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A robust sensor bias estimation approach, named as the ridge least trimmed squares (RLTS), is proposed. Combing the advantages of ridge regression and least trimmed squares, RLTS can solve the sensor bias estimation problem with the presence of misassociations and ill-conditioning. Simulation results verify the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Aimed at the deficiency of traditional feature extraction techniques in radar emitter recognition, a novel deep feature extraction and recognition architecture is proposed. To fit into the model, the time-domain emitters are transformed into unique time-frequency images correspondingly. Since auto-encoders restrict the input data to be vector-form and convolutional model is hard to optimize, denoising auto-coders are stacked in a convolutional manner in the pre-training stage and the proposed framework is trained by greedy layer-wise algorithm. The optimized network parameters are employed to initialize convolutional neural networks. By layers of mapping and pooling, deep time-frequency features are extracted, which are fed into the collaborative representation-based classifier to implement classification task. Experimental results on simulated data validate the feasibility of the proposed architecture. Furthermore, compared with conventional shallow algorithms, the proposed one can obtain higher recognition accuracy and more robust performance. Taking advantage of collaborative representation, the proposed algorithm is more applicable to the small-sample-size case. [ABSTRACT FROM AUTHOR]

A novel framework of sub-Nyquist sampling and reconstruction for linear frequency modulation (LFM) radar signals based on the theory of blind compressed sensing (BCS) is proposed. This structure takes LFM signals as a sparse linear combination under an unknown transform order $$p$$ in fractional Fourier transform (FRFT) domain. First, making good use of energy concentration of LFM signal in the proper FRFT domain, we determine the optimal order $$p$$ which meets the convergence conditions under subsampling condition. Second, discrete fractional Fourier transform (DFRFT) sparse basis is constructed according to the specific sparse FRFT domain dominated by $$p$$ . Third, based on the DFRFT basis dictionary, using the random demodulator and block reconstruction algorithm, a LFM signal subsampling and reconstruction system is designed in the framework of BCS theory. With this system, the unknown LFM signal in radar system can be sampled at about 1/8 of Nyquist rate without the knowledge of priori sparse basis, but still can be reconstructed with overwhelming probability. Finally, simulations are taken on verifying the feasibility and efficiency of the proposed method, the novel framework can bring a new way to subsample and reconstruct LFM signals under the environment of non-collaboration. [ABSTRACT FROM AUTHOR]

The metal doping at the Pb2+ position provides improved luminescence performance for the cesium lead halide perovskites, and their fabrication methods assisted by microwave have attracted considerable attention due to the advantages of fast heating and low energy consumption. However, the postsynthetic doping strategy of the metal-doped perovskites driven by microwave heating still lacks systematic research. In this study, the assembly of CsPbBr3/CsPb2Br5 with a strong fluorescence peak at 523 nm is used as the CsPbBr3 precursor, and through the optimization of the postsynthetic conditions such as reaction temperatures, Mn2+/Pb2+ feeding ratios, and Mn2+ sources, the optimum Mn2+-doped product (CsPb(Cl/Br)3:Mn) is achieved. The exciton fluorescence peak of CsPb(Cl/Br)3:Mn is blueshifted to 437 nm, and an obvious fluorescence peak attributing to the doped Mn2+ ions at 597 nm is obtained. Both the CsPbBr3 precursor and CsPb(Cl/Br)3:Mn have high PLQY and stability because there are CsPb2Br5 microcubic crystals to well disperse and embed the CsPbBr3 nanocrystals (NCs) in the precursor, and after Mn2+-doping, this structure is maintained to form CsPb(Cl/Br)3:Mn NCs on the surface of their microcrystals. The exploration of preparation parameters in the microwave-assisted method provides insights into the enhanced color-tunable luminescence of the metal-doped perovskite materials. [ABSTRACT FROM AUTHOR]