Large Localization Errors Research Articles

Underwater sensor networks localization based on mobility-constrained beacon

Node localization is one of the key technologies of Underwater Sensor Networks (UWSN). Because of the unique ocean environment conditions, beacon nodes are difficult to deploy precisely and move under the action of ocean currents and tides. Usually the beacon nodes are fixed to the sea floor through anchors and cables, and they can move within a certain range. Most existing localization algorithms do not take such mobility of beacon nodes into account, resulting in large localization errors. In order to overcome this disadvantage, this paper analyzes the characteristics of mobile-constrained beacon nodes and proposes a new localization algorithm in UWSN, namely, Mobile-constrained beacon based localization algorithm. It solves the problem that the position of beacon node is dynamic and imprecise. The unknown node can be located by the geometric relationship between the position of the anchor and the moving radius of the beacon node. In the calculation process, the polygonal area is transformed into a rectangular area, and the three-dimensional (3D) space is mapped into a two-dimensional (2D) space, the calculation process is simplified, and the feasibility of the algorithm is improved. The algorithm can be used both for 2D and 3D localization. Experiment results show that the algorithm proposed in this paper improves the localization accuracy, reduces the error rate of network node location, and has good practicality.

A Pairwise SSD Fingerprinting Method of Smartphone Indoor Localization for Enhanced Usability

Smartphone indoor localization has attracted considerable attention over the past decade because of the considerable business potential in terms of indoor navigation and location-based services. In particular, Wi-Fi RSS (received signal strength) fingerprinting for indoor localization has received significant attention in the industry, for its advantage of freely using off-the-shelf APs (access points). However, RSS measured by heterogeneous mobile devices is generally biased due to the variety of embedded hardware, leading to a systematical mismatch between online measures and the pre-established radio maps. Additionally, the fingerprinting method based on a single RSS measurement usually suffers from signal fluctuations due to environmental changes or human body blockage, leading to possible large localization errors. In this context, this study proposes a space-constrained pairwise signal strength differences (PSSD) strategy to improve Wi-Fi fingerprinting reliability, and mitigate the effect of hardware bias of different smartphone devices on positioning accuracy without requiring a calibration process. With the efforts of these two aspects, the proposed solution enhances the usability of Wi-Fi fingerprint positioning. The PSSD approach consists of two critical operations in constructing particular fingerprints. First, we construct the signal strength difference (SSD) radio map of the area of interest, which uses the RSS differences between APs to minimize the device-dependent effect. Then, the pairwise RSS fingerprints are constructed by leveraging the time-series RSS measurements and potential spatial topology of pedestrian locations of these measurement epochs, and consequently reducing possible large positioning errors. To verify the proposed PSSD method, we carry out extensive experiments with various Android smartphones in a campus building. In the case of heterogeneous devices, the experimental results demonstrate that PSSD fingerprinting achieves a mean error ∼20% less than conventional RSS fingerprinting. In addition, PSSD fingerprinting achieves a 90-percentile accuracy of no greater than 5.5 m across the tested heterogeneous smartphones

Modified Incremental LMS with Improved Stability via Convex Combination of Two Adaptive Filters

In distributed networks, the conventional incremental mode of cooperation between the nodes may suffer instability due to two major reasons: (1) large local errors due to accidental problems, and (2) instability due to link failure or noisy link. This causes error propagation through the entire network resulting in divergence. In this research, we propose a novel incremental least mean square algorithm with improved stability by employing convex combination of two filters. Adaptation of one filter is based on the estimate of the adjacent node (incremental type), while that of the other is based on the estimate of the current local node at previous time instant. These two filters are then fused together by using a suitable mixing parameter. An adaptive mixing parameter is further proposed for this convex combination, ensuing dynamic assignment of the weights for the two combining filters. Steady state excess mean square error is derived for the proposed convex combination, and simulations are presented to validate the proposed claims.

Empirical analysis and modeling of Argos Doppler location errors in Romania.

