Fully Connected Neural Networks Ensemble with Signal Strength Clustering for Indoor Localization in Wireless Sensor Networks

The paper introduces a method which improves localization accuracy of the signal strength fingerprinting approach. According to the proposed method, entire localization area is divided into regions by clustering the fingerprint database. For each region a prototype of the received signal strength is determined and a dedicated artificial neural network (ANN) is trained by using only those fingerprints that belong to this region (cluster). Final estimation of the location is obtained by fusion of the coordinates delivered by selected ANNs. Sensor nodes have to store only the signal strength prototypes and synaptic weights of the ANNs in order to estimate their locations. This approach significantly reduces the amount of memory required to store a received signal strength map. Various ANN topologies were considered in this study. Improvement of the localization accuracy as well as speed-up of learning process was achieved by employing fully connected neural networks. The proposed method was verified and compared against state-of-the-art localization approaches in realworld indoor environment by using both stationary andmobile sensor nodes

Underwater Source Localization based on Modal Propagation and Acoustic Signal Processing

Acoustic localization plays a pivotal role in underwater vehicle systems and marine mammal detection. Previous efforts adopt synchronized arrays of sensors to extract some features like direction of arrival (DOA) or time of flight (TOF) from the received signal. However, installing and synchronizing several hydrophones over a large area is costly and challenging. To tackle this problem, we use a single-hydrophone localization system which relies on acoustic signal processing methods rather than multiple hydrophones. This system takes modal dispersion into consideration and estimates the distance between sound source and receiver (range) based on dispersion curves. It is shown that the larger the range is, the more separable the modes are. To make the modes more distinguishable, a non-linear signal processing technique, called warping, is utilized. Propagation model of low-frequency signals, such as dolphin sound, is well-studied in shallow water environment (depth D\u3c200 m), and it was demonstrated that at large ranges (range r\u3e1 km), modal dispersion is utterly visible at time frequency (TF) domain. We used Peker is model for the aforementioned situation to localize both synthetic and real underwater acoustic signals. The accuracy of the localization system is examined with various sounds, including impulsive signal, sounds with known Fourier transform, and signals with estimated source phase. Experimental results show that the warping technique can considerably lessen the localization error, especially when prior knowledge about the source signal and waveguide are available

A Survey of Sound Source Localization Methods in Wireless Acoustic Sensor Networks

Wireless acoustic sensor networks (WASNs) are formed by a distributed group of acoustic-sensing devices featuring audio playing and recording capabilities. Current mobile computing platforms offer great possibilities for the design of audio-related applications involving acoustic-sensing nodes. In this context, acoustic source localization is one of the application domains that have attracted the most attention of the research community along the last decades. In general terms, the localization of acoustic sources can be achieved by studying energy and temporal and/or directional features from the incoming sound at different microphones and using a suitable model that relates those features with the spatial location of the source (or sources) of interest. This paper reviews common approaches for source localization in WASNs that are focused on different types of acoustic features, namely, the energy of the incoming signals, their time of arrival (TOA) or time difference of arrival (TDOA), the direction of arrival (DOA), and the steered response power (SRP) resulting from combining multiple microphone signals. Additionally, we discuss methods not only aimed at localizing acoustic sources but also designed to locate the nodes themselves in the network. Finally, we discuss current challenges and frontiers in this field

Blind localization of radio emitters in wireless communications

The proliferation of wireless services is expected to increase the demand for radio spectrum in the foreseeable future. Given the limitations of the radio spectrum, it is evident that the current fixed frequency assignment policy fails to accommodate this increasing demand. Thus, the need for innovative technologies that can scale to accommodate future demands both in terms of spectrum efficiency and high reliable communication. Cognitive radio (CR) is one of the emerging technologies that offers a more flexible use of frequency bands allowing unlicensed users to exploit and use portions of the spectrum that are temporarily unused without causing any potential harmful interference to the incumbents. The most important functionality of a CR system is to observe the radio environment through various spectrum awareness techniques e.g., spectrum sensing or detection of spectral users in the spatio-temporal domain. In this research, we mainly focus on one of the key cognitive radio enabling techniques called localization, which provides crucial geo-location of the unknown radio transmitter in the surrounding environment. Knowledge of the user’s location can be very useful in enhancing the functionality of CRs and allows for better spectrum resource allocations in the spatial domain. For instance, the location-awareness feature can be harnessed to accomplish CR tasks such as spectrum sensing, dynamic channel allocation and interference management to enable cognitive radio operation and hence to maximize the spectral utilization. Additionally, geo-location can significantly expand the capabilities of many wireless communication applications ranging from physical layer security, geo-routing, energy efficiency, and a large set of emerging wireless sensor network and social networking applications. We devote the first part of this research to explore a broad range of existing cooperative localization techniques and through Monte-Carlo simulations analyze the performance of such techniques. We also propose two novel techniques that offer better localization performance with respect to the existing ones. The second and third parts of this research put forth a new analytical framework to characterize the performance of a particular low-complexity localization technique called weighted centroid localization (WCL), based on the statistical distribution of the ratio of two quadratic forms in normal variables. Specifically, we evaluate the performance of WCL in terms of the root mean square error (RMSE) and cumulative distribution function (CDF). The fourth part of this research focuses on studying the bias of the WCL and also provides solutions for bias correction. Throughout this research, we provide a case study analysis to evaluate the performance of the proposed approaches under changing channel and environment conditions. For the new theoretical framework, we compare analytical and Monte-Carlo simulation results of the performance metric of interest. A key contribution in our analysis is that we present not only the accurate performance in terms of the RMSE and CDF, but a new analytical framework that takes into consideration the finite nature of the network, overcoming the limitations of asymptotic results based on the central limit theorem. Remarkably, the numerical results unfold that the new analytical framework is able to predict the performance of WCL capturing all the essential aspects of propagation as well as the cognitive radio network spatial topology. Finally, we present conclusions gained from this research and possible future directions