Target Motion Analysis Problem Research Articles

A Novel Batch Bayesian WIV Estimator for Three-Dimensional TMA Using Bearing and Elevation Measurements

In this paper, we derive a novel batch Bayesian weighted instrumental variable estimator for the three-dimensional (3D) target motion analysis (TMA) problem using bearing and elevation measurements. Unlike most existing 3D estimators based on instrumental variables, the proposed approach is able to incorporate a priori information into the estimation process and is proven to be approximately asymptotically unbiased. An approximate asymptotic covariance matrix is also presented to evaluate the performance of the estimator. Simulation results show that the proposed estimator outperforms its non-Bayesian counterpart and has an estimation performance on par with the conventional iterative maximum a posteriori estimator, while being orders of magnitude faster in a passive sonar TMA problem. Moreover, it was shown that the proposed approach can provide an accurate initialization for recursive estimators.

Bearings‐only target motion analysis of a highly manoeuvring target

In this study, the authors tackle the bearings-only target motion analysis problem, which is to estimate the position and velocity of a target using bearing measurements. They consider a target which is highly manoeuvrable. This study introduces the modified range-parameterised extended Kalman filter (MRPEKF), which consists of a number of independent EKFs, each with a different initial range estimate. The MRPEKF uses the filter re-initialisation strategy to track a highly manoeuvring target. They further introduce the method to improve the efficiency of the MRPEKF. They verify the effectiveness of their MRPEKF by comparing it with the IMM KF using simulations.

Moving scanning emitter tracking by a single observer using time of interception: Observability analysis and algorithm

The target motion analysis (TMA) for a moving scanning emitter with known fixed scan rate by a single observer using the time of interception (TOI) measurements only is investigated in this paper. By transforming the TOI of multiple scan cycles into the direction difference of arrival (DDOA) model, the observability analysis for the TMA problem is performed. Some necessary conditions for uniquely identifying the scanning emitter trajectory are obtained. This paper also proposes a weighted instrumental variable (WIV) estimator for the scanning emitter TMA, which does not require any initial solution guess and is closed-form and computationally attractive. More importantly, simulations show that the proposed algorithm can provide estimation mean square error close to the Cramer-Rao lower bound (CRLB) at moderate noise levels with significantly lower estimation bias than the conventional pseudo-linear least square (PLS) estimator.

누적 일괄추정 기법을 이용한 수동 선배열 소나 방위 주파수 -표적기동분석 연구

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.

Optimal design of the Own Ship maneuver in the bearing-only target motion analysis problem using a heuristically supervised Extended Kalman Filter

The state estimation algorithm and the Own Ship maneuvering specifications have great effects on the accuracy of the bearing-only tracking (BOT) method. In the BOT problem, the EKF algorithm is widely used as the nonlinear state estimation algorithm while it suffers from its sensitivity to the initial values of covariance matrixes. This paper aims at improving the accuracy of the BOT problem by using the metaheuristic evolutionary optimization algorithms as supervising algorithms. Three different optimization algorithms, Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Cuckoo Search (CS) are used in finding the optimal initial values of the dynamic and the measurement process noise covariance matrixes of the Extended Kalman Filter (EKF). Moreover, the optimal path (leg) planning of the Own Ship maneuver is also done by minimizing the Fisher Information Matrix (FIM). The Monte Carlo analysis of the simulation results demonstrates the effectiveness of the evolutionary algorithms in improving the performance of the EKF in a BOT problem.

표적기동분석을 위한 Levenberg-Marquardt 적용에 관한 연구

The Levenberg-Marquardt method is a well known solution about the least square problem. However, in a Target Motion Analysis(TMA) application most of researches have used the Gauss-Newton method as a batch estimator, which of inverse matrix calculation may causes instability problem. In this paper, Levenberg-Marquardt method is applied to TMA problem to prevent its divergence. In experiment, its performance is compared with Gauss-Newton in domain of range, course and speed. Monte Carlo simulation reveals the convergence time and reliability of the TMA based on Levenberg-Marquardt.

Three-dimensional target motion analysis using azimuth/elevation angles

We present new closed-form and low-complexity solutions for the three-dimensional (3D) target motion analysis problem using azimuth/elevation angles. A 3D pseudolinear estimator (PLE) is developed based on an approximation of the maximum likelihood (ML) cost function. Drawing on a large-sample bias analysis, a bias-compensated PLE is constructed. The concept of instrumental variables (IV) is employed to derive an asymptotically unbiased estimator. Finally a weighted IV estimator is developed to achieve an estimation performance close to the ML estimator (MLE).

