Diversity and adaptation in populations of clustering ants
SAB94 Proceedings of the third international conference on Simulation of adaptive behavior : from animals to animats 3: from animals to animats 3
The nature of statistical learning theory
The nature of statistical learning theory
Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
Future Generation Computer Systems
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Comparison between coherent and noncoherent receivers for UWB communications
EURASIP Journal on Applied Signal Processing
Performance of UWB receivers with partial CSI using a simple body area network channel model
IEEE Journal on Selected Areas in Communications - Special issue on body area networking: Technology and applications
A survey on the application of genetic programming to classification
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Simultaneous feature selection and classification using kernel-penalized support vector machines
Information Sciences: an International Journal
Fuzzy c-means clustering based robust and blind noncoherent receivers for underwater sensor networks
WASA'10 Proceedings of the 5th international conference on Wireless algorithms, systems, and applications
Fuzzy clustering of time series in the frequency domain
Information Sciences: an International Journal
Artificial immune multi-objective SAR image segmentation with fused complementary features
Information Sciences: an International Journal
Particle swarm optimization for determining fuzzy measures from data
Information Sciences: an International Journal
A novel ant-based clustering algorithm using the kernel method
Information Sciences: an International Journal
Journal of Network and Computer Applications
A modified clustering algorithm based on swarm intelligence
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
Low Complexity Rake Receivers in Ultra-Wideband Channels
IEEE Transactions on Wireless Communications
Energy-Detection UWB Receivers with Multiple Energy Measurements
IEEE Transactions on Wireless Communications
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Characterization of ultra-wide bandwidth wireless indoor channels: a communication-theoretic view
IEEE Journal on Selected Areas in Communications
Channel estimation for ultra-wideband communications
IEEE Journal on Selected Areas in Communications
Generalized UWB transmitted reference systems
IEEE Journal on Selected Areas in Communications - Part 1
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Given the computational complexity and sophisticated implementation of traditionally parametric channel estimators, it has been gradually recognized that the existing data detection methodologies based on the finite impulse response (FIR) propagation channel modeling may become infeasible for ultra-wideband (UWB) radar sensors, especially in some large-scale distributed scenarios. By exploiting the implicit information involved in the received signals, in this investigation, we present a non-parametric UWB data detection scheme for the distributed radar sensor networks. A novel characteristic representation is suggested first. From a pattern classification point of view, a group of quantitative features are then extracted by making full use of the inherent property of UWB propagations. Thus, UWB data detection is formulated as a pattern classification problem in a multidimensional feature space. By thoroughly utilizing the self-similarity of the representative patterns, the ant swarm intelligence inspired clustering algorithm, with the new designed ant movement strategy, is adopted to perform unsupervised data detections. The developed scheme is independent of any a priori modeling information, which essentially avoids the expensive parametric estimators and thus enables practically feasible realizations. To alleviate the computational burden, the principle component analysis (PCA) is further employed to compress the feature space. The simulation results validate the new algorithm, which is superior to the other popular non-parametric data analysis schemes.