BackgroundAdvances in wildlife tracking technology have allowed researchers to understand the spatial ecology of many terrestrial and aquatic animal species. Argos Doppler is a technology that is widely used for wildlife tracking owing to the small size and low weight of the Argos transmitters. This allows them to be fitted to small-bodied species. The longer lifespan of the Argos units in comparison to units outfitted with miniaturized global positioning system (GPS) technology has also recommended their use. In practice, large Argos location errors often occur due to communication conditions such as transmitter settings, local environment, and the behavior of the tracked individual.MethodsConsidering the geographic specificity of errors and the lack of benchmark studies in Eastern Europe, the research objectives were: (1) to evaluate the accuracy of Argos Doppler technology under various environmental conditions in Romania, (2) to investigate the effectiveness of straightforward destructive filters for improving Argos Doppler data quality, and (3) to provide guidelines for processing Argos Doppler wildlife monitoring data. The errors associated with Argos locations in four geographic locations in Romania were assessed during static, low-speed and high-speed tests. The effectiveness of the Douglas Argos distance angle filter algorithm was then evaluated to ascertain its effect on the minimization of localization errors.ResultsArgos locations received in the tests had larger associated horizontal errors than those indicated by the operator of the Argos system, including under ideal reception conditions. Positional errors were similar to those obtained in other studies outside of Europe. The errors were anisotropic, with larger longitudinal errors for the vast majority of the data. Errors were mostly related to speed of the Argos transmitter at the time of reception, but other factors such as topographical conditions and orientation of antenna at the time of the transmission also contributed to receiving low-quality data. The Douglas Argos filter successfully excluded the largest errors while retaining a large amount of data when the threshold was set to the local scale (two km).DiscussionFilter selection requires knowledge about the movement patterns and behavior of the species of interest, and the parametrization of the selected filter typically requires a trial and error approach. Selecting the proper filter reduces the errors while retaining a large amount of data. However, the post-processed data typically includes large positional errors; thus, we recommend incorporating Argos error metrics (e.g., error ellipse) or use complex modeling approaches when working with filtered data.

Measurement Data Fusion Based on Optimized Weighted Least-Squares Algorithm for Multi-Target Tracking

The outliers remove, the classification of effective measurements, and the weighted optimization method of the corresponding measurement are the main factors that affect the positioning accuracy based on range-based multi-target tracking in wireless sensor networks. In this paper, we develop an improved weighted least-square algorithm based on an enhanced non-naive Bayesian classifier (ENNBC) method. According to the ENNBC method, the outliers in the measurement data are removed effectively, dataset density peaks are found quickly, and remaining effective measurements are accurately classified. The ENNBC method improves the traditional direct classification method and took the dependence among continuous density attributes into account. Four common indexes of classifiers are used to evaluate the performance of the nine methods, i.e., the normal naive Bayesian, flexible naive Bayesian (FNB), the homologous model of FNB (FNB ROT ), support vector machine, k-means, fuzzy c-means (FCM), possibilistic c-means, possibilistic FCM, and our proposed ENNBC. The evaluation results show that ENNBC has the best performance based on the four indexes. Meanwhile, the multi-target tracking experimental results show that the proposed algorithm can reduce the root-mean-squared error of the position compared with the extended Kalman filter. In addition, the proposed algorithm has better robustness against large localization and tracking errors.

On the Use of a System-Scale Ascent/Descent Diagnostic for Short-Term Forecasting of Tropical Cyclone Development, Intensification and Decay

ABSTRACT The Brisbane Tropical Cyclone Warning Centre has used two forms of a thermal advection diagnostic to identify relatively large areas of isentropic ascent and descent for many years. When the thermodynamic conditions are favourable the ascent regions are correlated with significant outbreaks of convection that produce heavy rainfall. The diagnostic is based on the relationship between geostrophic winds that turn with height and flow perpendicular to thickness contours. As the relationship is also valid for the more general case of gradient winds, the diagnostic, in theory, should be useful for most heavy-rain-bearing tropical systems. A climatology of rainfall rate with one form of the diagnostic is presented at two Queensland locations (one tropical and one subtropical) that demonstrates a clear relationship between the isentropic ascent wind distribution and heavy to extreme rainfall.The diagnostics applied to numerical weather prediction models are valuable forecast tools as they identify heavy rainfall threat regions within which the extreme rain is likely to fall, whereas the rainfall from the same models is often under predicted or has large location errors. Applied to tropical lows and tropical cyclones the diagnostics have been used successfully to forecast tropical cyclone formation and rapid intensification and decay. Examples of such intensification and decay from around the world are presented, as well as a climatology of the diagnostic applied to intensifying tropical cyclones in the Australian region.