A Novel Power-Bearing Approach and Asymptotically Optimum Estimator for Target Motion Analysis

The problem of target motion analysis (TMA) has been extensively investigated using bearing-only (BO), Doppler-bearing (DB), and other measurement data. For radio frequency (RF) emitters, signal powers follow the well-known path loss law that can be utilized to aid localization and target tracking in BO-TMA, leading to the novel power-bearing (PB) approach as proposed in this paper. Our main contribution lies in the proof of the asymptotic optimality of the total least-squares (TLS) algorithm for PB-TMA under some mild condition. The Cramer-Rao lower bound (CRLB) is employed to demonstrate the performance improvements of the PB-TMA over the BO-TMA. Our work shows the promise of the PB-TMA approach for RF signals.

Bearings-Only Target Motion Analysis via Instrumental Variable Estimation

This paper deals with the instrumental variable (IV) estimation for the problem of target motion analysis from bearing-only measurements (BO-TMA). By taking asymptotical analysis of the IV estimation, a systematic method for developing consistent IV estimate and the sufficient condition for its asymptotical normality are proposed. The asymptotical covariance of IV estimate is also derived explicitly which can be used to evaluate its performance. These results generalize the previous studies and enhance the application of the IV estimation in target tracking. Numerical examples are shown to verify the theoretical results.

Square-root second-order extended Kalman filter and its application in target motion analysis

In this study, a new non-linear filter based on second-order extended Kalman technology and square-root Kalman algorithm is proposed and applied to the problem of target motion analysis (TMA). By orthogonalising the state vectors with the square root of covariance matrix, the authors reduce the computational complexity in the second-order extended Kalman algorithm, and then we deduce a second-order extended Kalman algorithm based on square-root Kalman technology, which is more numerically stable and can guarantee positive and semi-definiteness of the state covariance. The authors apply the new algorithm to a TMA problem, and prove with both numerical simulation and actual experimentation that their algorithm performs well in the significant non-linear systems.

An evolutionary computing approach for the target motion analysis (TMA) problem for underwater tracks

This study is concerned with the problem of determination of kinematic parameters of a moving sound source by using only bearings known as target motion analysis (TMA). A new matched field signal processing approach which uses genetic algorithm (GA) and Monte Carlo simulation is proposed to establish tracks from bearing-only contacts. The basic idea is to take a number of measurements and run a simulation of underwater tactical situation, then to let the simulation change its parameters until the output matches with the measurement data. When the simulation output (i.e. the replica data) matches the real measurement within a predefined degree, it is expected that the simulation resembles the real situation. In this sense, the TMA problem is considered as an optimization problem on a large parameter space and genetic algorithm was used to solve it. We developed an application called target motion analysis with genetic algorithm (TMAGA) in order to demonstrate the correctness of the algorithm. Monte Carlo simulations demonstrate the results of the experiments conducted with TMAGA.

On a novel ACO-Estimator and its application to the Target Motion Analysis problem

In the oceanic context, the aim of Target Motion Analysis (TMA) is to estimate the state, i.e. location, bearing and velocity, of a sound-emitting object. These estimates are based on a series of passive measures of both the angle and the distance between an observer and the source of sound, which is called the target. These measurements are corrupted by noise and false readings, which are perceived as outliers. Usually, sequences of measurements are taken and statistical methods, like the Least Squares method or the Annealing M-Estimator, are applied to estimate the target’s state by minimising the residual in range and bearing for a series of measurements. In this project, an ACO-Estimator, a novel hybrid optimisation algorithm based on Ant Colony Optimisation, has been developed and applied to the TMA problem and its effectiveness was compared with standard estimators. It was shown that the new algorithm outperforms conventional estimators by successfully removing outliers from the measurements.

Efficient matched processing for localisation of a moving acoustic source

In recent years matched processing techniques have emerged as a solution for the problem of target motion analysis. A target's motion parameters are estimated by determining which of a bank of replica signals best correlates with the signal received from that target. Matched processing methods have been used in low SNR environments such as the underwater acoustic environment because they outperform pseudomeasurement-based target motion analysis. However, matched processing is notoriously computationally demanding, requiring evaluation of complex equations for replica generation, and batch processing via an exhaustive search of a multidimensional parameter space. This study presents a matched processing method for localisation of a slow-moving low-frequency acoustic source by matching Fourier coefficients from a single frequency bin. A simple range-dependent propagation model is assumed, and the Doppler effect is carefully modelled to produce realistic replicas. An efficient matching algorithm is presented which computes cost values in a recursive fashion, discarding poorly scoring replicas before they are fully evaluated. Simulated examples demonstrate the ability of the technique to resolve the locus of low-Doppler targets at extremely low SNR with a fraction of the cost associated with exhaustive search batch processing.

