In this letter, we propose a novel preprocessing method to improve the transmission diversity smoothing (TDS) effect. Firstly, by applying different matched-filters to the receiving data, we acquire a series of virtual subarray data with identical array manifold. Then, a batch of cross-covariance matrices are constructed with different subarray data. On this basis, multiplying all obtained cross-covariance matrices and auto-covariance matrices by their own conjugate transpose, we get a series of high-order covariance matrices. Afterwards, averaging all these new matrices, we reconstruct a smoothed data covariance matrix for bearing estimation. Compared with the existing TDS based methods, the proposed one makes improvement by introducing cross-covariance matrices to smoothing, rather than further increasing the number of auto-covariance matrices. Theory shows that the proposed scheme well reserves the decorrelation effect of TDS and improves the estimation accuracy by enhancing the array signal to noise ratio. Numerical simulations verify the effectivity and superiority of the modified TDS method with the comparison of root mean square error for bearing estimation of coherent targets.

Privacy-preserving distributed data fusion is a pretreatment process in data mining involving security models. In this paper, we present a method of implementing multiparty data fusion, wherein redundant attributes of a same set of individuals are stored by multiple parties. In particular, the merged data does not suffer from background attacks or other reasoning attacks, and individual attributes are not leaked. To achieve this, we present three algorithms that satisfy K-anonymous and differential privacy. Experimental results on real datasets suggest that the proposed algorithm can effectively preserve information in data mining tasks.

Localization abstraction is a powerful technique that has long been a solution to the scalability problem of hardware model checking. However, computation resources are often inefficiently consumed during the repeated trial-and-errors between abstraction refinement engines and proof engines. To this end, many efforts have been made to combine the two independent techniques for better efficiency in recent years. In this paper, we present a novel model checking method that combines PDR (aka IC3) with a gate-level, hybrid abstraction technique to achieve further enhancement of scalability and performance for PDR. We implemented our work in ABC and evaluated it on the HWMCC13, HWMCC14 benchmark suites. The results show that our method substantially outperforms PDR as implemented in ABC and complements it on a large number of benchmark instances.