In order to solve the problem of coherent signal subspace method (CSSM) depending on the estimated accuracy of signal subspace, a new direction of arrival (DOA) estimation method of wideband source, which is based on iterative adaptive spectral reconstruction, is proposed. Firstly, the wideband signals are divided into several narrowband signals of different frequency bins by discrete Fourier transformation (DFT). Then, the signal matched power spectrum in referenced frequency bins is computed, which can form the initial covariance matrix. Finally, the linear restrained minimum variance spectral (Capon spectral) of signals in other frequency bins are reconstructed using sequential iterative means, so the DOA can be estimated by the locations of spectral peaks. Theoretical analysis and simulation results show the proposed method based on the iterative spectral reconstruction for the covariance matrices of all sub-bands can avoid the problem of determining the signal subspace accurately with the coherent signal subspace method under the conditions of small samples and low signal to noise ratio (SNR), and it can also realize full dimensional focusing of different sub-band data, which can be applied to coherent sources and can significantly improve the accuracy of DOA estimation. 

Most of the direction of arrival (DOA) estimation methods often need the exact array manifold, but in actual applications, the gain and phase of the channels are usually inconsistent, which will cause the estimation invalid. A novel direction finding approach for mixed far-field and near-field signals with gain-phase error array is provided. Based on simplifying the space spectrum function by matrix transformation, DOA of far-field signals is obtained. Consequently, errors of the array are acquired according to the orthogonality of far-field signal subspace and noise subspace. Finally, DOA of near-field signals can be estimated. The method merely needs one-dimensional spectrum searching, so as to improve the computational efficiency on the premise of ensuring a certain accuracy, simulation results manifest the effectiveness of the method.

In order to efficiently mitigate error propagation and reduce computational complexity, this paper proposes a scheme for traditional cooperative networks, named as dual-threshold symbol selective demodulate-and-forward. In the scheme, two log likelihood ratio (LLR)-based thresholds are devised to measure the reliability of received signals for the relay and the destination, respectively. One of the threshold guarantees that the relay only forwards reliable symbols, thus less error will be propagated to the destination. The other threshold is used at the destination for determining the reliability of symbols received from the source. The destination will directly demodulate reliable symbols received from the source. Otherwise, when the symbols received from the source are not reliable, the maximum ratio combiner (MRC) is used to combine symbols received from the source and the relay. Closed-form expression of the bit error probability (BEP) of the proposed scheme is derived and analyzed under binary phase shift keying (BPSK) modulation. Then, the relationship and closed-form solutions of two LLR-based thresholds are derived. Simulation results prove that the theoretical BEP of the proposed scheme closely matches the simulated ones. The proposed scheme can achieve high performance with low computational complexity compared to existing schemes.

This paper is concerned with the recursive filtering problem for a class of discrete-time nonlinear stochastic systems in the presence of multi-sensor measurement delay. The delay occurs in a multi-step and asynchronous manner, and the delay probability of each sensor is assumed to be known or unknown. Firstly, a new model is constructed to describe the measurement process, based on which a new particle filter is developed with the ability to fuse multi-sensor information in the case of known delay probability. In addition, an online delay probability estimation module is introduced in the particle filtering framework, which leads to another new filter that can be implemented without the prior knowledge of delay probability. More importantly, since there is no complex iterative operation, the resulting filter can be implemented recursively and is suitable for many real-time applications. Simulation results show the effectiveness of the proposed filters.

An algorithm is presented for image prior combinations based blind deconvolution and applied to astronomical images. Using a hierarchical Bayesian framework, the unknown original image and all required algorithmic parameters are estimated simultaneously. Through utilization of variational Bayesian analysis, approximations of the posterior distributions on each unknown are obtained by minimizing the Kullback-Leibler (KL) distance, thus providing uncertainties of the estimates during the restoration process. Experimental results on both synthetic images and real astronomical images demonstrate that the proposed approaches compare favorably to other state-of-the-art reconstruction methods.