Human-to-human interaction across distributed applications requires that sufficient consistency be maintained among participants in the face of network characteristics such as latency and limited bandwidth. Techniques and approaches for reducing bandwidth usage can minimise network delays by reducing the network traffic and therefore better exploiting available bandwidth. However, these approaches induce inconsistencies within the level of human perception. Deadreckoning is a well-known technique for reducing the number of update packets transmitted between participating nodes. It employs a distance threshold for deciding when to generate update packets. This paper questions the use of such a distance threshold in the context of absolute consistency and it highlights a major drawback with such a technique. An alternative threshold criterion based on time and distance is examined and it is compared to the distance only threshold. A drawback with this proposed technique is also identified and a hybrid threshold criterion is then proposed. However, the trade-off between spatial and temporal inconsistency remains.
Route taken and distance travelled are important parameters for studies of ani- mal locomotion. They are often measured using a collar equipped with GPS. Collar weight restrictions limit battery size, which leads to a compromise between collar operating life and GPS fix rate. In studies that rely on linear interpolation between intermittent GPS fixes, path tortuosity will often lead to inaccurate path and distance travelled estimates. Here, we investigate whether GPS-corrected deadreckoning can improve the accuracy of localization and dis- tance travelled estimates while maximizing collar operating life. Custom-built tracking collars were deployed on nine freely exercising domestic dogs to collect high fix rate GPS data. Simulations were carried out to measure the extent to which combining accelerometer-based speed and magnetometer heading esti- mates (deadreckoning) with low fix rate GPS drift correction could improve the accuracy of path and distance travelled estimates. In our study, median 2-dimen- sional root-mean-squared (2D-RMS) position error was between 158 and 463 m (median path length 16.43 km) and distance travelled was underestimated by between 30% and 64% when a GPS position fix was taken every 5 min. Deadreckoning with GPS drift correction (1 GPS fix every 5 min) reduced 2D-RMS position error to between 15 and 38 m and distance travelled to between an underestimation of 2% and an overestimation of 5%. Achieving this accuracy from GPS alone would require approximately 12 fixes every minute and result in a battery life of approximately 11 days; deadreckoning reduces the number of fixes required, enabling a collar life of approximately 10 months. Our results are generally applicable to GPS-based tracking studies of quadrupedal animals and could be applied to studies of energetics, behavioral ecology, and locomotion. This low-cost approach overcomes the limitation of low fix rate GPS and enables the long-term deployment of lightweight GPS collars.
Today, the AUV are one of the most effective tools for study and development of the oceans. The navigation sensors are very important components of AUV, because its signals are used to generate trajectories of AUV. Usually, the basis of the navigation system of AUV is deadreckoning system (DRS), which includes navigation sensors. Since the faults or malfunction of these sensors can lead to errors in the implementation of underwater mission or to loss of expensive AUV, the task of timely faults detection is important. In case of faults arising, the diagnostic system should send information about detected faults to control system of AUV that should decide to stop the mission or to continue with by using special correction of control signals (case of fault tolerant control). Forming of such signals is an important part of increasing reliability problem. The solution to this task is called the accommodation of faults .
One of the localization most and simple important techniques is the deadreckoning. This technique estimates the next robot location as a function of time using the current location and other commands like output velocity and steering. Deadreckoning varies according the design of the mobile robot. In the case of differential drive mobile robot (DDMR) which has two individual wheels on common axle, the robot should follow arc curves around a point lies on the wheel common axle  provided that the two wheels are fixed and have a fixed and flat contact with the ground.
Dead-reckoning has been employed for tracking aquatic species [30, 33, 35, 38–40] but is yet to be used for species that utilise terrestrial locomotion. This is partly because of the difficulty for determining the speed of terrestrial ani- mals , a process which is simpler underwater where mechanical methods can be used due to the density and viscosity of water [42–50]. However, an ability to estimate speed reliably for land animals should, in fact, make terrestrial dead-reckoning more straightforward than for aquatic or volant species  because terrestrial movement is not subject to drift due to air flow  or ocean currents . Thus, the primary difficulty for ter- restrial dead-reckoning may simply be the measurement of speed, and, were this to be provided, that this approach should provide a means to determine latent positions of animals between less frequent location data obtain by other means of telemetry .
and Svalbard [71–73]. These experiments were aimed at quantifying the behavioural effects of 1.3-2 kHz naval ac- tive sonar and to test the effectiveness of a mitigation measure called ‘ramp up’ . The whales were tagged with multi-sensor data loggers and Fastloc-GPS loggers, and were subsequently tracked by visual observers from a small boat. The distance between the whale and the sound source during experiments was a crucial param- eter; therefore, the main objective of this study was to develop SSMs to reconstruct whale tracks from dead- reckoning, Fastloc-GPS, and visual observations. A sec- ondary objective was to quantify the spatial accuracy of the Fastloc-GPS and visual (range and bearing) observa- tions in dedicated tests, so that the observation errors included in our models would be realistic. The track re- construction method presented here is easy to imple- ment and has potential application for a wide range of marine animal species and data recording systems. Example software and model code that users can adapt for their own research questions are provided as supple- mentary materials (Additional file 1).
