Optimal formation of cooperative multiple UAVs for enhancing passive target tracking performance

A major advantage of the use of passive sonar in the tracking multiple underwater targets is that they can be kept covert, which reduces the risk of being attacked. However, the nonlinearity of the passive Doppler and bearing measurements, the range unobservability problem, and the complexity of data association between measurements and targets make the problem of underwater passive multiple target tracking challenging. To deal with these problems, the cardinalized probability hypothesis density (CPHD) recursion, which is based on Bayesian information theory, is developed to handle the data association uncertainty, and to acquire existing targets’ numbers and states (e.g., position and velocity). The key idea of the CPHD recursion is to simultaneously estimate the targets’ intensity and the probability distribution of the number of targets. The CPHD recursion is the first moment approximation of the Bayesian multiple targets filter, which avoids the data association procedure between the targets and measurements including clutter. The Bayesian-filter-based extended Kalman filter (EKF) is applied to deal with the nonlinear bearing and Doppler measurements. The experimental results show that the EKF-based CPHD recursion works well in the underwater passive multiple target tracking system in cluttered and noisy environments.

It is well known that passive target tracking by a single observer, commonly referred to as target motion analysis (TMA), can be done using angle and/or frequency measurements. Depending on the measurement set, different passive tracking procedures result: angle-only tracking (AOT), frequency-only tracking (FOT), and angle/frequency tracking (AFT). Whereas the two-dimensional passive tracking problem has been solved for a multitude of scenarios, thus giving a good insight into the parametric dependences, the three-dimensional problem has been discussed only in a few specific cases. To get a deeper insight into the parametric dependences of three-dimensional TMA, this work analyzes AOT and AFT in typical three-dimensional Airborne Warning and Control System (AWACS) scenarios. A Cramer Rao (CR) analysis of the problem reveals the parametric dependences of both methods and gives a clear idea of the increase in estimation accuracy by using AFT instead of AOT. The results obtained in this way are well confirmed by Monte Carlo simulations taking maximum likelihood (ML) as estimation procedure.

This paper addresses a new passive target tracking problem using the range differences measured by cooperative UAVs. In order to solve the range difference based passive target tracking problem within the framework of linear robust state estimation, the uncertain linear measurement model which contains the stochastic parameter uncertainty is derived by using the noisy range difference measurements. To cope with the performance degradation due to the stochastic parameter uncertainty, the recently developed non-conservative robust Kalman filtering technique [1] is applied. For the cruciform formation flying UAVs, the relationship between the target tracking performance and the measurement errors is quantitatively analyzed. The proposed filter has practical advantages over the classical nonlinear filters because, for its recursive linear structure, it can provide satisfactory convergence properties and is suitable for real-time multiple UAVs applications. Through the simulations, the usefulness of the proposed method is demonstrated.

Fields of distributed passive and multistatic active sonar sensors provide surveillance capabilities against quiet submarine threats in Anti-Submarine Warfare (ASW) scenarios. The joint cooperative use of passive and active sonar offers synergistic and complimentary acoustic performance as well as operational advantages. However, a significant challenge of heterogeneous, multi-sensor systems is the successful fusion of information from the various sensors to enable robust target detection, classification, localization, and tracking. This paper describes the formation of a passive acoustic track that may be used to cue an active multistatic acoustic tracking system. A cue from a passive acoustic track reduces the false track rate in the active tracker by focusing the search area and selectively initiating active tracks. The passive track is assumed to originate from a fixed passive acoustic horizontal line array (HLA) which is unable to determine target range or resolve the familiar left-right ambiguity. However, passive systems can predict Closest-Point-of-Approach (CPA) events. For each CPA event, a cue can be forwarded to the active system for multistatic active track initiation. This paper presents a minimal parametric representation for a passive acoustic track. It also describes an algorithm for obtaining initial estimates of parameters that can be used to initiate the passive track more effectively. A performance evaluation is given. This paper also describes the conversion of the passive track to pseudo-coordinates that are convenient for use with a multistatic tracking system. Lastly, a method for measuring passive track quality is described which determines if track quality is sufficient to justify cueing the active multistatic system.

This paper focuses on underwater target tracking based on a multi-static sonar network composed of passive sonobuoys and an active ping. In the multi-static sonar network, the location of the target can be estimated using TDOA (Time Difference of Arrival) measurements. However, since the sensor network may obtain insufficient and inaccurate TDOA measurements due to ambient noise and other harsh underwater conditions, target tracking performance can be significantly degraded. We propose a robust target tracking algorithm designed to operate in such a scenario. First, track management with track splitting is applied to reduce performance degradation caused by insufficient measurements. Second, a target location is estimated by a fusion of multiple TDOA measurements using a Gaussian Mixture Model (GMM). In addition, the target trajectory is refined by conducting a stack-based data association method based on multiple-frames measurements in order to more accurately estimate target trajectory. The effectiveness of the proposed method is verified through simulations.

