New Channel Errors Estimation Method for Multichannel SAR Based on Virtual Calibration Source

The multichannel synthetic aperture radar (SAR) system can effectively overcome the fundamental limitation between high-resolution and wide-swath. However, the unavoidable channel errors will result in a mismatch of the reconstruction filter and false targets in pairs. To address this issue, a novel channel errors calibration method is proposed based on the idea of minimizing the mean square error (MMSE) between the signal subspace and the space spanned by the practical steering vectors. The practical steering matrix of each Doppler bin can be constructed according to the Doppler spectrum. Compared with the time-domain correlation method, the proposed method no longer depends on the accuracy of the Doppler centroid estimation. Besides, compared with the orthogonal subspace method, the proposed method has the advantage of robustness under the condition of large samples by using the diagonal loading technique. To evaluate the performance, the results of simulation data and the real data acquired by the GF-3 dual-channel SAR system demonstrate that the proposed method has higher accuracy and more robustness than the conventional methods, especially in the case of low SNRs and high non-uniformity.

Star Sensor Denoising Algorithm Based on Edge Protection

Single-pixel noise commonly appearing in a star sensor can cause an unexpected error in centroid extraction. To overcome this problem, this paper proposes a star image denoising algorithm, named Improved Gaussian Side Window Filtering (IGSWF). Firstly, the IGSWF algorithm uses four special triangular Gaussian subtemplates for edge protection. Secondly, it exploits a reconstruction function based on the characteristic of stars and noise. The proposed IGSWF algorithm was successfully verified through simulations and evaluated in a star sensor. The experimental results indicated that the IGSWF algorithm performed better in preserving the shape of stars and eliminating the single-pixel noise and the centroid estimation error (CEE) value after using the IGSWF algorithm was eight times smaller than the original value, six times smaller than that after traditional window filtering, and three times smaller than that after the side window filtering.

An efficient distance estimation and centroid selection based on k-means clustering for small and large dataset

In this paper an efficient distance estimation and centroid selection based on k-means clustering for small and large dataset. Data pre-processing was performed first on the dataset. For the complete study and analysis PIMA Indian diabetes dataset was considered. After pre-processing distance and centroid estimation was performed. It includes initial selection based on randomization and then centroids updations were performed till the iterations or epochs determined. Distance measures used here are Euclidean distance (Ed), Pearson Coefficient distance (PCd), Chebyshev distance (Csd) and Canberra distance (Cad). The results indicate that all the distance algorithms performed approximately well in case of clustering but in terms of time Cad outperforms in comparison to other algorithms.