Three-dimensional target motion analysis using angle and frequency measurements

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.

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.

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.

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.

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.

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.

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.

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).

In this study, the target motion analysis methods, especially used for passive target tracking in submarines, are implemented using evolutionary algorithms. In target motion analysis problem, using a time sequence of transmitted noise directions of a sound source in an acoustic environment; the range, course and speed of the corresponding source are estimated. Size of the search space; ambient noise on the transmission and noise on sensor measurements; decreased observability in different problem scenarios and the need of fast and exact solution, evolutionary strategies, a strong variant of the evolutionary search algorithms, is applied to the problem and efficiency of the results are presented for selected scenarios.

A Newton-type method is used to solve the target motion analysis (TMA) problem with respect to bearing and frequency measurements from a passive sonar system. In many long-range sonar situations the TMA problem is ill conditioned and suffers from a small signal-to-noise ratio. Although Kalman filters have been investigated extensively it is known that maximum likelihood (ML) estimation is superior in these cases. The main reason for the good performance of the ML method is that the underlying numerical optimization problem deals with the ill conditioning of the problem. This work illustrates how the conditioning depends on the geometry of the tracks and the signal-to-noise ratio. Monte Carlo simulations with respect to the measurement noise show the influence on the ML estimation performance for three specific cases concerning multileg situations and bottom bounce measurements.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

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.

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.

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.

In passive target tracking system, the observer trajectories have an important effect on the accuracy of target localization and tracking. This paper addressed an engineering approach to optimize the trajectory altitude of observer platform for airborne ESM surveillance system. The method proposed is based on Monte-Carlo run, which avoid complicated theoretical deduction and make the observer path easy-to-practice. By the analysis, the general principle of observer trajectory altitude optimization can be obtained. Within the range of allowed flying altitudes for observer, the measurements of elevation should be maintain or close to 45 degrees as much as possible.

Bearing and frequency measurements of TMA (Target Motion Analysis) in passive line array SONAR have lower bearing rate and frequency doppler, and are not detected or tracked continuously because of various ocean environments. This is a main reason to effect the TMA performance and it takes a long time to get TMA solutions. We propose the bearing and frequency TMA(BFTMA) using accumulative batch estimation to solve the TMA problem of line array passive SONAR. The accumulative batch estimation structure is based on MLE (Maximum Likelihood Estimation) but used accumulative measurements. The accumulative batch estimation is applied for the BFTMA with nonlinear Kalman filter to estimate the target range, speed and course. Simulation and sea data analysis were carried out to verify the performance and applicability of the proposed techniques.