Top PDF Sensor Fusion and Deep Learning for Indoor Agent Localization

Sensor Fusion and Deep Learning for Indoor Agent Localization

Sensor Fusion and Deep Learning for Indoor Agent Localization

such as classification, detection, or other machine learning tasks. At a high level, deep learning techniques utilize a cascade of nonlinear units, usually called layers, where the output of one layer is the input to the next. This allows for a hierarchical representation to be learned, with the first layers learning low level features and later layers learning higher level features based on the previous layers. Like other machine learning algorithms, deep learning algorithms can be either supervised or unsupervised; however supervised is much more common. Deep learning techniques have become extremely popular over the past few years due to their performance on many different tasks. One of the first successful implementations of deep learning was in 2012 when Krizhevsky et al. [61] won the ImageNet classification competition by a significant margin using a deep Convolutional Neural Network. After that, researchers began applying deep learning to increasingly more sophisticated problems and out-performing conventional methods. In addition, a lot of effort has gone into designing better architectures and new techniques that continue to improve deep learning models. Today, deep learning can out-perform conventional methods on a large number of machine learning tasks and can be used to solve new problems that were not possible before.
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Object Detection from a Vehicle Using Deep Learning Network and Future Integration with Multi-Sensor Fusion Algorithm

Object Detection from a Vehicle Using Deep Learning Network and Future Integration with Multi-Sensor Fusion Algorithm

Convolutional Neural Networks (CNNs) has been applied successfully for deep learning algorithms for classifying two and three dimensional images, however, their application in ADAS and autonomous vehicle is still in infancy. The most recent work [35] which is somewhat concrete is an empirical evaluation of deep learning on real life driving data at real-time for two tasks: lane detection and vehicle detection. The conclusion of this work is that CNN can provide acceptable solution for the above tasks by running at frame rate which is required for a real-time system. In another recent work, authors use deep learning for detecting pedestrians [36], however, authors comments that although the performance of deep network is better than cascade algorithms for detecting complex patterns, it is slow for a real-time pedestrian detection. None of the existing works consider sensor fusion along with deep neutral network, which is the focus of our work.
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Double Weight-Based SAR and Infrared Sensor Fusion for Automatic Ground Target Recognition with Deep Learning

Double Weight-Based SAR and Infrared Sensor Fusion for Automatic Ground Target Recognition with Deep Learning

Abstract: This paper presents a novel double weight-based synthetic aperture radar (SAR) and infrared (IR) sensor fusion method (DW-SIF) for automatic ground target recognition (ATR). IR-based ATR can provide accurate recognition because of its high image resolution but it is affected by the weather conditions. On the other hand, SAR-based ATR shows a low recognition rate due to the noisy low resolution but can provide consistent performance regardless of the weather conditions. The fusion of an active sensor (SAR) and a passive sensor (IR) can lead to upgraded performance. This paper proposes a doubly weighted neural network fusion scheme at the decision level. The first weight (α) can measure the offline sensor confidence per target category based on the classification rate for an evaluation set. The second weight (β) can measure the online sensor reliability based on the score distribution for a test target image. The LeNet architecture-based deep convolution network (14 layers) is used as an individual classifier. Doubly weighted sensor scores are fused by two types of fusion schemes, such as the sum-based linear fusion scheme (αβ-sum) and neural network-based nonlinear fusion scheme (αβ-NN). The experimental results confirmed the proposed linear fusion method (αβ-sum) to have the best performance among the linear fusion schemes available (SAR-CNN, IR-CNN, α-sum, β-sum, αβ-sum, and Bayesian fusion). In addition, the proposed nonlinear fusion method (αβ-NN) showed superior target recognition performance to linear fusion on the OKTAL-SE-based synthetic database.
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Indoor Localization of Wheeled Robots using Multi-sensor Data Fusion with Event-based Measurements

Indoor Localization of Wheeled Robots using Multi-sensor Data Fusion with Event-based Measurements

