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

Target angular information in a 3-dimensional space consists of an elevation angle and an azimuth angle. The azimuth angle indicates the direction of the target in a horizontal plane while the elevation angle indicates the direction of target signal in a vertical plane established from a passive line array sonar. The acoustic signals gathered by the line array sonar have different elevation angles as they propagate through multiple paths. The target angle measured by the line array sonar is, in fact, a conical angle that indicates the direction of incoming signal to the line array sonar system. Therefore, bottom bounce path produces an inaccurate target location if it is interpreted as the azimuth angle in the horizontal plane as is commonly used in exiting Target Motion Analysis (TMA) technologies. Therefore, it is necessary to consider the conical angle effect on bearings-only TMA (BO-TMA). In this paper, the conical angle of target that causes angular ambiguity will be simulated, and the BO-TMA method using Particle Swarm Optimization (PSO) is proposed to solve the angular ambiguity problem.

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>

Classical array processing are based upon a short-time analysis followed by source tracking, target motion analysis (TMA) and data association. However, for numerous practical applications (e.g. passive sonar), a large amount of spatio-temporal data is available. It seems, then, possible to use globally all these data in order to separate, detect and track the sources by using their respective trajectories. In this spatio-temporal approach, the concept of source trajectory replaces the concept of instantaneous source bearing for the classical analysis. This article tries to build a bridge between two major areas of statistical signal processing which are classical array processing and the TMA. >

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.

Efficient matched processing for localisation of a moving acoustic source

The problem of track to track association in passive sonar with multiple platforms and/or time interruptions is considered. Typical case is a submarine equipped with a hull array (HA) and a towed array (TA). A few methods are applicable and discussed here, coming from detection and estimation theory. Performance of likelihood ratio test are studied for the binary case to associate one track from measurement platform 1 to one another from platform 2. Then it is generalized to two sets of tracks using either a motion representation of the target state (Target Motion Analysis, TMA) or directly the spectrum of frequency lines. Finally, this is also applied to a single antenna like TA where the two origins comes from two different time periods e.g. before/after an ownship maneuver Both applications are supported by a theoretic background and at-sea results.

A general linear and quasi-efficient estimator is presented which is an optimal (for a given criterion) approximation of the maximum likelihood estimator (MLE with nonlinear measurement equation) when the measurements are corrupted by a Gaussian noise. This approach consists of choosing a particular state vector which characterizes the signal. The model is defined by special values of the signal at sample times which are the roots of an orthogonal Lagrange polynomial. It is rigorously established that the linear estimator is quasi-unbiased and has a covariance matrix which is close to the Cramer-Rao lower bound. A practical algorithm is derived, and it is shown to be very easy to implement. This method is successfully applied to the problem of target motion analysis (TMA). >

This thesis discusses a mathematical approach to solving the bearings only target motion analysis problem. The problem of solving a sonar contact's tract can be reduced to a two variable minimization problem of finding the values of initial and final ranges to contact that minimize the sum of squares of error between the actual sonar bearings and the computed bearings given the estimated initial and final ranges. Because a submarine can use specific tactics when tracking a contact and because maximum detection ranges can be estimated, the general shape of the sum of squares of error function and its orientation are known. This research develops a search technique in which two conjugate gradient searches converge simultaneously from opposite sides of the optimum. Rules are developed to determine starting points which guarantee that the searches remain on opposite sides of the optimum in both variables. The stopping criterion for the search is the distance between the searches after each iteration. A measure of the progress of the search in terms of maximum distance from the optimum is guaranteed because either search is no further from the optimum at any iteration than the distance between the two searches. The research shows that a solution to the bearings only target motion analysis problem is obtained with the required accuracy more efficiently by searching simultaneously from opposite s ides of the optimum than by searching from one point only. The advent of multi-processors and co-processors in small computers argues for exploring the concept of searching simultaneously from multiple starting points. Multiple search in itself is not a solution. Criteria are still needed for stopping the searches and choosing a solution from the results of the two searches. This study tests a number of criteria for stopping the search and also evaluation criteria for choosing a solution.

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.

