Informatics Publishing Limited

Rajkumar Goswami ¹, G. Sasi Bhushana Rao ²

Affiliations
1 Andhra University, Visakhapatnam, IN
2 Electronics and Communication Department, Andhra University Visakhapatnam, IN

Abstract

The corruption of the transmission signal in the channel has always been a cause of worry for communication engineers, especially in the multipath environment where it necessitates the additional processing in order to mitigate the errors those creep in because of the fading effect created due to this multipath phenomenon. Various Forward Error Correction (FEC) Schemes have evolved over a period of time such as Block Codes, Convolutional codes, TCM Codes and in the recent past Turbo Coding Schemes have become more popular due to its high performance. In this paper the use of turbo coding scheme based on Serially Concatenated Convolutional Codes (SCCC) has been presented for achieving the better BER in respect of fading channel. A design of convolutionalencoder of rate 3/4, 16 states has been presented which forms the integral part of the Turbo Coder. The performance analysis of this turbo coding scheme has been undertaken in respect of fading environment. The complete analysis has been undertaken in MATLAB. The fading channel has been simulated through Rician Multipath Model and the performance of the proposed Turbo Coding Scheme has been compared with the performance of the Convolutional coding Scheme for the same convolutional encoder and with the uncoded 8PSK. The performance of the same has also been evaluated in the case of AWGN channel.

Keywords

Convolutional Code, Fading Channel, TCM, Turbo Codes.

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Rajkumar Goswami ¹, G. Sasi Bhushana Rao ², S. Swapna Rani ¹, M. N. V. S. S. Kumar ³, S. Deva Prasad ³, G. Hemanth Kumar ⁴

Affiliations
1 Andhra University, IN
2 Electronics and Communication Department, Andhra University, Visakhapatnam, IN
3 Andhra University, Visakhapatnam, IN
4 GNITS, Hyderabad, IN

Abstract

With the advancement of technology, the imaging sonars have become the reality and their usage has been extensive in the area of obstacle avoidance in respect of Autonomous Underwater Vehicle (AUV). The underwater environment being heterogeneous, the sonar images have a very complex background, low contrast, and deteriorative edges. These characteristics, therefore, pose the difficulties for extracting the objects from the sonar images. In this paper we have reviewed the various existing image processing techniques in respect of the sonar images and discussed their shortcomings. After discussing the existing image processing techniques and their limitations, an algorithm has been proposed for processing these sonar images effectively and the results have also been compared with the results of existing techniques. Extracting the obstacle (objects) aspects such as range, bearing, size, shape, speed and course from the images received from Sonar are very important for the AUV in order to avoid the collision from the obstacles those may come into its path. Another very important criterion is the time taken to process the image, which must be as least as possible in order to provide more time for the AUV to take evasive action. For achieving this, proper segmentation of sonar images is a very important step in order to identify the objects (or obstacles) correctly in the least possible time. Several algorithms have been developed in the past for segmentation of images, however these methods did not provide the desired results when subjected to the real sonar images. Therefore a new segmentation method for processing the underwater Sonar images was developed by taking into account the available techniques and domain knowledge. This method is based on the thresholding of the image in which the threshold is calculated adaptively on iterative basis by taking the parameters of image histogram into consideration. The initial threshold value for each individual region of image has been selected by taking the histogram of the region under consideration. Adaptive thresholding utilizes a local window for each individual pixel and computes the new intensity value, based on the local histogram defined in the local window. This is then followed by the morphological, dilation and erosion operations before producing the final segmented image. The performance of the proposed algorithm has been compared with the other existing methods such as Edge detection, Adaptive Thresholding, Fuzzy C Means Clustering (FCM) and Adaptive Histogram Equalization. The results have also been presented in the tabulated form in addition to the segmented images. It has been concluded from the results that the proposed segmentation method achieves better segmentation results in respect of sonar images and is also highly efficient as it takes the least time for segmentation amongst all the methods.

Keywords

Adaptive Threshold, Histogram, Segmentation, Sonar.

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G. Sasi Bhushana Rao ¹, Rajkumar Goswami ², S. Deva Prasad ³, M. N. V. S. S. Kumar ⁴, S. Swapna Rani ⁵

Affiliations
1 Electronics & Communication Engineering Department, Andhra University Engineering College, Visakhapatnam, IN
2 Andhra University College of Engineering, Visakhapatnam, IN
3 University College of Engineering, Andhra University, Visakhapatnam-530003, IN
4 Department of Electronics & Communication Engineering, University College of Engineering, Andhra University, Visakhapatnam-530003, IN
5 Electronics and Communication Engineering Department, Andhra University, Visakhapatnam, IN

Abstract

With the advent of Imaging SONAR, the field of underwater imaging has been gaining lot of importance especially for Autonomous Underwater Vehicle (AUV) for obstacle avoidance.AUV is the underwater robot used for detecting the underwater mines, monitoring and surveillance of coastline and important dense traffic movement ports and other vital defence installations. The AUV applications include the obstacle trajectory tracking, obstacle avoidance and intelligence surveillance and reconnaissance(IRS). The heart of the AUV system depends on the performance of the SONAR. SONAR provides the navigation and guidance by mounting it on the AUV and operates on the principle of acoustic wave propagation. SONAR provides only the location of the object in terms of range and bearing and the objects dimensions (length and thickness) but not the obstacle depth information. For effective maneuvering and for analyzing the target features especially for collision avoidance, the depth information of the object in 3Dimensions is important. In order to know the depth of the obstacle using 2D SONAR, the 2-Dimensional images of the obstacle at different elevation angles are obtained and are used to reconstruct the 3D. This is achieved by scanning the object at various depths. There can be two conditions one in which the SONAR beam partially covers the object, and the second in which SONAR beam covers the complete object. The algorithms developed for this analysis of SONAR data demonstrate the usefulness of the proposed system in the process of converting 2D to 3D information. In this paper a method along with the algorithm that has been designed and developed to calculate the aspect of the target in 3 Dimensions from the 2 Dimension images received from the imaging SONAR by scanning the objects at the various depths is presented. It has been concluded that by using the proposed method and by implementing the proposed algorithm in the 2D SONAR, the high precision 3D aspect of objects has been achieved. The main advantages of proposed algorithm are cost saving and high precision of reconstructed objects.

Keywords

2-Dimensional, 3-Dimensional, AUV, SONAR.

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