Pub Date : 2015-10-05DOI: 10.1109/SSPD.2015.7288521
Jing Dong, Wenwu Wang, J. Chambers
Speckle noise inherently exists in images acquired by coherent systems, for example, synthetic aperture radar (SAR) and sonar images. Removal of speckle noise is a challenging problem because the noise multiplies (rather than adds to) the original image and it does not follow a Gaussian distribution. In this paper, we focus on the speckle noise removal problem and propose a method using analysis dictionary learning. In our proposed method, the image recovery is addressed in the logarithmic transform domain, thereby converting the multiplicative model to an additive model. Our formulation consists of a data fidelity term derived from the distribution of the speckle noise and a regularization term using the learned analysis dictionary. Experimental results on synthetic speckled images and real SAR images demonstrate the promising performance of the proposed method.
{"title":"Removing Speckle Noise by Analysis Dictionary Learning","authors":"Jing Dong, Wenwu Wang, J. Chambers","doi":"10.1109/SSPD.2015.7288521","DOIUrl":"https://doi.org/10.1109/SSPD.2015.7288521","url":null,"abstract":"Speckle noise inherently exists in images acquired by coherent systems, for example, synthetic aperture radar (SAR) and sonar images. Removal of speckle noise is a challenging problem because the noise multiplies (rather than adds to) the original image and it does not follow a Gaussian distribution. In this paper, we focus on the speckle noise removal problem and propose a method using analysis dictionary learning. In our proposed method, the image recovery is addressed in the logarithmic transform domain, thereby converting the multiplicative model to an additive model. Our formulation consists of a data fidelity term derived from the distribution of the speckle noise and a regularization term using the learned analysis dictionary. Experimental results on synthetic speckled images and real SAR images demonstrate the promising performance of the proposed method.","PeriodicalId":212668,"journal":{"name":"2015 Sensor Signal Processing for Defence (SSPD)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115053442","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-10-05DOI: 10.1109/SSPD.2015.7288524
Di Wu, Mehrdad Yaghoobi, M. Davies
State-of-the-art Ground Moving Target Indicator (GMTI) schemes include the Displaced Phase Center Antenna (DPCA) and Along Track Interferometry (ATI) which are commonly used image-based dual- channel techniques for moving target detection. In the present paper, we provide a different perspective for solving GMTI tasks by generalising the ground moving targets imaging as a parameter estimation and an optimisation problem. A sparsity based ground target imaging approach is described to improve the image quality for moving targets and estimate their states. By exploiting the fact that moving targets are highly sparse in the observed scene and feasible velocity space, the proposed method constructs a velocity map for the illuminated region, and combines this map with a sparsity based optimisation algorithm to realise the image formation. The performance of the presented method is demonstrated through GOTCHA airborne SAR data set.
{"title":"Sparsity Based Ground Moving Target Imaging via Multi-Channel SAR","authors":"Di Wu, Mehrdad Yaghoobi, M. Davies","doi":"10.1109/SSPD.2015.7288524","DOIUrl":"https://doi.org/10.1109/SSPD.2015.7288524","url":null,"abstract":"State-of-the-art Ground Moving Target Indicator (GMTI) schemes include the Displaced Phase Center Antenna (DPCA) and Along Track Interferometry (ATI) which are commonly used image-based dual- channel techniques for moving target detection. In the present paper, we provide a different perspective for solving GMTI tasks by generalising the ground moving targets imaging as a parameter estimation and an optimisation problem. A sparsity based ground target imaging approach is described to improve the image quality for moving targets and estimate their states. By exploiting the fact that moving targets are highly sparse in the observed scene and feasible velocity space, the proposed method constructs a velocity map for the illuminated region, and combines this map with a sparsity based optimisation algorithm to realise the image formation. The performance of the presented method is demonstrated through GOTCHA airborne SAR data set.","PeriodicalId":212668,"journal":{"name":"2015 Sensor Signal Processing for Defence (SSPD)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129972514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-10-05DOI: 10.1109/SSPD.2015.7288504
B. Karlsen, E. Nielsen, Morten T. Pedersen
We present a method for fusion of radar and secondary sensor data, e.g. AIS (Automatic Identification System), ADS-B (Automatic Dependent Surveillance Broadcast) or IFF (Identification, Friend or Foe) data. The method is based on fusion of kinematic models of target trajectories from the two sensors into kinematic models of the associations. The method can handle several hundred simultaneous targets (shown for 529 x 529 targets + 1600 clutter plots). It does not require several iterations through the data set in order to find associations, and it includes track history from the two sensors. The mathematical framework of the method is based on Kalman filters, maximum likelihood and probability theory as well as kinematics.
