This paper first studies the phase errors for fine-resolution spotlight mode SAR imaging and decomposes the phase errors into two kinds, one is caused by translation and the other by rotation. Mathematical analysis an...This paper first studies the phase errors for fine-resolution spotlight mode SAR imaging and decomposes the phase errors into two kinds, one is caused by translation and the other by rotation. Mathematical analysis and computer simulations show the above mentioned motion kinds and their corresponding damages on spotlight mode SAR imaging. Based on this analysis, a single PPP is introduced for spotlight mode SAR imaging with the PFA on the assumption that relative rotation between APC and imaged scene is uniform. The selected single point is used first to correct the quadratic and higher order phase errors and then to adjust the linear errors. After this compensation, the space-invariant phase errors caused by translation are almost corrected. Finally results are presented with the simulated data.展开更多
For a computer to perform intelligent information processing requires functions that can extract concepts from words, as humans do, and then associate those concepts with related concepts. In order to implement this a...For a computer to perform intelligent information processing requires functions that can extract concepts from words, as humans do, and then associate those concepts with related concepts. In order to implement this association function, it is necessary to quantify the degree of association between two concepts. In the present paper, we propose a method for quantifying degree of association focusing on the viewpoint that uses a concept base （a knowledge base that expresses concepts as a collection of pairs, each pair consisting of an attribute word used to describe the concept and a weighting that expresses the word＇s importance）. Here, ＂Viewpoint＂ is the perspective from which a concept is viewed; for example, consider the degree of association between ＂airplane＂ and ＂automobile＂, and the degree of association between ＂airplane＂ and ＂bird＂. From the viewpoint of ＂vehicle＂, ＂airplane＂ and ＂automobile＂ are highly related, while from the viewpoint of ＂flight＂, ＂airplane＂ and ＂bird＂ are highly related. We present herein a comparison of two methods for calculating degree of association focusing on the viewpoint, and demonstrate that the method involving modulation of attribute weightings based on viewpoint results in degree of association calculations that are closer to human senses.展开更多
基金Supported by the Aeronautic Scientific Foundation(No.98F5118)
文摘This paper first studies the phase errors for fine-resolution spotlight mode SAR imaging and decomposes the phase errors into two kinds, one is caused by translation and the other by rotation. Mathematical analysis and computer simulations show the above mentioned motion kinds and their corresponding damages on spotlight mode SAR imaging. Based on this analysis, a single PPP is introduced for spotlight mode SAR imaging with the PFA on the assumption that relative rotation between APC and imaged scene is uniform. The selected single point is used first to correct the quadratic and higher order phase errors and then to adjust the linear errors. After this compensation, the space-invariant phase errors caused by translation are almost corrected. Finally results are presented with the simulated data.
文摘For a computer to perform intelligent information processing requires functions that can extract concepts from words, as humans do, and then associate those concepts with related concepts. In order to implement this association function, it is necessary to quantify the degree of association between two concepts. In the present paper, we propose a method for quantifying degree of association focusing on the viewpoint that uses a concept base （a knowledge base that expresses concepts as a collection of pairs, each pair consisting of an attribute word used to describe the concept and a weighting that expresses the word＇s importance）. Here, ＂Viewpoint＂ is the perspective from which a concept is viewed; for example, consider the degree of association between ＂airplane＂ and ＂automobile＂, and the degree of association between ＂airplane＂ and ＂bird＂. From the viewpoint of ＂vehicle＂, ＂airplane＂ and ＂automobile＂ are highly related, while from the viewpoint of ＂flight＂, ＂airplane＂ and ＂bird＂ are highly related. We present herein a comparison of two methods for calculating degree of association focusing on the viewpoint, and demonstrate that the method involving modulation of attribute weightings based on viewpoint results in degree of association calculations that are closer to human senses.
