Optimal Target Tracking Based on Dynamic Fingerprint in Indoor Wireless Network

This chapter provides an overview of the trends in wireless indoor networks. Since the late 1970's when the concept of wireless LAN was first introduced, this area has gone through significant ups and downs. After a very exciting development period in the late 1980's, market revenues still remained far below predictions in the mid 1990's. However, during the late 1990's with the introduction of wireless personal area networking, Bluetooth technology, and home networking, a new surge of excitement has emerged in the wireless indoor communication industry, resulting in a number of new start up projects. Furthermore in the past few years, the emergence of wireless indoor positioning systems to augment wireless indoor telecommunication services, has attracted tremendous attention to wireless indoor networks.

This paper presents an automatic planning software tool for indoor wireless networks. As a consequence of increasing number of wireless interface based systems, automation of deployments becomes actually relevant. There are a lot of parameters that influences network performance, so designers have to take them into account. The larger the network is, the more complex its planning results are. Planning determines performance, and getting it manually is not efficient. Mentioned tool automatically looks for an optimal planning solution, in terms of base stations distribution and frequency planning. It consists of analyzing data obtained by means of environment simulation. The user is able to decide the optimization criterion and the algorithm for searching solutions. This tool obtains an optimal solution in a short period of time, and it shows results such as power received at each user spatial point, interference level or traffic load for each station.

In this paper, we introduce two indoor Wireless Local Area Network (WLAN) positioning methods using augmented sparse recovery algorithms. These schemes render a sparse user's position vector, and in parallel, minimize the distance between the online measurement and radio map. The overall localization scheme for both methods consists of three steps: 1) coarse localization, obtained from comparing the online measurements with clustered radio map. A novel graph-based method is proposed to cluster the offline fingerprints. In the online phase, a Region Of Interest (ROI) is selected within which we search for the user's location; 2) Access Point (AP) selection; and 3) fine localization through the novel sparse recovery algorithms. Since the online measurements are subject to inordinate measurement readings, called outliers, the sparse recovery methods are modified in order to jointly estimate the outliers and user's position vector. The outlier detection procedure identifies the APs whose readings are either not available or erroneous. The proposed localization methods have been tested with Received Signal Strength (RSS) measurements in a typical office environment and the results show that they can localize the user with significantly high accuracy and resolution which is superior to the results from competing WLAN fingerprinting localization methods.

Multimedia wireless networks are addressed at the in-building network market where they will ultimately offer a flexible and mobile alternative to wired LANs. The primary application of the technology is in the wireless communication of voice, data, and video signals. The potential market for indoor wireless LANs is considerable for both the commercial and domestic sectors. The choice of an appropriate transmission frequency for indoor wireless networks is an important one. The NII/SUPERNET/HIPERLAN band at 5 GHz is a strong contender as are those frequencies around 60 GHz. Future indoor wireless networks are expected to have data rates in excess of 100 Mbits/s in order to be compatible with future wired networks. A pivotal element in the design of future wireless LANs is knowledge of the propagation environment in which such systems are expected to be used. The indoor propagation channel is a complex one and it is dependent on the age, structure, and function of the building, the layout and occupancy of rooms, the presence of exterior and internal walls and windows, and the construction materials used. The paper investigates the indoor electromagnetic propagation environment at microwave and millimetre frequencies. This in turn requires examination of the mechanisms of electromagnetic wave propagation and the effects of the electromagnetic properties of building materials at these frequencies. Ultimately this study compares microwave and millimetre frequencies in terms of their respective suitability and feasibility for broadband indoor wireless communications.

This paper investigated the problem of moving target recognition and tracking in unknown environments for autonomous mobile robots. The mobile robot is equipped with the Kinect sensor to acquire the appearance and depth information of the environment for target recognition and motion control. As for the visual target recognition, the depth feature is fused with the color feature on the basis of the Kernel Correlation Filtering algorithm (KCF), and the Kalman filtering algorithm based on the target motion state is integrated with the KCF, to cope with such situations as the target occlusion in tracking process. The movement of the robot can be controlled according to the results of visual target recognition and the standard of keeping the target in the central area of the mobile robot’s vision. Finally, controlling the mobile robot to track the moving target is realized. The experimental results on Kobuki robot platform prove the effectiveness of the presented method.

The ability to estimate the location of wireless device users has led to many applications that use location information to provide users and service providers with commercially valuable real time information. In this paper, we propose a low-complexity method for location estimation in an indoor wireless local area network (WLAN) deployment. The proposed method relies on measurements of signal strength (SS) of multiple access-points (APs) in a WLAN and is based on a piecewise-linear model that represents distances from APs in terms of SS measurements. The proposed scheme has significantly lower computational and storage requirements as compared to conventional location estimation methods.

In the 5G era, the communication networks tend to be ultra-densified, which will improve the accuracy of indoor positioning and further improve the quality of positioning service. In this study, we propose an indoor three-dimensional (3D) dynamic reconstruction fingerprint matching algorithm (DSR-FP) in a 5G ultra-dense network. The first step of the algorithm is to construct a local fingerprint matrix having low-rank characteristics using partial fingerprint data, and then reconstruct the local matrix as a complete fingerprint library using the FPCA reconstruction algorithm. In the second step of the algorithm, a dynamic base station matching strategy is used to screen out the best quality service base stations and multiple sub-optimal service base stations. Then, the fingerprints of the other base station numbers are eliminated from the fingerprint database to simplify the fingerprint database. Finally, the 3D estimated coordinates of the point to be located are obtained through the K-nearest neighbor matching algorithm. The analysis of the simulation results demonstrates that the average relative error between the reconstructed fingerprint database by the DSR-FP algorithm and the original fingerprint database is 1.21%, indicating that the accuracy of the reconstruction fingerprint database is high, and the influence of the location error can be ignored. The positioning error of the DSR-FP algorithm is less than 0.31 m. Furthermore, at the same signal-to-noise ratio, the positioning error of the DSR-FP algorithm is lesser than that of the traditional fingerprint matching algorithm, while its positioning accuracy is higher.