我们提出了一种融合雷达和辅助传感器数据的方法，例如AIS(自动识别系统)、ADS-B(自动相关监视广播)或IFF(敌我识别)数据。该方法基于将两个传感器的目标轨迹的运动学模型融合为关联的运动学模型。该方法可以同时处理数百个目标(如图所示为529 x 529目标+ 1600杂波图)。它不需要通过数据集进行多次迭代来找到关联，并且它包括来自两个传感器的跟踪历史。该方法的数学框架是基于卡尔曼滤波、极大似然和概率论以及运动学。
{"title":"Fusion of Radar and Secondary Sensor Data Using Kinematic Models of Multiple Simultaneous Targets","authors":"B. Karlsen, E. Nielsen, Morten T. Pedersen","doi":"10.1109/SSPD.2015.7288504","DOIUrl":"https://doi.org/10.1109/SSPD.2015.7288504","url":null,"abstract":"We present a method for fusion of radar and secondary sensor data, e.g. AIS (Automatic Identification System), ADS-B (Automatic Dependent Surveillance Broadcast) or IFF (Identification, Friend or Foe) data. The method is based on fusion of kinematic models of target trajectories from the two sensors into kinematic models of the associations. The method can handle several hundred simultaneous targets (shown for 529 x 529 targets + 1600 clutter plots). It does not require several iterations through the data set in order to find associations, and it includes track history from the two sensors. The mathematical framework of the method is based on Kalman filters, maximum likelihood and probability theory as well as kinematics.","PeriodicalId":212668,"journal":{"name":"2015 Sensor Signal Processing for Defence (SSPD)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131075912","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-10-05DOI: 10.1109/SSPD.2015.7288507
M. Leng, S. G. Razul, C. See, Wee Peng Tay, Chi Cheng, F. Quitin
We consider the problem of tracking a receiver using signals of opportunity (SOOP) from beacons and a reference anchor with known positions and velocities, and where all devices have asynchronous local clocks or oscillators. Based on an extended Kalman filter, we propose a sequential estimator to jointly track the receiver location, velocity, and its clock parameters using time- difference-of-arrival and frequency-difference-of-arrival measurements obtained from the SOOP samples collected by the receiver and reference anchor. Field experiments are carried out using a software defined radio testbed, and Iridium satellites as the SOOP beacons. Experiment demonstrate that our measurement model has a good fit, and our proposed estimator can successfully track both the receiver location, velocity, and the relative clock offset and skew with respect to the reference anchor with good accuracy.
