airborne radar
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The airborne ice-penetrating radar (IPR) is an effective method used for ice sheet exploration and is widely applied for detecting the internal structures of ice sheets and for understanding the mechanism of ice flow and the characteristics of the bottom of ice sheets. However, because of the ambient influence and the limitations of the instruments, IPR data are frequently overlaid with noise and interference, which further impedes the extraction of layer features and the interpretation of the physical characteristics of the ice sheet. In this paper, we first applied conventional filtering methods to remove the feature noise and interference in IPR data. Furthermore, machine learning methods were introduced in IPR data processing for noise removal and feature extraction. Inspired by a comparison of the filtering methods and machine learning methods, we propose a fusion method combining both filtering methods and machine-learning-based methods to optimize the feature extraction in IPR data. Field data tests indicated that, under different conditions of IPR data, the application of different methods and strategies can improve the layer feature extraction.

Medium pulse repetition frequency (MPRF) is an important mode in airborne radar system. Since MPRF mode brings both Doppler and range ambiguities, it causes difficulty for the airborne radar to suppress ground or sea clutter. In recent years, it has been pointed out that the frequency diverse array (FDA) radar is capable of separating the range ambiguous clutter, which is helpful for the airborne radar in detecting weak moving targets originally buried in ambiguous clutter. To further improve the ambiguous clutter separation performance, an enhanced pre-STAP beamforming for range ambiguous clutter suppression is proposed for the vertical FDA planar array in this paper. With consideration of range dependence of the vertical spatial frequency, a series of pre-STAP beamformers are designed using a priori knowledge of platform and radar parameters. The notches of the beamformers are aligned with the ambiguous clutter to extract echoes from desired range region while suppressing clutter from ambiguous range regions. The notches can be widened by using covariance matrix tapering technique and the proposed method can improve the performance of range ambiguous clutter separation with limited degrees-of-freedom (DOFs). Simulation examples show the effectiveness of the proposed method.

The paper proposes an analytical study regarding airborne radar imaging performances and accounts for a down-looking radar system moving along parallel lines far, in terms of probing wavelength, from the investigated domain and collecting multi-frequency and multi-monostatic data. The imaging problem is formulated in a constant depth plane by exploiting the Born approximation. Hence, a linear inverse scattering problem is faced by considering both the Adjoint and the Truncated Singular Value Decomposition reconstruction schemes. Analytical and simulated results are provided to state how the achievable performances depend on the measurement configuration. These results are of practical usefulness because, in operative conditions, it is unfeasible to plan a flight grid made up by a high number of closely (in terms of probing wavelength) spaced lines. Hence, the understanding of how the availability of under-sampled data affects the radar imaging allows for a trade-off between operative data collection constrains and reliable reconstructions of the scenario under test.

Reliability allocation is a very important problem during early design and development phases of a system. There are several reliability allocation techniques which are used to achieve the target reliability. The feasibility of objectives (FOO) technique is one of them that is widely used to perform system reliability allocation. But this technique has two fundamental shortcomings. The first is the measurement scale and the second is that it does not consider the order weight of the reliability allocation factors. The prioritization of the factors is also an important topic in decision making. Practically, all factors in multi-criteria decision making (MCDM) are not in the same priority level. Hence, in decision making situation, it is usual for decision makers to consider different priority factors. So, considering the prioritization of the factors, a reliability allocation method is proposed here to overcome the shortcomings of the FOO technique. Also, a case study on reliability allocation in airborne radar system is considered here to verify the efficiency of the proposed approach. Finally, the results are calculated in different optimistic and pessimistic view point and compared with the FOO technique. This comparison exhibits the advantages and supremacy of the proposed approach.

We investigate a target detection probability (TDP) using path loss of an airborne radar based on air-to-air scenarios in anomalous atmospheric and weather environments. In the process of calculating the TDP, it is necessary to obtain the overall path loss including the anomalous atmospheric environment, gas attenuation, rainfall attenuation, and beam scanning loss. The path loss including the quad-linear refractivity model and other radar input parameters is simulated using the Advanced Refractive Effects Prediction System (AREPS) software along the range and the altitude. For the gas and rainfall attenuations, ITU-R models are used to consider the weather environment. In addition, the radar beam scan loss and a radar cross section (RCS) of the target are considered to estimate the TDP of the airborne long-range radar. The TDP performance is examined by employing the threshold evaluations of the total path loss derived from the detectability factor and the free-space radar range equation. Finally, the TDPs are obtained by assuming various air-to-air scenarios for the airborne radar in anomalous atmospheric and weather environments.