Buffer-Aided Model Predictive Controller to Mitigate Model Mismatches and Localization Errors

Any vehicle needs to be aware of its localization, destination, and neighboring vehicles’ state information for collision free navigation. A centralized controller computes controls for cooperative adaptive cruise control (CACC) vehicles based on the assumed behavior of manually driven vehicles (MDVs) in a mixed vehicle scenario. The assumed behavior of the MDVs may be different from the actual behavior, which gives rise to a model mismatch. The use of erroneous localization information can generate erroneous controls. The presence of a model mismatch and the use of erroneous controls could potentially result into collisions. A controller robust to issues such as localization errors and model mismatches is thus required. This paper proposes a robust model predictive controller, which accounts for localization errors and mitigates model mismatches. Future control values computed by the centralized controller are shared with CACC vehicles and are stored in a buffer. Due to large localization errors or model mismatches when control computations are infeasible, control values from the buffer are used. Simulation results show that the proposed robust controller with buffer can avoid almost the same number of collisions in a scenario impacted by localization errors as that in a scenario with no localization errors despite model mismatch.

SLAP: Simultaneous Localization and Planning Under Uncertainty via Dynamic Replanning in Belief Space

Simultaneous localization and planning (SLAP) is a crucial ability for an autonomous robot operating under uncertainty. In its most general form, SLAP induces a continuous partially observable Markov decision process (POMDP), which needs to be repeatedly solved online. This paper addresses this problem and proposes a dynamic replanning scheme in belief space. The underlying POMDP, which is continuous in state, action, and observation space, is approximated offline via sampling-based methods, but operates in a replanning loop online to admit local improvements to the coarse offline policy. This construct enables the proposed method to combat changing environments and large localization errors, even when the change alters the homotopy class of the optimal trajectory. It further outperforms the state-of-the-art Feedback-based Information RoadMap (FIRM) method by eliminating unnecessary stabilization steps. Applying belief space planning to physical systems brings with it a plethora of challenges. A key focus of this paper is to implement the proposed planner on a physical robot and show the SLAP solution performance under uncertainty, in changing environments and in the presence of large disturbances, such as a kidnapped robot situation.

Incremental Rule Splitting in Generalized Evolving Fuzzy Systems for Autonomous Drift Compensation

Gradual drifts in data streams are usually hard to detect and often do not necessarily trigger the evolution of new fuzzy rules during model adaptation steps in order to represent the new, drifted data distribution(s) appropriately in the fuzzy model. Over time, they thus lead to oversized rules with untypically large local errors (typically also worsening the global model error), as representing joint local data distributions before and after a drift happened likewise. We therefore propose an incremental rule splitting concept for generalized fuzzy rules in order to autonomously compensate these negative effects of gradual drifts. Our splitting condition is based on the local error of rules measured in terms of a weighted contribution to the whole model error and on the size of the rules measured in terms of the volume of the associated clusters. We use the concept of statistical process control in order to omit an extra threshold parameter in our splitting condition. The splitting technique relies on the eigendecomposition of the rule covariance matrix to adequately manipulate the largest eigenvector and eigenvalues in order to retrieve the new centers and contours of the two split rules. Furthermore, we guarantee sufficient flexibility in adapting the shapes and consequents of the split rules to the new drifted situation in the stream by integrating a specific dynamic and smooth forgetting concept of older samples, which formed the original (nonsplit) rules. Robustness against outliers is guaranteed by the realization of a two-layer model building process , where one layer represents the cluster partition and the other layer the rule partition: Only clusters becoming significant over time are accepted as rules in the fuzzy model. The splitting concepts are integrated in the generalized smart evolving learning engine for fuzzy systems (termed as Gen-Smart-EFS ) and successfully tested on two real-world application scenarios, engine test benches and rolling mills, the latter including a real-occurring gradual drift (whose position in the data is known). Results show clearly improved error trend lines over time when splitting is applied, compared to the case when it is not applied: reduction of the mean absolute model error by about one third (rolling mills) and about one half (engine test benches).