{"title":"Joint Navigation and Synchronization Using SOOP in GPS-Denied Environments: Algorithm and Empirical Study","authors":"M. Leng, S. G. Razul, C. See, Wee Peng Tay, Chi Cheng, F. Quitin","doi":"10.1109/SSPD.2015.7288507","DOIUrl":"https://doi.org/10.1109/SSPD.2015.7288507","url":null,"abstract":"We consider the problem of tracking a receiver using signals of opportunity (SOOP) from beacons and a reference anchor with known positions and velocities, and where all devices have asynchronous local clocks or oscillators. Based on an extended Kalman filter, we propose a sequential estimator to jointly track the receiver location, velocity, and its clock parameters using time- difference-of-arrival and frequency-difference-of-arrival measurements obtained from the SOOP samples collected by the receiver and reference anchor. Field experiments are carried out using a software defined radio testbed, and Iridium satellites as the SOOP beacons. Experiment demonstrate that our measurement model has a good fit, and our proposed estimator can successfully track both the receiver location, velocity, and the relative clock offset and skew with respect to the reference anchor with good accuracy.","PeriodicalId":212668,"journal":{"name":"2015 Sensor Signal Processing for Defence (SSPD)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122517117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-10-05DOI: 10.1109/SSPD.2015.7288514
Isabella McKenna, F. Tonolini, Rachael Tobin, J. Houssineau, H. Bridle, C. McDougall, Isabel Schlangen, J. McGrath, M. Jimenez, Daniel E. Clark
In environments of scarce hygiene it is of primary importance to detect potentially harmful concentrations of pathogens in drinking water. In many situations, however, accurate analysis of water samples is prohibitively complex and often requires highly specialised apparatuses and technicians. In order to overcome these limitations, a method to employ video processing to assist microfluidics water filtering apparatuses is proposed. Through the automated analysis of videos captured at the output of such devices it is possible to extract useful information that could control an autonomous calibration, hence eliminating the need of an expert and possibly leading to the construction of readily employable water quality assessing devices.
{"title":"Observing the Dynamics of Waterborne Pathogens for Assessing the Level of Contamination","authors":"Isabella McKenna, F. Tonolini, Rachael Tobin, J. Houssineau, H. Bridle, C. McDougall, Isabel Schlangen, J. McGrath, M. Jimenez, Daniel E. Clark","doi":"10.1109/SSPD.2015.7288514","DOIUrl":"https://doi.org/10.1109/SSPD.2015.7288514","url":null,"abstract":"In environments of scarce hygiene it is of primary importance to detect potentially harmful concentrations of pathogens in drinking water. In many situations, however, accurate analysis of water samples is prohibitively complex and often requires highly specialised apparatuses and technicians. In order to overcome these limitations, a method to employ video processing to assist microfluidics water filtering apparatuses is proposed. Through the automated analysis of videos captured at the output of such devices it is possible to extract useful information that could control an autonomous calibration, hence eliminating the need of an expert and possibly leading to the construction of readily employable water quality assessing devices.","PeriodicalId":212668,"journal":{"name":"2015 Sensor Signal Processing for Defence (SSPD)","volume":"282 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116571404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-10-05DOI: 10.1109/SSPD.2015.7288501
S. Sthapit, J. Thompson, J. Hopgood, N. Robertson
Person re-identification is to associate people across different camera views at different locations and time. Current computer vision algorithms on person re-identification mainly focus on performance, making it unsuitable for distributed systems. For distributed system, computational complexity, network usage, energy consumption and memory requirement are as important as the performance. In this paper, we compare the merits of the current algorithms. We consider three key algorithms Keep It Simple and Straightforward MEtric (KISSME), Symmetry-Driven Accumulation of Local Features (SDALF) and Unsupervised Saliency Matching (USM). The advantage of SDALF, and USM is that they are unsupervised methods so training is not required but computationally many time expensive than KISSME. Saliency based method is superior in performance but also has the longest feature length. As the feature needs to be transmitted from one camera to other in distributed system, this mean higher energy consumption and longer time delay. Among these three, KISSME offers a balance between performance, complexity and feature lengths hence more suitable for distributed systems.