RFID systems have become very popular in the area of localization in the recent years. They are cheap, simple, user-friendly and reliable. RFID tags, usually installed in the walls or on the ground, can be detected rapidly by a reader mounted within the mobile agent [112]. The active RFID tags have large reading range. However, they are more expensive than the passive tags and since a power source is needed inside each tag, the battery should be replaced after a while. Passive RFID tags do not need internal power source, replacement and maintenance and can work for several years. They can provide higher accuracy than the active tags due to their shorter detection range. This characteristic requires to deploy a large number of tags in the environment. Like other wireless techniques, the RFID tags also need direct line of sight connection to the reader, mounted on the agent, to assure optimal performance. This problem can be partially overcome by sticking the passive tags on the ground [74]. In this situation, anytime the agent passes over a tag, there is no obstacle between them. However, an RFID reader cannot provide any information about the orientation. For this reason, Nazari et al. [92] mounted four RFID readers on the agent providing an accurate algorithm to estimate both position and orientation. The solutions based on only passive RFID tags, require sticking a very large number of RFID tags on the ground which may not be practical in large environments.
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Moving target localization in indoor wireless sensor networks mixed with LOS/NLOS situations

Moving target localization in indoor wireless sensor networks mixed with LOS/NLOS situations

approach is adopted for NLOS mitigation. Some re- searchers propose soft-decision algorithms without NLOS identification and discarding [26-28]. In [26,27], Chen uses a residual weighting (RWgh)-based algo- rithm to alleviate the NLOS errors. On the basis of Chen's research, Hammes and Zoubir [28] suggest a data fusion algorithm to achieve higher localization ac- curacy. However, RWgh algorithms cannot perform well when the number of LOS nodes is deficient. But, the computational complexity will grow rapidly when the number of ANs increases. In [29], vehicle velocity and heading direction measurements are exploited to make constrained optimization for NLOS error mitiga- tion. Other researchers propose some model matching and database linked methods, such as fingerprinting algorithm, which are always time-consuming [12]. Some researchers begin to explore new methods without NLOS identification. Typical algorithms include least square multi-lateration [30], optimized residual weighting [31], support vector machine classifier [11], and ma- chine learning [32]. These methods often require ideal synchronization or massive experimental data.
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Minet Magnetic Indoor Localization

Minet Magnetic Indoor Localization

and not any past ones. However, this only means that all the information is contained in one state, as a state in the particle filter is created using data from all the previous state. This idea comes from Bayesian statistics, where beliefs are updated with incoming information. Another aspect of hidden Markov systems is that there is an internal state that cannot be measured, so it has to be inferred from observable information. Particle filters work by reducing the error of this inference. The initial state is populated with random guesses, or some guesses derived from a process. However, through the mechanisms of the particle filter these guesses are narrowed around the most probable state. The power of the particle filter comes from the weighing and resampling functions [7]. The weighting function determines how close the guess is to the true state, through some sort of calculation on the observed data. The resampling function determines how to read the weights from the weighting function to determine the distribution of new particles. A good resampling function should not generate the least probable particles, only the ones that appear to be closest to the true state. This probabilistic approach is a much more feasible way of determining a state than to attempt to program for all features in a given environment, although Deep Learning models find success using a similar pattern.
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Effective sensor fusion with event-based sensors and deep network architectures

Effective sensor fusion with event-based sensors and deep network architectures

Abstract: The use of spiking neuromorphic sensors with state-of-art deep networks is currently an active area of research. Still relatively unexplored are the pre-processing steps needed to transform spikes from these sensors and the types of network architectures that can produce high-accuracy performance using these sensors. This paper discusses several methods for preprocessing the spiking data from these sensors for use with various deep network architectures. The outputs of these preprocessing methods are evaluated using different networks including a deep fusion network composed of Convolutional Neural Networks and Recurrent Neural Networks, to jointly solve a recognition task using the MNIST (visual) and TIDIGITS (audio) benchmark datasets. With only 1000 visual input spikes from a spiking hardware retina, the classification accuracy of 64.5% achieved by a particular trained fusion network increases to 98.31% when combined with inputs from a spiking hardware cochlea.
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A New Approach to Self-Localization for Mobile Robots Using Sensor Data Fusion

A New Approach to Self-Localization for Mobile Robots Using Sensor Data Fusion

Figure 7 shows the occupancy grids map, created by pseudo information fusion, and the square path inside it. Practically, the robot does not travel the exact square path in each round, because the motion control commands are created based on the robot's pose information, acquired from the erroneous odometry data. It causes the robot not to return to its initial starting point in each round. This deviation of the location of the robot with respect to the original point of motion starting was manually measured. In the first trial of 32 rounds, their mean values were ∆ x = 35 . 5 mm and
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Localization and Classification of Brain Tumor using Machine Learning & Deep Learning Techniques

Localization and Classification of Brain Tumor using Machine Learning & Deep Learning Techniques

