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Cognitive radio is based on the thought of adaptive frequency allocation. It solves the problems of spectrum lack and spectrum inefficiency in current communication networks. In this paper we propose a solution using Artificial Neural Networks (ANNs) to replace a complicated frequency allocation system in the cognitive radio. The solution will make sure that the frequency allocation working well in...
As the phased array radar works in a multi-objective environment, it's necessary to refer to the priority of targets when allocating resources during scheduling. The determination of priorities of targets needs to be fast and efficient, and consider multiple factors. Artificial neural network has adaptive and self-learning ability, and good fault-tolerance features, especially for processing problems...
Location estimation using received signal strength (RSS) in pervasively available Wi-Fi infrastructures has been considered as a popular indoor positioning solution. However, accuracy deterioration due to uncertainty of RSS and offline manual calibration cost limit the deployment of Wi-Fi positioning systems. This paper proposes a signal perturbation technique to enhance existing support vector regression...
A novel indoor location algorithm based on dynamic Radio Maps construction in wireless local area network (WLAN) is proposed. The limitation of previous static Radio Map method is that reconstruction work must be taken to adapt the variation of indoor wireless environment. By taking received signal strength (RSS) values varying over time and space into account, a dynamic Radio Map is constructed to...
Much attention has been paid to WLAN indoor positioning algorithm for its high accuracy and low cost to meet the location based services (LBS). This paper proposes a novel positioning algorithm based on positioning characteristics extraction in WLAN indoor environment. Each RSS signal from an individual access point is taken as input of the RBF neural networks to establish the mapping between RSS...
This paper proposes the optimal K nearest neighbors (KNN) positioning algorithm via theoretical accuracy criterion (TAC) in wireless LAN (WLAN) indoor environment. As far as we know, although the KNN algorithm is widely utilized as one of the typical distance dependent positioning algorithms, the optimal selection of neighboring reference points (RPs) involved in KNN has not been significantly analyzed...
To begin with, for indoor location system, the necessity of research on genetic neural network and its math model are introduced. Then, by analyzing principle of genetic optimized artificial neural network, an indoor location math model of genetic neural network is established. As for various coding types, regularity is taken as the measurement to determine the best coding type for parameter optimization...
This paper presents the optimal networking strategy based on signal coverage requirement in wireless local area network (WLAN) indoor location environment. Up to now, much attention has been paid for the improvement of various positioning algorithms to guarantee the location efficiency in WLAN environment. However, the layout of access points (APs) and corresponding topological structures also significantly...
WLAN Indoor tracking system is presented based on the comparison between the off-line pre-stored Radio-map and new recorded signal strength in the on-line phase to estimate user's motion trajectory. Furthermore, the improved particle filter tracking algorithm that consists of the particles-reference points (P-RPs) transferring for getting the likelihood function and velocity estimation from the ANN...
The paper presents a fuzzy neural network with radial basis function (RBF) to apply on WLAN indoor location, effectively reducing the cost of indoor location, enhancing real-time indoor location, greatly improving indoor location accuracy. Through the equivalent theorem, the fuzzy inference system and the RBF neural network are combined and form a fuzzy RBF neural network system, which takes advantage...
With the development of positioning in indoor wireless environments, RSS-based indoor positioning algorithm has been widely applied. Compared with other indoor positioning algorithms, the greatest advantage of RSS-based is that it can be configured easily and can get the signal strength from various types of networks that support the 802.11 protocol. Furthermore, it doesn't need complex clock synchronization...
Neural network optimized by genetic algorithm (GA) based WLAN indoor location method is proposed. GA based artificial neural network (GA-ANN) method can effectively reduce the storage cost, enhance real-time ability, and greatly improves the accuracy of indoor location. By analyzing the inherent shortage in neural network when applying in indoor environment, make use of genetic algorithm to encode...
This paper proposes the WiFi indoor location determination method based on adaptive neuro-fuzzy inference system (ANFIS) with principal component analysis (PCA). It reduces the WiFi signal vectors dimensions and saves the storage cost and simplifies the fuzzy rules generated by subtractive clustering method for ANFIS training. In the off-line phase, the received signal strength (RSS) or signal to...
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