Passive target tracking with uncertain sensor positions

A new approach to the optimal formation problem is proposed in the sense of the passive target tracking using time difference of arrival (TDOA) data obtained by cooperative UAVs. In most existing methods, under the assumption that arbitrary unbiased and efficient estimator could be designed for target tracking, the objective function for the information maximizing formation is defined without particular consideration of the filter structure used for passive target tracking. However, this approach might be restrictive because the overall performance of passive target tracking depend on the UAV formation and the filter structure as well. To overcome this limitation, the proposed technique takes account of the practical tracking filter structure and its properties in the problem. To do this, it is firstly shown that the amount of moving target information is expressed in terms of the variance of UAVs' angular positions. In order to guarantee the satisfactory target tracking performance even when the UAVs-target relative geometry is time-varying, the optimal formation problem is formulated as finding the maximizing solution of the objective function in worst-case. Through the simulations, the effectiveness of the proposed scheme is demonstrated.

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.

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.

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 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).

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.

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.

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.

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.

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.

Phenomenological modeling coupled with statistical signal processing has been shown to significantly improve capabilities for discriminating unexploded ordnance (UXO) from benign clutter using electromagnetic induction (EMI) sensor data. The general premise underlying the majority of these coupled approaches is that a phenomenological model is fit to the measured data, and the parameters estimated from this model inversion, which characterize the interrogated target, are utilized in subsequent statistical signal processing algorithms to classify the target as either UXO or clutter. A potential limitation of this coupled approach is that the inversion has been shown to be sensitive to uncertainty associated with the sensor positions. When the measurement positions are uncertain, the inversion results are more variable, and consequently, discrimination performance degrades. In this letter, a Bayesian methodology is applied to estimate the desired features from the measured data. This method explicitly acknowledges that uncertainty in the sensor positions exists and incorporates this knowledge to find the maximum-likelihood feature estimates by integrating over the uncertain measurement positions. Due to the high dimensionality of the integration, Monte Carlo integration, a statistical technique to estimate the value of an integral, is employed. Simulation results show that this Bayesian approach in mitigating sensor position uncertainty produces features with lower variability and, therefore, provides improved discrimination performance.

In this work, we develop shifted Rayleigh filter (SRF) for continuous-discrete (CD) systems. The proposed filter is applied to a real-life underwater passive bearings-only target tracking problem, where the discretization error is accounted for. The stochastic differential equation describing the process dynamics is approximated using Ito-Taylor expansion of order 1.5. Inorder to make a fair comparative study, the performance of the proposed algorithm, which we refer to as CD-SRF, is examined with other popular filters like the unscented Kalman filter (UKF), cubature Kalman filter (CKF) and Gauss-Hermite filter (GHF) in the CD domain. The filtering performance has been studied in terms of root mean square error (RMSE), track-loss and computational time. Theoretical Cramer-Rao lower bound (CRLB) is also implemented to determine the maximum achievable accuracy.

Geographic routing protocols have been developed to increase the effectiveness and the usefulness of wireless sensor networks in many emerging applications. In these protocols, the routing decision, at each sensor node, is based only on the positions of the destination node and forwarding node's neighbors. However, these protocols are based on exact sensors position, assuming that sensors are equipped with a geographic positioning or a localization-based technique. Recently, several techniques, such DV-Hop, have been proposed for estimating sensors' positions. This paper introduces an integrated geographic routing protocol by integrating both sensors' position estimation and geographic routing techniques. We have mainly combined DV-Hop for estimating sensors position and the greedy perimeter stateless protocol for data forwarding and routing. Simulations have been carried out and results are reported to show the efficiency of the proposed hybrid geographic routing protocol in WSNs.

Passive sensor target tracking has the challenge to 3-D tracking applications in that the raw angle-only measurements cannot be converted into Cartesian measurements. Multi-sensor methods can be used to convert the raw measurements into Cartesian measurements by finding the intersection of the two lines of sight. Examples include triangulation, law of sines, and maximum likelihood estimation. However, many of these methods can have an inherent bias present in the conversion as a result of its nonlinearity. In long range applications this can cause significant error. This paper investigates the conversion of passive sensor angle-only measurements into Cartesian coordinates and evaluates the conversion bias present in triangulation.