Prior Assumptions for Leak Localisation in Water Distribution Networks with Uncertainties

Hydraulic model-based leak (burst) localisation in water distribution networks is a challenging problem due to a limited number of hydraulic measurements, a wide range of leak properties, and model and data uncertainties. In this study, prior assumptions are investigated to improve the leak localisation in the presence of uncertainties. For example, $$\ell _2$$ ℓ 2 -regularisation relies on the assumption that the Euclidean norm of the leak coefficient vector should be minimised. This approach is compared with a method based on the sensitivity matrix, which assumes the existence of only a single leak. The results show that while the sensitivity matrix method often yields a better leak location estimate in single leak scenarios, the $$\ell _2$$ ℓ 2 -regularisation successfully identifies a search area for pinpointing the accurate leak location. Furthermore, it is shown that the additional error introduced by a quadratic approximation of the Hazen-Williams formula for the solution of the localisation problem is negligible given the uncertainties in Hazen-Williams resistance coefficients in operational water network models.

EXPRESS: Cue Combination in Goal-Oriented Navigation

This study examined cue combination of self-motion and landmark cues in goal-localization. In an immersive virtual environment, before walking a two-leg path, participants learned the locations of three goal objects (one at the path origin, i.e., home) and landmarks. After walking the path without seeing landmarks or goals, participants indicated the locations of the home and non-home goals in four conditions: 1) path integration only, 2) landmarks only, 3) both path integration and the landmarks, and 4) path integration and rotated landmarks. The ratio of the length between the testing position (P) and the turning point (T) over the length between the T and the three goals (G) (i.e., PT/TG) was manipulated. The results showed the cue combination consistently for participants’ heading estimates but not for goal-localization. In Experiments 1-2 (using distal landmarks), the cue combination for goal estimates appeared in a small length ratio (PT/TG=0.5) but disappeared in a large length ratio (PT/TG=2). In Experiments 3-4 (using proximal landmarks), while the cue combination disappeared for the home with a medium length ratio (PT/TG=1), it appeared for the non-home goal with a large length ratio (PT/TG=2) and only disappeared with a very large length ratio (PT/TG=3). These findings are explained by a model stipulating that cue combination occurs in self-localization (e.g., heading estimates), which leads to one estimate of the goal location; proximal landmarks produce another goal location estimate; these two goal estimates are then combined, which may only occur for non-home goals.

Forward-backward recursive expectation-maximization for concurrent speaker tracking

In this paper, a study addressing the task of tracking multiple concurrent speakers in reverberant conditions is presented. Since both past and future observations can contribute to the current location estimate, we propose a forward-backward approach, which improves tracking accuracy by introducing near-future data to the estimator, in the cost of an additional short latency. Unlike classical target tracking, we apply a non-Bayesian approach, which does not make assumptions with respect to the target trajectories, except for assuming a realistic change in the parameters due to natural behaviour. The proposed method is based on the recursive expectation-maximization (REM) approach. The new method is dubbed forward-backward recursive expectation-maximization (FB-REM). The performance is demonstrated using an experimental study, where the tested scenarios involve both simulated and recorded signals, with typical reverberation levels and multiple moving sources. It is shown that the proposed algorithm outperforms the regular common causal (REM).

Vehicular Localization Enhancement via Consensus

This paper presents a strategy to cooperatively enhance the vehicular localization in vehicle-to-everything (V2X) networks by exchanges and updates of local data in a consensus-based manner. Where each vehicle in the network can obtain its location estimate despite its possible inaccuracy, the proposed strategy takes advantage of the abundance of the local estimates to improve the overall accuracy. During the execution of the strategy, vehicles exchange each other’s inter-vehicular relationship pertaining to measured distances and angles in order to update their own estimates. The iteration of the update rules leads to averaging out the measurement errors within the network, resulting in all vehicles’ localization error to retain similar magnitudes and orientations with respect to the ground truth locations. Furthermore, the estimate error of the anchor—the vehicle with the most reliable localization performance—is temporarily aggravated through the iteration. Such circumstances are exploited to simultaneously counteract the estimate errors and effectively improve the localization performance. Simulated experiments are conducted in order to observe the nature and its effects of the operations. The outcomes of the experiments and analysis of the protocol suggest that the presented technique successfully enhances the localization performances, while making additional insights regarding performance according to environmental changes and different implementation techniques.