Bhavneet Kaur, et.al [23] proposed an algorithm to detect salient objects for performing brain image analysis. Few existing techniques designed previously were reconsidered in this mechanism. Either the background data exploited or the foreground information estimated as per the latest advancements made in these techniques. Thus, the authors proposed an algorithm using which the background cues and foreground connectivity were covered. This algorithm worked on the edges of objects. The brain MR images were segmented and analyzed thoroughly through this mechanism. In this, different parameters used are background connectivity, noise, localization and color uniqueness in fore- ground regions. Geodesic Saliency (GS), Manifold Ranking (MR), Saliency Filter (SF), Saliency Optimization (S-Obj) and (WCtr-Opt) methods were used for analysing the performance measures. Quantitative evaluation metrics like Precision–Recall curves and Accuracy had been evaluated in this proposed work. It was
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Localization in Wireless Sensor Network Using Manifold Learning

Localization in Wireless Sensor Network Using Manifold Learning

much new application coming up, for example, habitat monitoring, tracking the target, detecting and reporting failure in building. For such applications it becomes important to strictly orient all the nodes depending on a global coordinate system, so that we can make a report for data which has some geographical meaning. Also the general middle ware services for example routing are based on the location information. This is also called as geographical routing. Hence localization of sensor nodes with good accuracy is very important for various applications.

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Performance Comparison of Three Types of Sensor Matrices for Indoor Multi Robot Localization

Performance Comparison of Three Types of Sensor Matrices for Indoor Multi Robot Localization

This paper has focused on the ability of choosing a suitable sensor for implementation a localization system. The chosen sensors are distributed as a matrix of sensors to work as a localization points for any robot placed on the environment. The experiments have tested the low cost LEDs & LDR, IR transmitter & receiver and RFID Tag & Reader which are able to obvious reliable range to demonstrate the measurements. The experiment results show that the RFID reader and Tags produce a more accurate measurements than other sensors and it is non-effected by neither color nor light. The drawback of this sensor is the interference occurs when more than one Tag
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Adaptive Deep Learning through Visual Domain Localization

Adaptive Deep Learning through Visual Domain Localization

We can identify two main solving directions, one based on instance re-weighting and the other on feature alignment. In the first case, the basic approach consists in evaluating the similarity of source instances to the target with the aim of balancing their importance or eventually sub-select them before learning a model. Different measures of similarity have been proposed in combination with shallow learning methods for this weighting procedure [6], [7], [8]. More recently in [9] a deep autoencoder was trained to weigh the importance of source samples by fitting the marginal distributions of target samples for pedestrian detection. The second adaptive solution based on feature transformation and alignment has been declined in a large number of ways, all based on searching a common subspace to minimize the difference among the corresponding domain distributions. Feature transformation was obtained through metric learning in [10], [11], PCA in [12], while multiple intermediate pro- jection steps were considered in [13]. Intermediate features were also obtained in [14] by aligning source and target covariance matrices. Deep learning architectures for object classification have been modified to accommodate the second objective of minimizing a domain divergence measure [15], [16], [17]. An alternative way to measure domain similarity is that of discriminating among them and using the domain recognition loss in an adversarial min-max game while training for object classification [18]. Finally, a different solution based on the introduction of adaptive network layers for tunable batch normalization has shown high performance on several object classification benchmark datasets [19]. All the existing CNN-DA methods, with the notable exception of [20], are restricted by the assumption that samples of source and target share the same label space and that data of all the categories are provided at training time. In case only a subset of the target categories is available during training, a possible solution is to restrict also the source category set, but this implies that the classifier will not cover all the classes and it should be trained again in case new unlabeled target samples of the remaining classes become available. Alternatively the whole source can be used for adaptation with the partial target but this unbalanced condition generally affect the final classification performance. Our work overcomes this limitation, by proposing a modular approach that yields robustness with respect to variations in the number of classes from the source to the target domains.
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VIDEO FOREGROUND LOCALIZATION FROM TRADITIONAL METHODS TO DEEP LEARNING

