Theoretical analysis of polarization recognition between chaff cloud and ship

Aimed at the difficulty in finding an efficient method to depress and recognize chaff in both time and frequency domain,this paper tries to recognize chaff and target such as warship in polarization domain under the theoretical analysis of the polarization scattering cross section of chaff.In order to find target more exactly,non-linear polarization transformation and polarization smoothness are applied to process the polarization information of chaff and ship target.The resulting recognition is proved to be effective by simulation.

Effects of wide band interference to GPS C/A code

The NAVSTAR Global Positioning System (GPS), a networked system providing three-dimension user position and timing, is widely used during the past ten years. However, interferences to GPS introduce great dangers to GPS users. This paper gives an assessment of interferences to the GPS positioning signal. The work is mainly concentrated on effects of wide band interferences. Effects of traditional interferences such as continuous wave (CW), as well as broadband noise and pulse interference are studied. Furthermore, according to the GPS’s signal characteristics, another kind of wideband interferences, named m sequence interference in this paper, is also studied. Simulation results show that m sequence interference is the most harmful to GPS C/A code if special m sequence is adopted and thus special countermeasures should be used to eliminate this effect.

Sub-pixel mapping method based on BP neural network

A new sub-pixel mapping method based on BP neural network is proposed in order to determine the spatial distribution of class components in each mixed pixel.The network was used to train a model that describes the relationship between spatial distribution of target components in mixed pixel and its neighboring information.Then the sub-pixel scaled target could be predicted by the trained model.In order to improve the performance of BP network,BP learning algorithm with momentum was employed.The experiments were conducted both on synthetic images and on hyperspectral imagery(HSI).The results prove that this method is capable of estimating land covers fairly accurately and has a great superiority over some other sub-pixel mapping methods in terms of computational complexity.

Speed-up for N-FINDR algorithm

N-FINDR is a very popular algorithm of endmember (EM) extraction for its automated property and high efficiency. Unfortunately, innumerable volume calculation, initial random selection of EMs and blind searching for EMs lead to low speed of the algorithm and limit the applications of the algorithm. So in this paper two measures are proposed to speed up the algorithm. One of the measures is substituting distance calculation for volume calculation. Thus the avoidance of volume calculation greatly decreases the computational cost. The other measure is resorting dataset in terms of pixel purity likelihood based on pixel purity index (PPI) concept. Then, initial EMs can be selected well-founded and a fast searching for EMs is achieved. Numerical experiments show that the two measures speed up the original algorithm hundreds of times as the number of EMs is more than ten.

Unsupervised linear spectral mixture analysis with AVIRIS data

A new algorithm for unsupervised hyperspectral data unmixing is investigated, which includes a modified minimum noise fraction (MNF) transformation and independent component analysis (ICA). The modified MNF transformation is used to reduce noise and remove correlation between neighboring bands. Then the ICA is applied to unmix hyperspectral images, and independent endmembers are obtained from unmixed images by using post-processing which includes image segmentation based on statistical histograms and morphological operations. The experimental results demonstrate that this algorithm can identify endmembers resident in mixed pixels. Meanwhile, the results show uhe high computational efficiency of the modified MNF transformation. The time consumed by the modified method is almost one fifth of the traditional MNF transformation.

Target polarization recognition under rain and snow cluster

This paper derives the polarization scattering matrix of both the warship target and the interferential chaff conditioned on the exit of rain and snow cluster,and furthermore,proposes a method to recognize the chaff and warship by analyzing their corresponding polarization information.This method mainly aims to the radar,which can receive both the left and right circularly polarized wave to catch the full polarization information of echoes,and then acquires more accurate polarization characteristics of the target and chaff after getting rid of the impacts of rain and snow cluster from the polarization scattering power matrix of echoes.In order to further improve the recognition probability,the non-linear polarization transformation is introduced to make the separation of target and chaff more realizable.It indicates that the resulting performance is incredible for the traditional recognition in time and frequency domain.