Evidently, using a spatial threshold in the deadreckoning prediction contract mechanism is questionable, as varying its value within a heavily loaded network has an uncertain impact on the value of inconsistency. Furthermore, the spatial threshold also exhibits another performance related drawback. Consider the diagram in Figure 1(a). In this case, the local user remains within the distance threshold δ, but outside the perceivable error ε. Such a scenario could arise in a network racing car game, for example, where an entity navigates a straight section of the racing track. As far as the spatial threshold is concerned, the position of the entity is considered to be spatially consistent, as it remains within the distance error threshold, so no deadreckoning updates are generated. However, the position of the entity then remains spatially inconsistent over an extended period of time. This scenario could result in interaction difficulties. For example, in the case of the racing game, one driver might see a successful overtaking manoeuvre, while the other sees a collision. In this case, an objective outcome may need to be agreed between the distributed processes, which may leave at least one driver confused. The chances of this occurring can be reduced by sending an update when a spatial inconsistency below the threshold value persists over an extended period of time, as shown in Figure 1(b).
Fuzzy logic unlike classical logic is tolerant to imprecision, uncertainty, and nonlinearity. In the context of mobile robot navigation, a fuzzy logic-based system has the advantage in that it allows an intuitive nature of rule-based navigation to be easily modelled using linguistic terminology . The block diagram of the overall structure of the control system is shown in Figure 3. It consists of three parts i.e. the system inputs, fuzzy inputs and fuzzy control outputs. The system inputs part can be treated as a pre-processing module where the calculations of the current position and heading angle error are determined by using deadreckoning technique. Crisp values from the calculation are sent to the fuzzy controller as fuzzy inputs i.e. heading error (θ e ) and distance error (d e ). The fuzzy controller
4. F Li, C Zhao, G Ding, J Gong, C Liu, F Zhao, A reliable and accurate indoor localization method using phone inertial sensors, in Proceedings of the 2012 ACM Conference on Ubiquitous Computing (ACM, New York, 2012), pp. 421 – 430 5. A Ali, S Siddharth, Z Syed, N El-Sheimy, An improved personal dead-reckoning algorithm for dynamically changing smartphone user modes, in Proceedings of the 25th International Technical Meeting of the Satellite Division of the Institute of Navigation (ION GNSS 2012) (Nashville), pp. 2432 – 2439. 17 – 21
Deadreckoning has the distinct advantage of providing autonomous positioning capabilities and is thus particularly attractive for indoor search and rescue operations. However positions provided by this method will unavoidably drift over time due to errors in measurements being integrated . The drift can be reduced by using shoe-mounted inertial sensors and resetting the velocity to zero at each footfall  and by combining the inertial measurements with data from an electronic compass through a Kalman filter in order to avoid drift in heading . It has been shown that disruptive mo- tion such as side-stepping, back-stepping, tight turns that are typical in search and rescue scenarios produce scaling errors and cause the travelled distance and thus the esti- mated position to drift even more than during normal walk- ing. Despite these limitations deadreckoning is the only completely self-contained location technique that requires no prior knowledge of the environment. This is why we and others attempt to address these limitations by combining deadreckoning with other complementary technologies.
Abstract. In view of user's requirements of high performance, easy operation and low cost with indoor location, data acquisition software based on Android platform utilize sensors built-in cellphone is used to study the pedestrian deadreckoning (PDR). Step detection and step length estimation are carried out by collecting leg swing angle while walking, and the classical step length estimation algorithm is improved. At the same time, the improved heuristic drift elimination algorithm is used to calculate the direction. Finally, the experiment results show that the PDR technology can better satisfy the requirements of indoor pedestrian positioning, and as for the low-cost multi-sensor. compared with the existing algorithm, the improved algorithm with higher positional accuracy.
Abstract—Currently, Pedestrian DeadReckoning (PDR) systems are becoming more attractive in market of indoor positioning. This is mainly due to the development of cheap and light Micro Electro-Mechanical Systems (MEMS) on smartphones and less requirement of additional infrastructures in indoor areas. However, it still faces the problem of drift accumulation and needs the support from external positioning systems. Vision-aided inertial navigation, as one possible solution to that problem, has become very popular in indoor localization with satisfied performance than individual PDR system. In the literature however, previous studies use fixed platform and the visual tracking uses feature-extraction-based methods. This paper instead contributes a distributed implementation of positioning system and uses deep learning for visual tracking. Meanwhile, as both inertial navigation and optical system can only provide relative positioning information, this paper contributes a method to integrate digital map with real geographical coordinates to supply absolute location. This hybrid system has been tested on two common operation systems of smartphones as iOS and Android, based on corresponded data collection apps respectively, in order to test the robustness of method. It also uses two different ways for calibration, by time synchronization of positions and heading calibration based on time steps. According to the results, localization information collected from both operation systems has been significantly improved after integrating with visual tracking data.