Bearings-only target motion analysis with acoustic propagation models of uncertain fidelity

The augmented bearings-only target motion analysis (TMA) problem arises when the bearing measurements of the classical bearings-only TMA problem are augmented with received signal-to-noise ratio (SNR) measurements. A combined acoustic propagation and sensor (CAPS) performance prediction model specifying the conditional density of the SNR measurements is assumed given; however, mismatch may exist between the CAPS model and the real world. We present a novel missing data formulation of the augmented bearings-only TMA problem using an empirical maximum a posteriori (EMAP) method for target parameter estimation, and show that it provides a natural and straightforward technique for mitigating CAPS model mismatch. The EMAP approach leads to an iteratively reweighted, linear least-squares algorithm for solving both the augmented bearings-only TMA problem and the classical (nonaugmented) bearings-only TMA problem. Examples are provided.

Target motion analysis using range-only measurements: algorithms, performance and application to ISAR data

The paper considers the problem of target motion analysis from range and range-rate target measurements. The motivation for this work comes from the need to track the target motion with the Ingara multi-mode radar during an extended data collection in the ISAR mode. The paper makes three main contributions. First, the theoretical Cramér–Rao bound for the performance of an unbiased range-only tracking algorithm is derived. Second, three algorithms for target motion analysis (using range and range-rate measurements only) are developed and compared to the theoretical bounds of performance. The three algorithms are: the maximum likelihood estimator, the extended Kalman filter and the regularised particle filter. Finally, the paper presents the results of the application of the developed theory to the ISAR data collected in the recent trials with the Ingara radar.

Bearings-Only Target Motion Analysis Using a Dual-Waveband Infrared System

Infrared systems are widely used for target detection, designation and tracking. For example, an Infrared Search and Track (IRST) system, as a typical airborne or shipboard detecting device, is widely equipped for the remote target detection and tracking. In recent years, the problem of target motion analysis (TMA) and tracking has been studied increasingly extensively. In an airborne infrared system, the problem becomes more difficult due to absence of range information. In this paper, the infrared model and motion model of typical aerial targets are described. An airborne dual-waveband IRST system, which is quite familiar nowadays, is choosed for implementation of target motion analysis. Based on the above, a novel and more practical algorithm of target tracking via bearings-only measurements is formed and the major parameters are defined according to a typical airborne dual-waveband IRST system. Finally, data simulation is implemented, and the results demonstrate the new algorithm has a better performance than before for bearings-only target tracking.

A closed-form solution to bearings-only target motion analysis

Bearings-only target motion analysis is a nonlinear state estimation problem in which the noise corrupted angle of arrival measurements of an emitted signal are used to obtain estimates of the source's range, bearing, course, and speed. The estimation process is complicated by unusual observability properties that render the quality of the estimate highly dependent on both the measurement noise levels and the source-observer geometry. Solutions that use recursive Kalman filtering approach or batch-style algorithms have been reported. The nonlinear batch style estimators for this process require iterative solution methods and under certain scenarios can be sensitive to initial conditions. Pseudolinear solutions that alleviate some of the difficulties with the iterative batch algorithms have been proposed. Although early versions of the pseudolinear filter suffered from biased estimates, subsequent improvements appear to have reduced the bias problem. This paper discusses a new pseudolinear solution based on the observable parameters from individual data segments defined by periods of constant observer velocity (termed legs). This solution is a true closed-form solution to the bearings-only target motion analysis problem. Although theoretically interesting, the technique does suffer under conditions of poor observability. A practical pseudolinear estimate, that does not suffer from the same observability problems, is developed and related to the first solution. Algorithm performance results, obtained from computer simulation, are presented. For the scenarios examined, the technique provides good state estimates under conditions of high observability. As observability conditions deteriorate, the solution does develop biases. However, it may still be useful for initializing an iterative nonlinear batch-style estimation algorithm.

Properties and performance of extended target motion analysis

The problem of target motion analysis (TMA) has been the subject of an important literature. However, present methods use data estimated by a short time analysis (azimuths, Dopplers, etc.). For far sources, the nonstationarities of the array processing outputs, induced by the sources motion, may be simply modeled. This model leads one to consider directly a spatio-temporal TMA. Then new (spatio-temporal) data can be estimated. These estimates correspond to a long time analysis. Further, note that they are estimated independently of the (classical) bearings. In this general framework, the concept of source trajectory replaces the classical instantaneous bearings. Corresponding TMA algorithms are then studied. Then the study of statistical performance is carefully studied.

Maximum likelihood estimation for long-range target tracking using passive sonar measurements

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>