{"title":"Distributed Implementation for Person Re-Identification","authors":"S. Sthapit, J. Thompson, J. Hopgood, N. Robertson","doi":"10.1109/SSPD.2015.7288501","DOIUrl":"https://doi.org/10.1109/SSPD.2015.7288501","url":null,"abstract":"Person re-identification is to associate people across different camera views at different locations and time. Current computer vision algorithms on person re-identification mainly focus on performance, making it unsuitable for distributed systems. For distributed system, computational complexity, network usage, energy consumption and memory requirement are as important as the performance. In this paper, we compare the merits of the current algorithms. We consider three key algorithms Keep It Simple and Straightforward MEtric (KISSME), Symmetry-Driven Accumulation of Local Features (SDALF) and Unsupervised Saliency Matching (USM). The advantage of SDALF, and USM is that they are unsupervised methods so training is not required but computationally many time expensive than KISSME. Saliency based method is superior in performance but also has the longest feature length. As the feature needs to be transmitted from one camera to other in distributed system, this mean higher energy consumption and longer time delay. Among these three, KISSME offers a balance between performance, complexity and feature lengths hence more suitable for distributed systems.","PeriodicalId":212668,"journal":{"name":"2015 Sensor Signal Processing for Defence (SSPD)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116023499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-09-11DOI: 10.1109/SSPD.2015.7288523
J. Corr, K. Thompson, Stephan Weiss, I. Proudler, J. McWhirter
This paper extends the analysis of the recently introduced row- shift corrected truncation method for paraunitary matrices to those produced by the state-of-the-art sequential matrix diagonalisation (SMD) family of polynomial eigenvalue decomposition (PEVD) algorithms. The row-shift corrected truncation method utilises the ambiguity in the paraunitary matrices to reduce their order. The results presented in this paper compare the effect a simple change in PEVD method can have on the performance of the paraunitary truncation. In the case of the SMD algorithm the benefits of the new approach are reduced compared to what has been seen before however there is still a reduction in both reconstruction error and paraunitary matrix order.
{"title":"Shortening of Paraunitary Matrices Obtained by Polynomial Eigenvalue Decomposition Algorithms","authors":"J. Corr, K. Thompson, Stephan Weiss, I. Proudler, J. McWhirter","doi":"10.1109/SSPD.2015.7288523","DOIUrl":"https://doi.org/10.1109/SSPD.2015.7288523","url":null,"abstract":"This paper extends the analysis of the recently introduced row- shift corrected truncation method for paraunitary matrices to those produced by the state-of-the-art sequential matrix diagonalisation (SMD) family of polynomial eigenvalue decomposition (PEVD) algorithms. The row-shift corrected truncation method utilises the ambiguity in the paraunitary matrices to reduce their order. The results presented in this paper compare the effect a simple change in PEVD method can have on the performance of the paraunitary truncation. In the case of the SMD algorithm the benefits of the new approach are reduced compared to what has been seen before however there is still a reduction in both reconstruction error and paraunitary matrix order.","PeriodicalId":212668,"journal":{"name":"2015 Sensor Signal Processing for Defence (SSPD)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128963528","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-09-09DOI: 10.1109/SSPD.2015.7288512
A. Persico, C. Clemente, C. Ilioudis, D. Gaglione, Jianlin Cao, J. Soraghan
The capability to recognize ballistic threats, is a critical topic due to the increasing effectiveness of resultant objects and to economical constraints. In particular the ability to distinguish between warheads and decoys is crucial in order to mitigate the number of shots per hit and to maximize the ammunition capabilities. For this reason a reliable technique to classify warheads and decoys is required. In this paper the use of micro-Doppler signatures in conjunction with the 2-Dimensional Gabor filter is presented for this problem. The effectiveness of the proposed approach is demonstrated through the use of real data.
{"title":"Micro-Doppler Based Recognition of Ballistic Targets Using 2D Gabor Filters","authors":"A. Persico, C. Clemente, C. Ilioudis, D. Gaglione, Jianlin Cao, J. Soraghan","doi":"10.1109/SSPD.2015.7288512","DOIUrl":"https://doi.org/10.1109/SSPD.2015.7288512","url":null,"abstract":"The capability to recognize ballistic threats, is a critical topic due to the increasing effectiveness of resultant objects and to economical constraints. In particular the ability to distinguish between warheads and decoys is crucial in order to mitigate the number of shots per hit and to maximize the ammunition capabilities. For this reason a reliable technique to classify warheads and decoys is required. In this paper the use of micro-Doppler signatures in conjunction with the 2-Dimensional Gabor filter is presented for this problem. The effectiveness of the proposed approach is demonstrated through the use of real data.","PeriodicalId":212668,"journal":{"name":"2015 Sensor Signal Processing for Defence (SSPD)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114613365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-09-09DOI: 10.1109/SSPD.2015.7288503
Yixin Chen, Carmine Clamente, J. Soraghan, Stephan Weiss
In this paper, a hybrid Discrete Fractional Cosine Transform (DFrCT) with Tikhonov regularization based Turbo Minimum mean square error (MMSE) equalization (DFrCT-Turbo) is presented to suppress inter-carrier interference (ICI) over underwater acoustic channels (UWA). The scheme is based on Orthogonal Frequency Division Multiplex (OFDM) scenario. In addition, an optimal order selecting method for DFrCT is developed by maximizing carrier to interference ratio (CIR) to UWA channel character. Simulation results show that BER improvement of up to 5dBs over traditional orthogonal based methods with moderate complexity.
