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This study proposes a new imaging algorithm for passive Global Navigation Satellite System-based synthetic aperture radar to improve range resolution. In the proposed algorithm, to reduce range compressed pulse mainlobe ambiguity caused by pseudo-random noise code frequency, second-order differentiation is carried out to the square of range compressed signal with respect to range delay lag. Because the carrier phase value is distorted in the squaring procedure, thereafter, a recovery processing is applied to each identified range compressed pulse for preserving the carrier phase for azimuth compression. Both simulation and experimental results indicate that compared to the conventional imaging algorithm, a significant enhancement in range resolution can be achieved by the proposed imaging algorithm. Meanwhile, from the results, the proposed algorithm will not degrade the compressed scene illumination level.

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Optimizing the combustion efficiency of a thermal power generating unit (TPGU) is a highly challenging and critical task in the energy industry. We develop a new data-driven AI system, namely DeepThermal, to optimize the combustion control strategy for TPGUs. At its core, is a new model-based offline reinforcement learning (RL) framework, called MORE, which leverages historical operational data of a TGPU to solve a highly complex constrained Markov decision process problem via purely offline training. In DeepThermal, we first learn a data-driven combustion process simulator from the offline dataset. The RL agent of MORE is then trained by combining real historical data as well as carefully filtered and processed simulation data through a novel restrictive exploration scheme. DeepThermal has been successfully deployed in four large coal-fired thermal power plants in China. Real-world experiments show that DeepThermal effectively improves the combustion efficiency of TPGUs. We also report the superior performance of MORE by comparing with the state-of-the-art algorithms on the standard offline RL benchmarks.