Traditional passive tracking algorithms are usually subjected to problems such as the poor measurement accuracy of parameters of target motion, complex algorithm, instable tracking precision under complex circumstances. Aiming at this issue, in this paper we combined the dual function of transmitter identification and target tracking in passive reconnaissance, discussed the selection of transmitter eigenvector in passive tracking based on transmitter attribute information, established the passive tracking algorithm framework combined with transmitter attribute information, and then investigated passive tracking arithmetic model based on statistical regulation and ratiocinative regulation respectively, in the hope of providing new idea for passive target tracking.

When tracking maneuvering targets, the sudden changes of target states and nonlinear problems in passive tracking cause serious decline and even divergence in the performance of some conventional algorithms. Taking this into account, a novel adaptive tracking algorithm for maneuvering targets is proposed in this paper. First, a new weighed factor could adaptively adjust the filter gain is introduced in Modified Input Estimation (MIE) algorithm to improve its tracking performance. Then, the least squares technique is combined for tracking passive multi-sensor maneuvering targets in high accuracy without any priori information of maneuver. Simulation results show the effectiveness of the proposed algorithm. Compared with the traditional MIE and fuzzy MIE algorithm, this algorithm has a better tracking performance.

Passive target tracking deals with nonlinear filtering in which dynamics of the system are considered to be linear, while the target state is built on nonlinear measurements. In this paper, a comparative study is conducted for accurate state estimation of an underwater far-field moving target by exploiting the strength of well known nonlinear variant of Bayesian filter, i.e., extended Kalman filter (EKF) with discrete-time Kalman smoother, called Rauch–Tung–Striebel (RTS) smoother. Analysis is performed with two key parameters in target tracking by mean of variation in the number of sensors and different standard deviations of measured noise in the context of underwater bearings-only tracking technology. Exhaustive experiments are performed for finding the least root-mean square error between true and estimated position of target movement in the trajectory at every time instant. Relatively accurate estimation of the target state is observed from noisy measurements of sensors in case of RTS smoother than that of EKF for each scenario.

Marine resources may now be explored because of advancements in acoustic technology and instruments that were previously unavailable. Underwater acoustic target tracking has gotten a lot of attention nowadays, both in the military and in the civilian world, because it is such an important component of ocean exploration. Due to the complex nature of the marine environment, several tracking approaches have been proposed to improve tracking performance in the maritime environment. The Doppler Bearing tracking (DoBT) of an undersea target can be used to safeguard maritime territory while also allowing for the efficient use of available marine resources. When taking into account the impacts of an unknown underwater environment, the goal of this study was to estimate the 2-D positions and velocities of an unknown underwater target (i.e., Target Motion Parameters (TMP)) with undetermined underwater disturbances. In this research paper, the UKF algorithm is compared against CRLB to demonstrate its accuracy.

In this paper, we describe the implementation of applying and testing the "Lightweight Target Tracking using Passive Traces algorithm" on a FIRE wireless sensors testbed located in the Theoretical Computer Science/Sensors Lab in Geneva, Switzerland. We provide information about the hardware installation and configuration, the changes we did to the algorithm to adapt it to a real testbed as well as the tools we implemented to operate the network and receive feedback from the algorithm's operation. Finally, we discuss the performance evaluation findings of our implementation.