VIDEO FOREGROUND LOCALIZATION FROM TRADITIONAL METHODS TO DEEP LEARNING

Then, all these five categories can be grouped into pixel-based, region-based, and the combination of the two strategies. The sample-based algorithms create a BG from the past set of N frames, i.e., for each pixel location there are N samples stored. If there are k number of pixels in the BG that have a distance smaller than a threshold τ to the incoming pixel, then the pixel is classified as FG. The probabilistic models work on the principle of stochastic process, like Gaussian mixture models (GMM) [3, 94] and Conditional Random Field (CRF)-based algorithms [187]. The subspace- based approaches perform a transformation of data to a subspace, such as Eigenspace or Principal Component Analysis (PCA)-based subspace. Then, they form a BG model using the subspace and estimate the FG. The Code-book generates a dictionary that consists of color, intensity, temporal features, or similar representations. Same properties of a new pixel are compared with the dictionary values to determine it’s status. The NN-based models are kind of generating a classifier through training to handle the segmentation task. The trained weights of a NN serve as BG model and can be updated to reflect the changes occurred in the scene. Here, a learning system, which formulates FGL as a structured input-output matching problem. Such models have gained their reputation after breakthrough performances in the ImageNet-Large- Scale Visual Recognition Challenge (ILSVRC). The NN-based techniques have been exploited for image semantics/ labeling [130, 178], medical image partitioning [109, 119], and recently for video FG segmentation [169] as well. The main challenges in CNN-based FG detection is that dealing with time-dependent motion and the dithering effect at bordering pixels of FG objects. We address these issues in Chapters 7, 8, and 9, by excogitating an encoder-decoder (EnDec) CNN-LSTM that utilizes ResNet [56]-like residual connections for lost feature recovery and LSTM units to handle spatiotemporal motion of FG objects. To facilitate the training process, we take advantage of intra-domain transfer learning.
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Medinoid : computer-aided diagnosis and localization of glaucoma using deep learning

Medinoid : computer-aided diagnosis and localization of glaucoma using deep learning

InceptionNets: Unlike VGGNets and ResNets, InceptionNets [31] make it possible to better focus on the location of features that are important for the purpose of classification. Since salient parts can have different sizes per image, choosing the right receptive field size is difficult. In addition, in deep networks, there is the issue of overfitting and the issue of vanishing gradients. Therefore, Google Research introduced a new module, called “inception”, having several receptive fields with a different size [43]. Specifically, filter sizes of 1 × 1, 3 × 3, and 5 × 5 were used, making the network wider. After max pooling, each output is concatenated and then sent to the next inception module. Over the course of time, inception modules have been modified and improved, leading to more powerful InceptionNets. Inception-ResNet-v2 and Inception-v4 are currently the most effective networks. By default, InceptionNets take as input images with a size of 299 × 299 × 3, with pixel values belonging to [ − 1, 1]. The optimizer used is also different from the previous networks: InceptionNets typically make use of the Root Mean Squared Propagation (RMSProp) optimizer [44].
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Indoor localization based on Received Signal Strength Indicator (RSSI) in Wireless Sensor Network (WSN)

Indoor localization based on Received Signal Strength Indicator (RSSI) in Wireless Sensor Network (WSN)

Range-based localization technique consists of several techniques such as received signal strength indication (RSSI), angle of arrival (AOA), time difference of arrival (TDOA), and time of arrival (TOA) (Du, 2018) . Among these techniques, the RSSI-based technique is relatively simple, less demanding on the hardware, and with low cost. So RSSI-based technique will be study more. Most of the researches enhance the localization accuracy through the use of various optimization techniques or combining a variety of localization algorithms (Qi, Liu, & Liu, 2018), with little regard to the real environment.
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Robust INS/GPS Sensor Fusion for UAV Localization Using SDRE Nonlinear Filtering

Robust INS/GPS Sensor Fusion for UAV Localization Using SDRE Nonlinear Filtering

Other data fusion techniques based on probabilistic approaches were presented and used in the literature. One of these techniques is Particle Filter (PF) [3], [4]. The main drawback of this filter is its computational requirement, which makes it not very suitable for real time applications such as aerial navigation problem. Approaches based on Unscented Transform (UT) resulted in a technique called Unscented Kalman Filter (UKF) [5]. This technique preserves the linear update structure of Kalman filter. It uses only second order system moments, which may not be sufficient for some nonlinear systems. In addition, the number of sigma points, used in UKF, is small and may not represent adequately complicated distributions. Moreover, unscented transformation of the sigma-points is computationally heavy, which is not suitable and practical for real time aerial navigation applications.
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Source localization in reverberant rooms using Deep Learning and microphone arrays

Source localization in reverberant rooms using Deep Learning and microphone arrays