In bearings-only target motion analysis (BOTMA), the common hypothesis is that the observer maneuvers while the source is traveling according to a constant speed and course. But there is no reason that the roles of each protagonist cannot be exchanged. In this paper, we consider an uncommon scenario: the trajectory of the target is composed of two legs (i.e., a target brutally changes its heading while keeping its speed) and the observer does not maneuver. We prove that the trajectory of the target is observable under nonrestrictive conditions. A batch estimator is proposed, and its performance is compared with the classic Cramer-Rao lower bound (CRLB). Monte-Carlo simulations reveal the efficiency of the estimator. The robustness to a nonbrutal heading change is evaluated. Then an adhoc estimate taking the correct model into account is proposed and applied to simulated data. Again, its performance is compared with the CRLB, and its robustness is evaluated when the speed of the target changes. Finally, the respective performances of the target motion analysis (TMA) made by each mobile are compared.

This paper describes a technique for combining state estimates from two nonlinear filters for the purpose of providing a global estimate that converges to the correct state value, while the estimates from the individual filters may not. The practical situation considered-occurs in target motion analysis. In this case, there are two extended Kalman filters in the tracking system, one processing bearing, and the other processing conical angle measurements provided by spatially separated sensors. Prior to an observer maneuver, the target parameters from each filter will not converge. However, by using the proposed technique, the global estimate-a combination of the two estimates--converges to the correct value without an observer maneuver, as the numerical results show.

In multiple-input multiple-output (MIMO) radar, to estimate the reflection coefficient, spatial location, and Doppler shift of a target, maximum-likelihood (ML) estimation yields the best performance. For this problem, the ML estimation requires the joint estimation of spatial location and Doppler shift, which is a two dimensional search problem. Therefore, the computational complexity of ML estimation is prohibitively high. In this work, to estimate the parameters of a target, a reduced complexity optimum performance algorithm is proposed, which allow two dimensional fast Fourier transform to jointly estimate the spatial location and Doppler shift. To asses the performances of the proposed estimators, the Cramer-Rao-lower-bound (CRLB) is derived. Simulation results show that the mean square estimation error of the proposed estimators achieve the CRLB.

In weak GNSS signal environments, extending integration time is paramount to improving the GNSS receiver's sensitivity. Furthermore, sufficient coherent integration can help to separate the line-of-sight (LOS) and non LOS (NLOS) signals — primarily in the Doppler domain — for multipath mitigation. However, extending integration time is limited by the presence of the navigation message data bits. The Maximum-Likelihood (ML) estimation method has been shown as the most effective way to estimate the navigation bit boundary locations (i.e., bit synchronization) and subsequently estimate the data bit values (i.e., bit decoding) in the presence of noise alone. This paper further analyzes the performance of ML estimation method as a function of various other parameters by using the successful synchronization rate (SSR) and successful decoding rate (SDR) as the criteria. The parameters considered include the number of data bits required (i.e., integration time) and the Doppler frequency error, and both are evaluated under different signal strength scenarios. The requirements for bit synchronization are analyzed under three SSRs, which are 85%, 90% and 95%. Results indicate that, under the SSR of 90% and the SDR of 90%, bit synchronization and bit decoding are valid for signal strengths of 15 dB-Hz and 20 dB-Hz respectively, and the Doppler frequency errors should not be larger than 24 Hz and 11 Hz respectively.

Active Doppler sonar systems are gaining popularity as a method to profile remotely the oceanic velocity field. Estimation of the variance with which an ideal single.beam sonar system measures relative Doppler motion is the subject of this paper. Theoretical formulations, simulated reverberation data, and sea data are considered to determine this variance. The sea data considered in this paper were selected carefully to avoid performance degrading physical/instrumental phenomena and bias corrected to remove further instrumental effects. Asymptotic performance, determined in this research, is realized when instrumental and physical phenomena do not measurably affect experimental performance. Oceanic and simulated data sets are processed with several velocity-estimation algorithms that are implemented in existing sonar systems. These algorithms are compared against each other and the velocity-estimation standard-deviation results tabulated. The algorithms are shown to possess similar performance characteristics when simulated, and sea data are processed in spite of the fact that some of these algorithms are suboptimal from a theoretical viewpoint. The results of this study show that the asymptotic performance of the Doppler system examined is approximately 25 percent above a theoretical lower bound.

The traditional Dectecion of Envelope Modulation on Noise (DEMON) method is difficult to obtain the modulation characteristics when the signal modulation frequency or depth changes with time. For non-stationary modulation characteristics, a modified method 'HDEMON' based on Hilbert-Huang Transform (HHT) is given. HDEMON is available for non-stationary mudulation due to its adaptivity and applicability to non-stationary signals. Simulated data and actual radiated noise data analysis show that, HDEMON can extract non-stationary broadband amplitude modulation characteristics, which could suport both modulation source for the target classification and motion analysis.