Simulasi Sistem Pendeteksi Objek Pada Pesawat Dengan Menggunakan Teknologi SAR (Synthetic Aperture Radar)

Untuk memetakan suatu objek berupa kontur suatu daerah akan terasa sulit jikamenggunakan sistem sensor pasif seperti kamera karena keterbatasannya untuk menembus awan, kabut dan cuaca yang tidak menentu. Oleh sebab itu diperlukannya teknologi yang lebih baik untuk dapat memetakan suatu objek dari atas permukaan bumi atau udara. Synthetic Aperture Radar (SAR) adalah teknik pemetaan dengan menggunakan radar untuk menghasilkan peta kontur bumi dengan resolusi tinggi, atau menggambarkan suatu objek serta menyajikan informasi dalam bentuk citra atau gambar. SAR dapat bekerja dalam kondisi cuaca apapun, baik dalam keadaan hujan, salju atau bahkan kabut sekalipun. Kemampuan SAR lainnya adalah untuk dapat mendeteksi objek dengan tingkat keakuratanyang cukup baik. Beradasarkan hal tersebut di atas, penelitian dan pengembangan teknologi SAR sangat diperlukan. Pada penelitian ini studi awal mengenai teknologi SAR telah dilakukan. Penelitian tersebut dimaksudkan untuk dapat melengkapi kemampuan drone atau unmanned aerial vehicle (UAV) baik untuk pencitraan kontur bumi maupun aktifitas terkait society 5.0. Sehingga aplikasinya dapat digunakan untuk keperluan pertanian modern, kehutanan, kelautan, dan kegiatan pengamatan perbatasan. Tujuannya adalah untuk mensimulasikan pendeteksian objek yang berada di permukaan tanah. Terdapat dua metoda pendeteksian objek berbasis SAR yang disimulasikan, yaitu Range Migration Algoritma dan Back Projection Algoritma. Simulasi ini dibangun dengan menggunakan komputer dengan prosesor AMD A8, memori 8 GB dan softperaware MATLAB 2019. Hasil simulasi memperlihatkan bahwa disain system untuk kedua algoritman tersebut dapat bekerja baik pada frekuensi 4 GHz dengan range resolusi 3m. Citra yang ditampilkan pada simulasi ini dalam bentuk 2-D. Sedangkan waktu pemrosesan rata-rata dari ke dua algoritma tersebut untuk dapat melakukan pendeteksian objek adalah 103.2 detik.

Ground Moving Target Imaging for Highly Squint SAR by Modified Minimum Entropy Algorithm and Spectrum Rotation

Ground moving target (GMT) is displaced and defocused in conventional synthetic aperture radar (SAR) image due to the residual phase error of non-cooperative GMT motion. In this study, a GMT imaging (GMTIm) method is proposed for highly squint SAR. As the squint angle become large, the displace and defocus effect of the GMT image become severe and the geometry distortion of the GMT image cannot be ignored. The proposed method first deduced the two-dimensional (2-D) frequency domain signal of the GMT and the bulk compression function of the Range Migration Algorithm (RMA) in highly squint SAR. Then GMT ROI data are extracted and a modified minimum entropy algorithm (MMEA) is proposed to refocus the GMT image. MMEA introduces the idea of bisection into the iteration process to converge more efficiently than the previous minimum entropy method. To overcome the geometry distortion of the GMT image, an equivalent squint angle spectrum rotation method is proposed. Finally, to suppress the GMT image sidelobe, the sparse characteristic of GMT is considered and a sparse enhancement method is adopted. The proposed method can realize GMTIm in highly squint SAR where the squint angle reaches to 75 degrees. The PSNR and ISLR of point target in highly squint SAR is close to that in side-looking SAR. The simulated point target data and ship data are used to validate the effectiveness of the proposed method.

Effectiveness of target signal migration compensation at compression filter output

The paper describes a case study with the target moving at unknown radial velocity. Exemplified by the frequency modulation signal, the study presents the analysis of specific features of channel multiplexing for the target range migration compensator at the compression filter output within pulse burst periods. Channel multiplexing allows to reduces losses in case of long-term signal integration. The solution effectiveness is estimated and a method is proposed for compensator channel multiplexing ensuring protection against unwanted signal expansion.

Long-range migration of centrioles to the apical surface of the olfactory epithelium

Olfactory sensory neurons (OSNs) in vertebrates detect odorants using multiple cilia, which protrude from the end of the dendrite and require centrioles for their formation. In mouse olfactory epithelium, the centrioles originate in progenitor cells near the basal lamina, often 50 to 100 μm from the apical surface. It is unknown how centrioles traverse this distance or mature to form cilia. Using high-resolution expansion microscopy, we found that centrioles migrate together, with multiple centrioles per group and multiple groups per OSN, during dendrite outgrowth. Centrioles were found by live imaging to migrate slowly, with a maximum rate of 0.18 μm/min. Centrioles in migrating groups were associated with microtubule nucleation factors, but acquired rootletin and appendages only in mature OSNs. The parental centriole had preexisting appendages, formed a single cilium prior to other centrioles, and retained its unique appendage configuration in the mature OSN. We developed an air-liquid interface explant culture system for OSNs and used it to show that centriole migration can be perturbed ex vivo by stabilizing microtubules. We consider these results in the context of a comprehensive model for centriole formation, migration, and maturation in this important sensory cell type.

BeiDou-Based Passive Radar Vessel Target Detection: Method and Experiment via Long-Time Optimized Integration

The BeiDou navigation satellite system shows its potential for passive radar vessel target detection owing to its global-scale coverage. However, the restrained power budget from BeiDou satellite hampers the detection performance. To solve this limitation, this paper proposes a long-time optimized integration method to obtain an adequate signal-to-noise ratio (SNR). During the long observation time, the range migration, intricate Doppler migration, and noncoherence characteristic bring challenges to the integration processing. In this paper, first, the keystone transform is applied to correct the range walk. Then, considering the noncoherence of the entire echo, the hybrid integration strategy is adopted. To remove the Doppler migration and correct the residual range migration, the long-time integration is modeled as an optimization problem. Finally, the particle swarm optimization (PSO) algorithm is applied to solve the optimization problem, after which the target echo over the long observation time is well concentrated, providing a reliable detection performance for the BeiDou-based passive radar. Its effectiveness is shown by the simulated and experimental results.

A Modified Range Migration Algorithm for FMCW SAR Signal Processing

The range migration algorithm (RMA) is an accurate imaging method for processing synthetic aperture radar (SAR) signals. However, this algorithm requires a big amount of computation when performing Stolt mapping. In high squint and wide beamwidth imaging, this operation also requires big memory size to store the result spectrum after Stolt mapping because the spectrum will be significantly expanded. A modified Stolt mapping that does not expand the signal spectrum while still maintains the processing accuracy is proposed in this paper to improve the efficiency of the RMA when processing frequency modulated continuous wave (FMCW) SAR signals. The modified RMA has roughly the same computational load and required the same memory size as the range Doppler algorithm (RDA) when processing FMCW SAR data. In extreme cases when the original spectrum is significantly modified by the Stolt mapping, the modified RMA achieves better focusing quality than the traditional RMA. Simulation and real data is used to verify the performance of the proposed RMA.