Basics and first experiments demonstrating isolation improvements in the agile polarimetric FM-CW radar -PARSAX oleg a. krasnov, galina p. babur, zongbo wang, leo p. ligthart and fred van der zwanThe article describes the IRCTR PARSAX radar system, the S-band high-resolution Doppler polarimetric frequency modulated continuous wave (FM-CW) radar with dual-orthogonal sounding signals, which has the possibility to measure all elements of the radar target polarization scattering matrix simultaneously, in one sweep. The performance of such radar depends of the level of sounding signals orthogonality. In the main operational mode, the radar will be used for atmospheric remote sensing and polarimetric studies of ground-based targets. In such mode it will use a pair of synchronous linearly-frequency modulated (LFM) continuous signals with opposite frequency excursions of 50 MHz and duration of 1 ms. Such a combination of sounding signals has limited orthogonality even for huge BT-products, which produce cross-channel interferences. These interferences in case of radar scene with multiple pointed and distributed targets can completely degrade radar operational performance. In this article, we propose simple and effective technique to suppress interferences and to restore radar performance. The technique has been tested using simulation and has been implemented in multi-channel digital receiver of the PARSAX radar. The real radar measurements presented to illustrate effectiveness of cross-channel interferences suppression. The proposed technique can be useful not only for polarimetric radar design, but also in much wide radar applications, which use waveforms with high orthogonality.

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A novel pair of mutually-orthogonal radar waveforms is proposed for simultaneous polarimetric measurements (SPM). The proposed waveform pair exploits the orthogonality features between different subcarriers within a single orthogonal frequency division multiplexing (OFDM) chip, and is called interleaved OFDM (I-OFDM). With I-OFDM signals the isolation limitation defined by the bandwidth time (BT) product for frequently used waveform pairs like linear frequency modulated (LFM) and phase-code modulated (PCM) signals, can be overcome. The performance features of I-OFDM signals are theoretically analyzed; the application of I-OFDM signals and the corresponding signal processing scheme in SPM are presented. A significant increase of the polarimetric measurements efficiency by utilization of the proposed I-OFDM signals has been verified by experiments using an operational radar system.

Object detection in challenging situations such as scale variation, occlusion, and truncation depends not only on feature details but also on contextual information. Most previous networks emphasize too much on detailed feature extraction through deeper and wider networks, which may enhance the accuracy of object detection to certain extent. However, the feature details are easily being changed or washed out after passing through complicated filtering structures. To better handle these challenges, the paper proposes a novel framework, multiscale, deep inception convolutional neural network (MDCN), which focuses on wider and broader object regions by activating feature maps produced in the deep part of the network. Instead of incepting inner layers in the shallow part of the network, multi-scale inceptions are introduced in the deep layers. The proposed framework integrates the contextual information into the learning process through a single-shot network structure. It is computational efficient and avoids the hard training problem of previous macro feature extraction network designed for shallow layers. Extensive experiments demonstrate the effectiveness and superior performance of MDCN over the state-of-the-art models.