Joint Localization and Data Gathering Over a Small-World WSN With Optimal Data MULE Allocation

Localization of sensor nodes and efficient data gathering over a wireless sensor network (WSN) is vital in applications such as cyber-physical systems, Internet of Things, and context-aware pervasive systems. In WSNs, sensor nodes transfer the data cooperatively using multiple hops over a network. The large number of hops required for data transmission leads to erroneous distance estimation between node pairs, resulting in a large localization error. In this paper, we utilize a recent development in social networks called small-world characteristics for proposing a novel method of joint localization and data gathering over a WSN. A small-world WSN is developed by introducing data mobile ubiquitous LAN extensions (MULEs) into a conventional WSN. A small-world WSN exhibits low average path length and high average clustering coefficient. Such a small-world WSN when designed with novel routing strategies leads to reduced hop counts in sensor data transmission. Additionally, a method for optimal data MULE allocation is also developed. This method minimizes an objective function, which is a normalized weighted sum of network parameters such as bandwidth requirement and localization error. The optimal data MULE allocation method computes both the optimal number of data MULEs and their placement in the network. On the other hand, the joint localization and data gathering method that utilizes a multidimensional-scaling-based cooperative localization method is also developed for this purpose. Experiments are conducted using simulations and real node deployments over a WSN testbed. The performance of the proposed method is evaluated by conducting exhaustive analysis of power consumption, bandwidth required, localization error, data gathering efficiency, and throughput. The obtained experimental results indicate a significant improvement on several evaluation parameters when compared to results obtained on the conventional WSN. The obtained results also illustrate reasonable improvement when compared to results obtained using conventional small-world characteristic introduction and localization methods. The results are motivating enough for the proposed method to be used in large- and medium-scale network applications.

Improving underwater localization accuracy with machine learning.

Machine learning classification and regression algorithms were applied to calibrate the localization errors of a time-difference-of-arrival (TDOA)-based acoustic sensor array used for tracking salmon passage through a hydroelectric dam on the Snake River, Washington, USA. The locations of stationary and mobile acoustic tags were first tracked using the approximate maximum likelihood algorithm. Next, ensembles of classification trees successfully identified and filtered data points with large localization errors. This prefiltering step allowed the creation of a machine-learned regression model function, which decreased the median distance error by 50% for the stationary tracks and by 34% for the mobile tracks. It also extended the previous range of sub-meter localization accuracy from 100 m to 250 m horizontal distance from the dam face (the receivers). Median distance errors in the depth direction were especially decreased, falling from 0.49 m to 0.04 m in the stationary tracks and from 0.38 m to 0.07 m in the mobile tracks. These methods would have application to the calibration of error in any TDOA-based sensor network with a steady environment and array configuration.

Study of range free centroid based localization algorithm and its improvement using particle swarm optimization for wireless sensor networks under log normal shadowing

Localization deals with the determination of coordinates of unknown nodes for proper routing of data in wireless sensor networks. The centroid based localization algorithm (CLA) has been explored to a great extent till date. Its basic and improved form suffers from large localization error. In the present work the basic range free centroid based localization algorithm is studied under log normal shadowing and evaluated in terms of localization error. Further the basic CLA is improved by using the particle swarm optimization (PSO) under the same environment. The localization error for basic and PSO based CLA is calculated by varying the anchor ratio, communication range, number of unknown nodes, and network area. In every condition the PSO based CLA performs well over basic CLA in terms of reduction in localization error. The localization error in the proposed work is smallest as compared to other known centroid based localization algorithms.

Measurement Data Classification Optimization Based on a Novel Evolutionary Kernel Clustering Algorithm for Multi-Target Tracking

In the field of range-based multi-target localization and tracking, measurement data of each time cannot associate with its corresponding target, and clustering analysis can be used to solve this problem. In this paper, a novel evolutionary kernel clustering algorithm was developed for range-based multi-target tracking in wireless sensor networks. First, the locations of multi-targets are predicted according to the previous trajectories. Second, we apply the clustering number recognition algorithm to filter out the outliers and calculate the initial cluster center by analyzing the density of each measurement data. For each cluster, its relationship with corresponding target is established according to the predictive position and the initial cluster center. Third, the density factors of each measurement data are fused into the Gaussian kernel function to improve the accuracy of the cluster center. Finally, the accurate position of each target at current moment is calculated based on the predictive position and the measurement data set of corresponding cluster. Two different experiments are done in this paper: in the first experiment, the clustering performance of our proposed algorithm is evaluated based on the training data set. In the second one, the accuracy improvement of range-based target tracking is shown when proposed algorithm is used to analyze the measurement data. The tracking results show that the proposed algorithm is more robust to large localization errors.