As the body completes its tip, more blood dribbles from the middle of the chest. After a careful inspection of the body’s anterior surface, and the patch of linoleum once underneath, the recorder reappears: “Small scratches on right arm above elbow. Large bloody injury present about the midline in the center of the chest. No other obvious signs of injury are observed at this time. Tentative ID is Norbert Andrews, visual ID by drivers license found in bed- room and other photos in residence, to be confirmed by relatives, finger- prints, or dental. Personal effects indicate two children and one wife or ex-wife as possible next-of-kin. No notifications known or made at this time.” Germaine helps unfold a new white cotton sheet along the right side of the body. Our gloved hands grasp the decedent’s ankles and wrists above the bags, placing him in the middle of his fresh shroud. They fold the uncontam- inated sheet over the body, wrapping the contents like a giant burrito to con- tain any possible trace evidence that may tag along for the ride. Red stains wick through the top of the sheet as I unzip a white body bag, and we lift the body onto the opened container then zip the contents securely in place. Little more can be done with the 6-foot, 228-pound, 57-year-old dead man at the scene. He rides with me to the county morgue, where I confirm identification, locate the family, and write my report for the chief medical examiner.
To reduce the number of connections and the number of messages being sent, the deadreckoning technique may be employed [Gossweiler et. al]. DeadReckoning is a form of replicated computing in that everyone participating in a multi-user system winds up simulating all the entities in the environment, a predefined set of algorithms are used by all entity nodes to extrapolate the behaviour of entities in the game, and an agreement on how far reality should be allowed to get from these extrapolation algorithms before a correction is issued [Aronson97].
Navigation is a fundamental requirement of autonomous mobile robots. Referring to , navigation can be defined as a “scene of determining position, location, course, and distance travelled to a known destination”. Navigation is used for the localization of the robot to do a required task. The localization is very important issue for the mobile robot to move autonomously to achieve the goal position in any environment. Mobile robot cannot execute all operations related with the navigation without the localization information. As the mobile robot moves around its environments, its actual position and orientation always differs from the position and orientation that it is commanded to hold. There are several types of method approach for the localization of the robot. For example trajectory planning, approaches in the presence of obstacle . The goal of this method is to find the optimal of robot trajectory consisting the both path and the motion along the path which avoid the collision with moving obstacle. From previous research, some papers are widely used localization algorithm known as deadreckoning. Deadreckoning is a process of estimating the value of any variable quantity by using an earlier value and adding whatever changes have occurred in the meantime.
Various algorithms developed for beacon node localization requires the knowledge of either the mobile node positions or an initial rough estimate of the beacon position. The node positions have been traditionally assigned using GPS or by odometric information gathered by onboard sensors. Rapid and accurate data collection, equipment calibration, and processing are required in most cases for odometry to be used effectively. Algorithms using a mobile node which collects distance information to all beacons and then calculates their node positions have been explained in .There are algorithms that use the distance between the beacon node and a group of localized nodes to gather the beacon position. Certain algorithms randomly assign positions to nodes and then select the best solution from the randomly proposed conditions. When an H-∞ filter or an Extended Kalman Filter (EKF) is used, an initial position estimation of the node positions is obtained using dead-reckoning information from (onboard) sensors . The Dolphin positioning system uses wideband ultrasonic positioning system to locate devices. It requires previously surveyed beacon node position along with the beacon node positions to locate the target . The 3D-locus before introducing auto-calibration  used a mobile node with known position to calibrate the beacon node positions .
There are essentially two methods of using ZUPT in MEMS-based pedestrian navigation system (which will be discussed further in section II). First is the use of a conventional deadreckoning method, for example in [3, 4], where the relative position is computed by integrating velocity data from normal mechanization equations from inertial navigation technology. This is performed by simply zeroing the velocity during each detected stance phase when user is walking. Second is the use of an estimation filter to estimate the errors in MEMS sensors, where ZUPT is used as velocity measurement updates to the filter [5, 6, 7, 8].
and additional techniques are utilised to further minimise the amount of trac generated. In particular: replication of entities and deadreckoning are used to allow the behaviour of a remote entity to be simulated locally with a minimum amount of remote communication; subscription to events can be parameterised (using notify constraints) so that entities need only be notied of selected occurrences of a particular type of event; entities can also be organised into zones with the potential propagation of events restricted to entities within a zone.