In this paper, we presented BeamLearning, a machine learning approach for the sound source localization task, using raw multichannel audio. The proposed neural network allows to estimate a sound source’s angular po- sition, with a higher accuracy and a better robustness to noisy measurements than the traditional model-based MUSIC and SRP-PHAT methods. The proposed data-based approach is inspired by the filter and sum approach used in traditional beamforming, and allows to estimate a source position in real time, using a neural network which mainly consists in successive learnable filterbanks based on residual subnetworks of depthwise separable atrous 1D-convolutions, followed by a pseudo-energy computation over the learnt channels. We also show that the success of this approach strongly relies on the dataset used in the training phase, which has been built using an efficient implementation on the GPU of a large number of RIR, and a batch convolution on the GPU of these computed RIRs with real life recordings. In a future work, an experimental dataset will be presented, based on computed RIRs and the use 3D sound field synthesis using higher ambisonics. Preliminary results on this experimental dataset show that similar improvements are observed, with the main advantage of allowing the neural network to build the best representation in order to compensate the individual microphone frequency re- sponses and the diffraction effects induced by the microphone array structure, which are not taken into account using standard model-based methods.
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Practical indoor localization using bluetooth

Practical indoor localization using bluetooth

Fernandez et al. [9] recognize that signal propagation may be affected by changes in the en- vironment and thus can invalidate calibration data. They propose a localization system that automatically updates its calibration data using fixed reference devices, similar to the automatic calibration of the LNS model described in section 6.2.1. Other approaches to cope with changes in the environment that affect signal propagation are presented in [13, 30]. Although these works are based on Wi-Fi, the principles also apply for Bluetooth. Haeberlen et al. [13] address the issue by introducing a linear calibration function that maps observed RSS values in the online phase to RSS values as they would have been observed in the training phase. To obtain the parameters for calibration function they use a history of recent observations from which they construct an esti- mate of the calibration parameters. Yin et al. [30] argue that calibration function used in [13] to adapt to environmental changes cannot be uniformly performed across all locations. Instead they present a new algorithm called Location Estimation using Model Trees, that is able to better cope with the non-uniform nature of the environmental changes. This algorithm is based on learning mapping functions in the training phase and dynamically computing the expected signal strength vector spaces in the online phase. The disadvantage of the latter approach is that it still requires manual recalibration, although the amount of effort is significantly reduced.
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Ensemble learning particle swarm optimization for real time UWB indoor localization

Ensemble learning particle swarm optimization for real time UWB indoor localization

With the popularization of smart devices and the devel- opment of mobile Internet, there is an increasing de- mand for indoor positioning. Indoor localization-based services can support many application scenarios, such as public security and emergency response and positioning navigation. Diverse technologies have been developed for precise indoor localization. Localization technology based on Global Position System (GPS) and maps have been widely used. But GPS location signals are not able to penetrate buildings; they are unable to work indoors. In order to overcome the GPS positioning defects and realize the accurate positioning in the complex indoor en- vironment, many practical indoor localization schemes are introduced, such as infrared, WIFI, Bluetooth, ZigBee, ultrasound, radio frequency identification (RFID), and ultra-wideband (UWB). Infrared [1] is limited by its prop- erties and vulnerable to the external environment; the po- sitioning accuracy can only be up to 5 m. WIFI [2], Bluetooth [3], and ZigBee [4] can only locate the area of about a few tens of meters, and its positioning ac- curacy can only reach 3 m, unable to meet the indoor mobile positioning demand. Ultrasonic [5] indoor
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A Deep Learning Approach for Wi-Fi Based People Localization

A Deep Learning Approach for Wi-Fi Based People Localization

Recently, there is a trend of using deep learning for Wi-Fi based localization systems [31-35], [54]. Fang and Lin [31] proposed a system that uses a neural network with a single hidden layer to extract features from Received Signal Strength (RSS). It was able to improve the localization error to below 2.5m, which is 17% improvement over state of the art approaches. A system called DeepFi was proposed in [33] with four layers neural network. DeepFi was able to improve the accuracy by 20% over the FIFS system, which uses a probability based model. A system called CiFi was proposed in [34], it used a convolutional network for indoor localization based on Wi-Fi signals. First, the phase data was extracted from the channel state information (CSI), then the phase data is used to estimate the angle of arrival (AOA). which is used as an input to the convolutional network. The results show that CiFi has an error of less than 1 m for 40% of the test locations, while for other approaches it is 30%. In addition, it has an error of less than 3 m for 87% of the test locations, while for DeepFi it is 73%. In [35], A system called ConFi was proposed, which is a CNN based Wi-Fi localization technique that uses CSI as features. The CSI was organized as a CSI feature image, where the CSIs at different times and different subcarriers were arranged into a matrix. The CNN consists of three convolutional layers and two fully connected layers. The network is trained using the CSI feature images. ConFi was able to reduce the mean error by 9.2% and 21.64% over DeepFi and DANN respectively.
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