Vehicles driving in urban canyons are always confronted with a degraded Global Navigation Satellite System (GNSS) signal environment. The surrounding obstacles may cause signal reflections or blockages, which lead to large multipath noises and intermittent GNSS reception. Under these circumstances, it is not feasible to use conventional real-time kinematic (RTK) algorithms to maintain high-precision performance for positioning. In order to meet the special requirements of safety-critical applications under non-ideal observation conditions, a novel tightly coupled RTK/Inertial Navigation System (INS) algorithm is proposed in this paper, which can provide accurate and reliable positioning results continuously. Our integrated RTK/INS algorithm has three features. Firstly, INS measurements are used to help search for integer ambiguities in the position domain. INS solutions can provide a more accurate initial location and a more efficient search region. Secondly, the criterion for determining whether a candidate position is the correct solution is only related to the fractional value of the carrier-phase measurement. Thus, the new algorithm is immune to cycle slips as well as large pseudorange noises. Thirdly, our algorithm can provide more accurate ranging information than the pseudorange, even though it may not necessarily be fixed successfully, because it selects the weighted ambiguity solution as the result rather than the candidate point with maximum probability. The proposed algorithm is demonstrated on both simulated and real datasets. Compared with single epoch RTK and conventional tightly coupled RTK/INS integrations that search integer ambiguities in the ambiguity domain, our method attains better accuracy and stability in a simulated environment. Moreover, the real experimental results are presented to validate the performance of the new integrated navigation algorithm.

When configuring a delay tolerant network (DTN), there are many aspects that need to be taken into consideration for an effective and efficient network. One aspect is a buffer management strategy. Buffer strategies are used to determine which packets need to be forwarded or dropped. This paper will focus on the variety of buffer management strategies available, providing a comprehensive survey and analysis. They have all been taken into consideration, evaluated, and then classified into different categories based on their features.

Speed planning in rugged terrain poses challenges due to various constraints, such as traverse efficiency, dynamics, safety, and smoothness. This letter introduces a framework based on Constrained Reinforcement Learning (CRL) that considers all these constraints. In addition, extracting the terrain information as a constraint to be added to the CRL is also a barrier. In this letter, a terrain constraint extraction module is designed to quantify the semantic and geometric attributes of the terrain by estimating maximum safe speed. All networks are trained on simulators or datasets and eventually deployed on a real mobile robot. To continuously improve the planning performance and mitigate the error caused by the simulator-reality gap, we propose a feedback structure for detecting and preserving critical experiences during the testing process. The experiments in the simulator and the real robot demonstrate that our method can reduce the frequency of dangerous status by 45% and improve up to 71% smoothness.

New foreground digital calibration methods are proposed for successive approximation register (SAR) analog-to-digital converters (ADCs) to reduce the performance loss due to digital-to-analog converter (DAC) mismatch. A set of calibration coefficients which represent the actual weights of the specified patterns is obtained during foreground calibration, and then used during normal operation to correct the SAR ADC output using only a few multiplexers and an accumulator. The simulation results of a 10-bit SAR ADC with the proposed calibration methods show almost 10 effective number of bits (ENOB) with up to 0.5 % binary-weighted DAC mismatch, and more than 12 dB SNDR improvement for even larger DAC mismatch, compared to that without calibration.

In order to solve the problem that the application mechanism of scattering information of background of surface features in the design process of altimeter fuze is not clear, a simulation method of altimeter fuze based on Ulaby model is proposed. This method is based on the Ulaby model to model the target characteristics of background of surface features. The target characteristics information is used as the input information of fuze detection in the missile target encounter process, adopting the digital signal processing method. The response characteristic model of fuze target detection is established, and the response simulation of fuze detection to the time-varying scattering characteristics of background of surface features in the process of missile target encounter is carried out, and the response characteristics of echo and IF signal processing are clearly reproduced. The experimental results show that Ulaby model can be used to distinguish different background of surface features types. The target type information is added as another dimensional fuze detection information, and the detection accuracy is further improved by combining the height information.