FRACTAL ANALYSIS FOR AKAMURA MOUNDED TOMB AND LOCATION ESTIMATE OF YAMATAI COUNTRY

In 2018, the ancient tomb was discovered in Tagawa, Fukuoka. The sizes of the tomb were measured by UAV. The length is 443 m, the diameter is 152 m, and the height is 33 m: the second biggest mound in Japan. Chinese history book, Sanguozhi (Chen, 280) reads Yamatai country occupied 27 countries in Japan in the 3rd century by the queen Himiko. The location of Yamatai was 840 km south from Soul with 70000 families, and the size of Himiko’s tomb was 150 m diameters. The locations of Yamatai country and Himiko’s tomb are still unknown. From Sanguozhi, the optimum location was estimated with the direction, the distance and family numbers. Family numbers were estimated from the river watershed areas. Kyushu islands have 28 big rivers, corresponding to 28 countries described in Sanguozhi. Each river corresponded to each country, and their family number was estimated by 2.5% of the watershed area with family number / 10 a. Finally, the Chikugo river watershed was a candidate of Yamatai country. Moreover, two kinds of tombs became candidates of Himiko’s tomb.

Performance Analysis of AOA-Based Localization Using the LS Approach: Explicit Expression of Mean-Squared Error

In this paper, a passive localization of the emitter using noisy angle-of-arrival (AOA) measurements, called Brown DWLS (Distance Weighted Least Squares) algorithm, is considered. The accuracy of AOA-based localization is quantified by the mean-squared error. Various estimates of the AOA-localization algorithm have been derived (Doğançay and Hmam, 2008). Explicit expression of the location estimate of the previous study is used to get an analytic expression of the mean-squared error (MSE) of one of the various estimates. To validate the derived expression, we compare the MSE from the Monte Carlo simulation with the analytically derived MSE.

Noniterative Three-Dimensional Location Estimation Using Azimuth and Elevation Measurements at Multiple Locations

Closed-form expression of three-dimensional emitter location estimation using azimuth and elevation measurements at multiple locations is presented in this paper. The three-dimensional location estimate is obtained from three-dimensional sensor locations and the azimuth and elevation measurements at each sensor location. Since the formulation is not iterative, it is not computationally intensive and does not need initial location estimate. Numerical results are presented to show the validity of the proposed scheme.

Tracking the Movements of Juvenile Chinook Salmon using an Autonomous Underwater Vehicle under Payload Control

An autonomous underwater vehicle (AUV) under payload control (PC) was used to map the movements of juvenile Chinook salmon (Oncorhynchus tshawytscha) tagged with acoustic transmitters. After detecting a tag, the AUV deviated from its pre-programmed route and performed a maneuver designed to enhance the location estimate of the fish and to move closer to collect proximal environmental data. Nineteen fish were released into marine waters of southeastern Alaska. Seven missions with concurrent AUV and vessel-based surveys were conducted with two to nine fish present in the area per mission. The AUV was able to repeatedly detect and estimate the location of the fish, even when multiple individuals were present. Although less effective at detecting the fish, location estimates from the vessel-based surveys helped verify the veracity of the AUV data. All of the fish left the area within 48 h of release. Most fish exhibited localized movements (milling behavior) before leaving the area. Dispersal rates calculated for the fish suggest that error associated with the location estimates was minimal. The average movement rate was 0.62 body length per second and was comparable to marine movement rates reported for other Chinook salmon stocks. These results suggest that AUV-based payload control can provide an effective method for mapping the movements of marine fish.

A new fast and calibration-free method for footstep impact localization in an instrumented floor

Impact localization in a floor is complicated due to the dispersion-caused distortion of the generated floor waves. Current localization methods that try to overcome the dispersion problem are computationally expensive, taking in some cases 2 seconds to yield a single footstep location estimate. If an accelerometer sensor network is utilized to localize footsteps, and consequently track an occupant's path, then there is a need for computationally fast algorithms that are able to keep up with the walking (or running) impact frequency; therefore, in this paper, a practical algorithm is proposed for footstep impact localization in an instrumented floor. The proposed algorithm has promising sub-meter localization accuracy and is computationally fast. In addition, the algorithm does not require estimation of floor-dependent parameters, which is an additional advantage since estimating floor-dependent parameters in a floor will have relatively high uncertainty as the floor cannot be treated as an isotropic/homogeneous material. The proposed algorithm is evaluated using simulations and an experiment in an operational smart building.