step mainly focuses on alignment within the sub-apertures. Experimental results based on simulated and real measured data demonstrate the effectiveness of the proposed algorithm in low signal-to-noise ratio scenarios.

Differential evolution (DE) algorithm is a heuristic approach that has a great ability to solve complex optimization and converges faster and with more certainty than many other acclaimed global optimization methods. In this paper, DE algorithm is applied to calibrate design seismic response spectra, the basic idea is that design response spectra and calculated response spectra are defined as objective function of characteristic parameters, the first turning point, the second turning point (characteristic period), the platform height and the attenuation index, of design response spectra, and the characteristic parameters are obtained by minimizing the objective function. Three pieces of the calculated response spectra for one real are calibrated as an application example. The obtained results demonstrate the effectiveness and efficiency of the proposed method.