Awareness of Line-of-Sight Propagation for Indoor Localization Using Hopkins Statistic

The non-line-of-sight (NLOS) propagation degrades the performances of mobile computing applications such as indoor localization when the line-of-sight (LOS) propagation between Access Point and client is blocked due to the mobility of users and building structure. Thus, the ability of sensing environment, i.e., awareness of LOS/NLOS propagation is significantly important. Many previous systems relied on channel state information statistic to construct LOS identification schemes by using the commodity Wi-Fi network. However, the vulnerability of these systems is that the robustness is not considered carefully since the system configuration such as operating channel affects statistic results. Up to now, the estimation of spatial parameters of multipath components (MCPs) is not challenging due to the advanced Multiple-input-multiple-output technique. In this paper, we propose a robust LOS/NLOS propagation identification system by using Wi-Fi signal. We first resolve the angle-of-arrival and time-of-flight of MCPs, and then creatively introduce Hopkins statistic to measure the spatial randomness of LOS and NLOS propagation, respectively. We finally employ the non-parametric method to calculate the optimal threshold of Hopkins statistic to differ the two cases. The experimental results show that the proposed system achieves LOS and NLOS identification rates over 91% and 87%, respectively under different system configuration. Furthermore, the upper-layer indoor localization applications can be benefited by cooperating with the proposed algorithm, and the case study of an indoor localization removes large localization errors effectively.

Empirical Determination of Efficient Sensing Frequencies for Magnetometer-Based Continuous Human Contact Monitoring.

The high linear correlation between the smartphone magnetometer readings in close proximity can be exploited for physical human contact detection, which could be useful for such applications as infectious disease contact tracing or social behavior monitoring. Alternative approaches using other capabilities in smartphones have aspects that do not fit well with the human contact detection. Using Wi-Fi or cellular fingerprints have larger localization errors than close human contact distances. Bluetooth beacons could reveal the identity of the transmitter, threatening the privacy of the user. Also, using sensors such as GPS does not work for indoor contacts. However, the magnetometer correlation check works best in human contact distances that matter in infectious disease transmissions or social interactions. The omni-present geomagnetism makes it work both indoors and outdoors, and the measured magnetometer values do not easily reveal the identity and the location of the smartphone. One issue with the magnetometer-based contact detection, however, is the energy consumption. Since the contacts can take place anytime, the magnetometer sensing and recording should be running continuously. Therefore, how we address the energy requirement for the extended and continuous operation can decide the viability of the whole idea. However, then, we note that almost all existing magnetometer-based applications such as indoor location and navigation have used high sensing frequencies, ranging from 10 Hz to 200 Hz. At these frequencies, we measure that the time to complete battery drain in a typical smartphone is shortened by three to twelve hours. The heavy toll raises the question as to whether the magnetometer-based contact detection can avoid such high sensing rates while not losing the contact detection accuracy. In order to answer the question, we conduct a measurement-based study using independently produced magnetometer traces from three different countries. Specifically, we gradually remove high frequency components in the traces, while observing the correlation changes. As a result, we find that the human coexistence detection indeed tends to be no less, if not more, effective at the sampling frequency of 1 Hz or even less. This is because unlike the other applications that require centimeter-level precision, the human contacts detected anywhere within a couple of meters are valid for our purpose. With the typical smartphone battery capacity and at the 1 Hz sensing, the battery consumption is well below an hour, which is smaller by more than two hours compared with 10 Hz sampling and by almost eleven hours compared with 200 Hz sampling. With other tasks running simultaneously on smartphones, the energy saving aspect will only become more critical. Therefore, we conclude that sensing the ambient magnetic field at 1 Hz is sufficient for the human contact monitoring purpose. We expect that this finding will have a significant practicability implication in the smartphone magnetometer-based contact monitoring applications in general.

Nearfield target localization with a few snapshots for sonar array

Sonars can be used for detection of targets located on the sea floor where the signal undergoes multipath propagations which may arise large localization errors. Besides, underwater environment is often highly unstable so that a large number of snapshots which means long time sampling are not reliable. In sonar systems, the wideband chirp signals are usually used. This paper presents a nearfield target localization method for underwater environments. First, the time of arrival (TOA) and the direction of arrival (DOA) for nearfield wideband chirp signals are estimated with only a few snapshots by using the fractional Fourier transform (FRFT) and the SPICE algorithm. Then, the virtual station method which is proposed in our previous work is used so that the multipath problems can be converted into line-of-sight (LOS) problem and its Cramer-Rao lower bound (CRLB) is also analyzed. Finally, simulation results demonstrate the effectiveness of the proposed method.