Neurovascular coupling (NVC) describes the effective matching of cerebral blood flow (CBF) to regions of neuro‐metabolic demand. There is increasing interest to assess human NVC for both basic research and its potential role in vascular‐cognitive impairment. The clinical utility of NVC relies on a standardized protocol for which the driving metabolic demands are highly‐selective. Various research groups deploy divergent strategies to elicit visual NVC responses, including inactive processes (visual grating), passive visual tracking (target with predictable motion) and active visual tracking (target with unpredictable motion). These strategies differ in degree of cognitive and metabolic demand and may elicit different NVC responses, thus precluding study comparison.The present NVC assessment evaluated temporal and regional responsiveness of blood flow (transcranial Doppler) to the visual cortex [via the posterior cerebral artery (PCA)] and blood pressure (Finapres NOVA) during visual stimulation in 19 healthy subjects while also measuring middle cerebral artery (MCA) blood flow. Visual stimulation included 10 cycles of 30‐seconds with eyes‐closed, followed by 30‐seconds with eyes‐open tracking a moving computerized target. Each subject completed three trials of passive tracking and three trials of active tracking (114 NVC protocols, 1140 individual hyperemias). A custom eye‐scanning apparatus followed eye motion to quantify visual target‐tracking vigilance. Additional custom software was used to quantify NVC.The data demonstrated that active tracking elicited greater NVC responses compared to passive tracking. Specifically, there was 26% greater change in the mean elevation of PCA blood velocity (p=<0.000) and 13% greater peak NVC response (p<0.01). The MCA response was also greater during active tracking (mean response 111% greater, peak response 41% greater; both p<0.001). Visual target‐tracking vigilance was linearly correlated to the degree of hyperemia in the MCA and PCA, as well blood pressure during NVC.It was observed that active and passive visual tracking elicit different NVC responses and cannot be reliably compared. That PCA and MCA responses were greater with active tracking suggests an elevated global CBF (i.e. not selective to regions perfused by the PCA) that may result from recruitment of brain centres responsible for sustained attention and executive function. In other words, active tracking leads to non‐selective elevations in global CBF and greater target‐tracking vigilance impacts the NVC response. These findings are a critical step to better understand and standardize the evaluation of NVC in humans and for potential clinical deployment of NVC assessments.Support or Funding InformationNatural Sciences and Engineering Research Council; Alberta Innovates Research Studentship; NSERC; CIHR; Libin Cardiovascular Institute; Hotchkiss Brain Institute; Compute CanadaThis abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

In underwater, the bearings-only passive target tracking can be done with many available estimators by supplying the noisy bearing measurements from a passive sonar as inputs. The problem here is, all the estimators fail to work in the abnormal conditions which includes the situation of missed bearings, the situation of spurious bearings and the situation in which the noise is non-Gaussian. The algorithm developed in this paper will take care of the above mentioned problems. The main step involved in the algorithm includes preprocessing of the noisy bearing measurements and then supply the preprocessed data along with the variance as input to the advanced nonlinear filter like Modified Gain Bearings-Only Extended Kalman Filter (MGBEKF) to find the target motion parameters. Preprocessing stage involves reducing the amplitude of the noise in the measurements, converting the nongaussian noise to Gaussian and generating the substitute for the missed and spurious bearings using Pseudo Linear Estimator (PLE).

As a key technology of detection and tracking system, moving target detection and tracking has attracted a great deal of attention. To achieve the goal of tracking target, conventional methods have to complete the detection of targets firstly, and then track the target. However, such kind of approaches require using the method of constant false-alarm rate to detect the whole frame image received at that time, which not only reduces the detection efficiency but also degrades the performance of target tracking. In order to implement real-time monitoring divers and other small moving targets under the conditions of strong underwater noise and clutter interference, an interactive algorithm for moving target detection and tracking is proposed based on acoustic image, which jointly analyzes the target detection system and the target tracking system. In the target tracking unit, the interactive algorithm takes advantage of data interconnection to remove the false targets produced by target detection; extrapolates the target's track, and predicts the location that the target may appear at the next moment by employing the Kalman filter. These messages will be used as priori information in the detection unit. In the detection unit, first of all, a gate is established whose center is the position that the target tracking unit predicted. For the image received at the next moment, detection in the predicted gate in implemented by using the method of constant false-alarm rate, followed by feeding back the detection results to the tracking unit. Compared with existed methods, the proposed algorithm can effectively improve the target detection efficiency and tracking performance, providing a better approach to monitor underwater small targets.

This study investigates the application of a batch oriented MAP estimation scheme to the problem of tracking a moving acoustic target from sensors of uncertain position. We propose a simple method for determining sensor movement from the data, and subsequently reducing tracking errors. Simulations are presented, and the results show that the enhancements to the proposed algorithm permit estimation of the sensor positions along with that of the target.

Using bearings only information, passive target tracking in under water scenario can be carried out for a target moving at a constant velocity. If the target changes its course or speed or both, the estimated parameters differ with those of actuals. Hence it is usual to find out target maneuver, before the solution diverges completely and appropriately the covariance matrix of target state vector is corrected. In general, the target maneuver is found out using chi-square test with normalized squared innovation process. J.L. Pirot and M.S. Smith used F-statistic to find out whether the target is stationary or not. In this paper, their work is extended to find out whether the target is maneuvering or not, as an alternate approach for chi-square test. The efficiency of the algorithm is demonstrated through Monte-Carlo simulation. The residuals generated from maximum likelihood estimator are used in sliding window format. The residuals are assumed to follow a second order polynomial. The mean squared errors of the innovations before and after maneuver is analyzed through F-statistic for deciding whether the target is maneuvering or not. Extensive simulation is carried out to evaluate the performance of the algorithm. For the purpose of evaluation, the results of two scenarios in Monte-Carlo simulation with and without target maneuver are presented.