{"title":"Fractional Cosine Transform (FRCT)-Turbo Based OFDM for Underwater Acoustic Communication","authors":"Yixin Chen, Carmine Clamente, J. Soraghan, Stephan Weiss","doi":"10.1109/SSPD.2015.7288503","DOIUrl":"https://doi.org/10.1109/SSPD.2015.7288503","url":null,"abstract":"In this paper, a hybrid Discrete Fractional Cosine Transform (DFrCT) with Tikhonov regularization based Turbo Minimum mean square error (MMSE) equalization (DFrCT-Turbo) is presented to suppress inter-carrier interference (ICI) over underwater acoustic channels (UWA). The scheme is based on Orthogonal Frequency Division Multiplex (OFDM) scenario. In addition, an optimal order selecting method for DFrCT is developed by maximizing carrier to interference ratio (CIR) to UWA channel character. Simulation results show that BER improvement of up to 5dBs over traditional orthogonal based methods with moderate complexity.","PeriodicalId":212668,"journal":{"name":"2015 Sensor Signal Processing for Defence (SSPD)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130952053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-09-01DOI: 10.1109/SSPD.2015.7288513
R. Mitra, V. Bhatia
Underwater communications systems are being increasingly used in defence, security service, oil exploration, ocean science and in many other applications. The underwater acoustic channel is characterised by the large delay spread, Doppler shifts, limited bandwidths and time variability. The channel is also affected by additive impulsive noise, which makes the underwater communication even more challenging. Since the channel and noise characteristics vary immensely, an adaptive equaliser at the communications receiver forms a viable solution for increasing the bit error rate of the communication link. The adaptive multistage clustering based equaliser is one such solution which provides high throughput. However, the performance of the multistage clustering equaliser degrades in the presence of impulsive noise. To improve the throughput and robustness, we propose an adaptive normalised multistage clustering based blind equaliser for underwater acoustic channel. From simulation results, it is observed that the proposed algorithm has better convergence and symbol error rate performance. Convergence analysis of the proposed algorithm is also presented in the paper.
{"title":"Normalised Multi-Stage Clustering Equaliser For Underwater Acoustic Channels","authors":"R. Mitra, V. Bhatia","doi":"10.1109/SSPD.2015.7288513","DOIUrl":"https://doi.org/10.1109/SSPD.2015.7288513","url":null,"abstract":"Underwater communications systems are being increasingly used in defence, security service, oil exploration, ocean science and in many other applications. The underwater acoustic channel is characterised by the large delay spread, Doppler shifts, limited bandwidths and time variability. The channel is also affected by additive impulsive noise, which makes the underwater communication even more challenging. Since the channel and noise characteristics vary immensely, an adaptive equaliser at the communications receiver forms a viable solution for increasing the bit error rate of the communication link. The adaptive multistage clustering based equaliser is one such solution which provides high throughput. However, the performance of the multistage clustering equaliser degrades in the presence of impulsive noise. To improve the throughput and robustness, we propose an adaptive normalised multistage clustering based blind equaliser for underwater acoustic channel. From simulation results, it is observed that the proposed algorithm has better convergence and symbol error rate performance. Convergence analysis of the proposed algorithm is also presented in the paper.","PeriodicalId":212668,"journal":{"name":"2015 Sensor Signal Processing for Defence (SSPD)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129183591","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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