This work aims at evaluating the performance of a direct sequence spread-spectrum (DS-SS) system over a 60 GHz real indoor channel characterized by the presence of severe multipath. The channel has been described by 1680 impulse response data calculated by means of a 3D ray-tracing program. Both multipath components with delays shorter and longer than the chip time have been examined, and a special emphasis has been placed onto the study of the multipath effects on the DLL tracking circuit. The obtained results show the spatial distribution of the effective received energy per bit at the data demodulator input and provide some useful indications about the design criteria for an indoor wireless wideband local area network at EHF.

Target positioning and tracking in wireless sensor networks (WSNs) are necessary for real ap-plications. This paper uses an artificial fish swarm algorithm (AFSA) with receive signal strength indicator (RSSI) for indoor target positioning and tracking. The performance of random and fixed artificial fish deployment topologies in AFSA for target positioning and tracking is investigated when the number of artificial fish is 12, 24, 52, 72, and 100, respectively. The simulation results show that the difference in average error between the random and fixed deployments is 5%, and the average positioning time is 2% for target position. The target tracking performance of fixed deployment is better than that of random one when the number of artificial fish is small.

A distributed two-tier near optimal algorithm is proposed for target tracking in sensor networks. Tier one is a multiple hypothesis tracking (MHT) algorithm where the Viterbi algorithm is used. In this tier, only binary data is used to obtain a rough region around the target. Tier two improves the accuracy of the MHT decision by localized maximum likelihood. This reduces the computational complexity and the communication costs between sensors over the global maximum likelihood approach. It also results in higher sensor power efficiency, hence longer service time of the tracking network. This two-tier system is a distributed near optimal tracking algorithm. The localized maximum likelihood tracking can tolerate errors made by the Viterbi algorithm in tier one, hence the overall algorithm is robust.

Target tracking in stand-forward swarm jamming for unmanned aerial vehicles (UAVs) is a promising topic but the relevant research has been tepid. This paper aims at exploring this problem for multistatic radar-enabled UAVs (RUAVs) via multiagent cooperative sensing and control scheme, in which all RUAVs constitute a multistatic radar system with variable and movable transmitting and receiving platforms, and each RUAV as an autonomous agent makes decision with other agents for transmit-receive task assignment and path planning to seek the optimal overall target tracking performance. The optimization problem of RUAVs is formulated as minimizing the Bayesian Cramer-Rao lower bound with some practical constraints. ´ To solve this problem, an alternating iterative optimization scheme is presented, in which the coalition formation method is applied for transmit-receive task assignment and the improved particle swarm optimization (PSO) method is devised for path planning. The alternating coalition formation and PSO (ACFPSO) algorithm is therefore proposed for RUAVs for target tracking in stand-forward jamming. Numerical simulations in the typical scenario are conducted to verify the effectiveness and superiority of the ACFPSO algorithm from aspects of tracking accuracy and jamming elimination.

We describe a broadband indoor wireless system, where the base stations are connected through an ATM network. The ATM switch provides backbone connectivity among microcells as well as with the outside world. This paper investigates a broadband indoor wireless network (BIWN), employing time division multiple access (TDMA), based on polling for dynamic bandwidth allocation. The paper evaluates the performance of an indoor wireless network supporting high speed bursty traffic using a developed, simplified simulation approach.

Interference among co-channel users is a fundamental problem in wireless networks, which prevents nearby links from operating concurrently. Directional antennas allow the radiation patterns of wireless transmitters to be shaped to form directed beams. Conventionally, such beams are assumed to improve the spatial reuse (i.e. concurrency) in indoor wireless networks. In this paper, we use experiments in an indoor office setting of Wifi Access points equipped with directional antennas, to study their potential for interference mitigation and spatial reuse. In contrast to conventional wisdom, we observe that the interference mitigation benefits of directional antennas are minimal. On analyzing our experimental traces we observe that directional links do not reduce interference to nearby links due to the lack of signal confinement due to indoor multipath fading. We then use the insights derived from our study to develop an alternative approach that provides better interference reduction in indoor networks compared to directional links.

Location aware computing is popularized and use of location information has become important due to huge application of mobile computing devices and local area wireless networks. To serve us well, mobile computing applications need to know the physical location of things so that they can record them and report them to us. Therefore in the future ubiquitous services, location estimation will be a key technology. This paper present distributed growing radial basis function neural networks (DGRBFNN) for location estimation of mobile device in wireless networks. DGRBFNN partitions the location space into clusters on the basis of availability of signals from various access points, and employ separate neural network architecture for each cluster to estimate the location of mobile device in indoor wireless networks. It provides better location estimation results than other approaches and systematically caters for unavailable signals at estimation time.

Map information can assist indoor localization to avoid improbable cases and achieve accurate location estimation. In this paper, we proposed a automatic method to extract useful information from indoor map as spatial skeleton database (SSD). Based on conventional probabilistic fingerprinting technique and particle filter tracking algorithm, we also proposed spatial skeleton-based dynamic probabilistic fingerprinting database (S-DFD) to filter out reference points (RPs) in fingerprinting database according to the previous target location and the walking distance between RPs. Finally, we proposed a spatial skeleton-based particle filter tracking (S-PT) which use SSD to construct realistic transition model. According to the experiment result, the whole system consists of SSD, S-DFD and S-PT called spatial skeleton-enhanced location tracking for indoor localization (SELT) can achieve accurate location estimation.