A new patch and stitch algorithm for localization in wireless sensor networks

Patch and stitch is a technique used for localization in wireless sensor networks. It combines the accuracy of centralized schemes with the computational efficiency of distributed approaches. The network is partitioned into small overlapping patches, localized and finally merged to form a single global map. However, due to noisy distance measurements, ordinary merging may lead to wrong reflection of patches causing large localization error. In this paper, we present a new patch and stitch algorithm which improves the map construction as well as map stitching phase to increase the accuracy of the algorithm. In map construction phase, each node, along with two-hop neighbors builds a local map using Isomap. In stitching phase, maps are stitched to each other in an incremental style. To restrict error propagation (1). a core map with a highest node degree is selected, (2). a robustness criterion that employs flip detect and discard policy is used to ensure a flip resistant stitching and (3). once the absolute core map is formed estimated positions of anchors are replaced by their true positions. Simulation results show that the proposed algorithm enhances the reliability of the location estimates without compromising with the number of nodes localized considerably and thus outperforms the state-of-the-art methods.

Proposing a measurement criterion to evaluate the border problem in localization algorithms in WSNs

Localization algorithms are one of the most important protocols that may be used in many different fields of wireless sensor networks. The border problem is a considerable challenge for localization algorithms. In this regard, the sensor nodes that are placed on the boundary of a deployment area have a larger localization error as compared to the other sensor nodes. In this article, a new criterion is proposed for measuring the impact of the border problem on the performance of localization algorithms in isotropic networks. The performance of some range-free localization algorithms is studied by simulation. The results show that, contrary to LSVM and NN algorithms, the impacts of the border problem are reduced by a reduction in the dimensions of the deployment environment in the DV-hop algorithm. Besides, the effect of the border problem on the performance of the localization algorithms can be reduced by an increase in the number of anchor nodes. On the other hand, the number of sensor nodes does not have a significant impact on reducing the effect of the border problem in localization algorithms. Finally, a solution is proposed to reduce the negative impact of this issue on the performance of localization methods.

Data Field-Based K-Means Clustering for Spatio-Temporal Seismicity Analysis and Hazard Assessment

Microseismic sensing taking advantage of sensors can remotely monitor seismic activities and evaluate seismic hazard. Compared with experts’ seismic event clusters, clustering algorithms are more objective, and they can handle many seismic events. Many methods have been proposed for seismic event clustering and the K-means clustering technique has become the most famous one. However, K-means can be affected by noise events (large location error events) and initial cluster centers. In this paper, a data field-based K-means clustering methodology is proposed for seismicity analysis. The application of synthetic data and real seismic data have shown its effectiveness in removing noise events as well as finding good initial cluster centers. Furthermore, we introduced the time parameter into the K-means clustering process and applied it to seismic events obtained from the Chinese Yongshaba mine. The results show that the time-event location distance and data field-based K-means clustering can divide seismic events by both space and time, which provides a new insight for seismicity analysis compared with event location distance and data field-based K-means clustering. The Krzanowski-Lai (KL) index obtains a maximum value when the number of clusters is five: the energy index (EI) shows that clusters C1, C3 and C5 have very critical periods. In conclusion, the time-event location distance, and the data field-based K-means clustering can provide an effective methodology for seismicity analysis and hazard assessment. In addition, further study can be done by considering time-event location-magnitude distances.

A characterization of the instantaneous wave front distortion characteristics in SW06 and ASIAEX 2001 data and strategies for exploiting lucky scintillations

Long line arrays are highly susceptible to signal wave front distortions caused by random medium effects like internal waves. Effects include large localization errors, biases, and loss of array gain. In earlier work, we had showed evidence from real data [Kirsteins and Ge, IEEE UASP workshop, Oct. 2017] that lucky scintillations, i.e., moments when the instantaneous signal wave front is relatively undistorted, occur regularly at much shorter time scales even during periods of strong internal wave activity that could potentially be exploited to improve array processing in environments with apparently poor spatial coherence. To better understand the phenomenon of lucky scintillations and how they can be used in array processing, we characterize in this paper the instantaneous or short time signal wave front distortion behaviors of horizontal line array (HLA) data from the Shallow Water 2006 (SW06) and ASIAEX 2001 experiments provided by the Woods Hole Oceanographic Institute, examining the time scales and the rate at which these lucky scintillations occur. Our analysis suggests an alternative array processing strategy by collecting data snap shots over a time interval matched to the ocean random medium time scales and using only the best snap shots in the estimator.