Current Issue : July - September Volume : 2018 Issue Number : 3 Articles : 5 Articles
The global navigation satellite system (GNSS) has been widely used in both military and civil fields. This study focuses on\nenhancing the carrier tracking ability of the phase-locked loop (PLL) in GNSS receivers for high-dynamic application. The\nPLL is a very popular and practical approach for tracking the GNSS carrier signal which propagates in the form of\nelectromagnetic wave. However, a PLL with constant coefficient would be suboptimal. Adaptive loop noise bandwidth\ntechniques proposed by previous researches can improve PLL tracking behavior to some extent. This paper presents a\nnovel PLL with an adaptive loop gain control filter (AGCF-PLL) that can provide an alternative. The mathematical model\nbased on second- and third-order PLL was derived. The error characteristics of the AGCF-PLL were also derived and\nanalyzed under different signal conditions, which mainly refers to the different combinations of carrier phase dynamic and\nsignal strength. Based on error characteristic curves, the optimal loop gain control method has been achieved to minimize\ntracking error. Finally, the completely adaptive loop gain control algorithm was designed. Comparable test results and\nanalysis using the new method, conventional PLL, FLL-assisted PLL, and FAB-LL demonstrate that the AGCF-PLL has\nstronger adaptability to high target movement dynamic...
We describe our automatic generative algorithm to create street addresses from satellite\nimages by learning and labeling roads, regions, and address cells. Currently, 75% of the world�s roads\nlack adequate street addressing systems. Recent geocoding initiatives tend to convert pure latitude\nand longitude information into a memorable form for unknown areas. However, settlements are\nidentified by streets, and such addressing schemes are not coherent with the road topology. Instead,\nwe propose a generative address design that maps the globe in accordance with streets. Our algorithm\nstarts with extracting roads from satellite imagery by utilizing deep learning. Then, it uniquely labels\nthe regions, roads, and structures using some graph- and proximity-based algorithms. We also\nextend our addressing scheme to (i) cover inaccessible areas following similar design principles;\n(ii) be inclusive and flexible for changes on the ground; and (iii) lead as a pioneer for a unified\nstreet-based global geodatabase. We present our results on an example of a developed city and\nmultiple undeveloped cities. We also compare productivity on the basis of current ad hoc and new\ncomplete addresses. We conclude by contrasting our generative addresses to current industrial and\nopen solutions....
We present various performance trades for multiantenna global navigation satellite system (GNSS) multisensor attitude estimation\nsystems. In particular, attitude estimation performance sensitivity to various error sources and system configurations is assessed.\nThis study is motivated by the need for system designers, scientists, and engineers of airborne astronomical and remote sensing\nplatforms to better determine which system configuration is most suitable for their specific application. In order to assess\nperformance trade-offs, the attitude estimation performance of various approaches is tested using a simulation that is based on a\nstratospheric balloon platform. For GNSS errors, attention is focused on multipath, receiver measurement noise, and carrierphase\nbreaks. For the remaining attitude sensors, different performance grades of sensors are assessed. Through a Monte Carlo\nsimulation, it is shown that, under typical conditions, sub-0.1-degree attitude accuracy is available when using multiple antenna\nGNSS data only, but that this accuracy can degrade to degree level in some environments warranting the inclusion of additional\nattitude sensors to maintain the desired level of accuracy. Further, we show that integrating inertial sensors is more valuable\nwhenever accurate pitch and roll estimates are critical....
This work investigates the possibility of using a novel evolutionary based technique as a solution for the navigation problem of a\nmobile robot in a strange environment which is based on Teaching-Learning-Based Optimization. TLBO is employed to train the\nparameters of ANFIS structure for optimal trajectory and minimum travelling time to reach the goal.The obtained results using\nthe suggested algorithm are validated by comparison with different results fromother intelligent algorithms such as particle swarm\noptimization (PSO), invasive weed optimization (IWO), and biogeography-based optimization (BBO). At the end, the quality of the\nobtained results extracted fromsimulations affirms TLBO-based ANFIS as an efficient alternative method for solving the navigation\nproblem of the mobile robot....
During the past several decades, there have been many reports of sightings of the\nivory-billed woodpecker (Campephilus principalis), but nobody has managed to obtain a clear photo,\nwhich is regarded as the standard form of evidence for documenting birds. A study was conducted\nin the Pearl River swamp in southeastern Louisiana to test the feasibility of searching for this elusive\nspecies and surveying its habitat using a DJI Phantom 3 Professional drone with a 4 K video camera.\nDrone images are of much higher quality than images that were previously obtained at much greater\nexpense during flights in a Cessna 172. The approach was found to be effective for searching for\nand inspecting trees that are potential foraging sites for woodpeckers and that might be suitable for\nnest and roost cavities. Large woodpeckers in flight are identifiable in video footage obtained from\nan altitude of 40 m, which was found to be sufficient to reliably avoid collisions with trees in the\nstudy area....
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