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Parameter estimation in the presence of noisy measurements characterizes a wide range of computer vision problems. Thus, many of them can be formulated as errors-in-variables (EIV) problems. In this paper we provide a closed form likelihood function to EIV problems with arbitrary covariance structure. Previous approaches either do not offer a closed form, are restricted in the structure of the covariance...
This paper presents an estimation procedure for exploiting multipath signal propagation in shallow water to perform passive ranging. The scenario of interest is a pair of sensors receiving an underwater acoustic signal that has arrived via a bounce from the ocean floor. This approach also applies in other situations, for example, to multiple airborne sensors passively receiving emissions from an airborne...
A target can be positioned by wireless communication sensors. When the range based sensors have biased measurements, an Expectation Maximization (EM) algorithm is proposed to jointly estimate the target state and sensors' biases, including the batch EM and sliding window EM algorithms. To implement the algorithms, the Iterated Extended Kalman Smoother (IEKS) is also embedded in the EM algorithm. The...
The identification of accurate disturbance models from data has application both to estimator design and controller performance monitoring. Methods to find the disturbance model include maximum likelihood estimation, Bayesian estimation, covariance matching, correlation techniques (such as autocovariance least-squares), and subspace identification methods. Here we formulate a maximum likelihood estimation...
Signal estimation in MIMO communications typically suffers from performance degradations due to imperfect channel state information (CSI). Traditional robustification schemes rely on assumptions about the model uncertainty and may result in conservative performance. We introduce a rank-reduction approach that enhances the performance in training-based applications. A sequence of reduced-rank channel...
In this paper, we consider the CRT problem for real numbers with noisy remainders that follow wrapped Gaussian distributions. We propose the maximum likelihood (ML) estimation based CRT when the remainder noises may not necessarily have the same variances. The proposed algorithm only needs to search for the solution among L elements, where L is the number of remainders. We compare the performances...
Sensor pattern noise (SPN) has been proved to be an inherent fingerprint of a camera, and it has been broadly used in the fields of image authentication and camera source identification. However, the SPN extracted using current denoising algorithm always contains image content residual, which would significatively influence the accuracy of camera source identification. In this paper, a novel patch-based...
In this paper, joint sensor localization and synchronization in non-cooperative wireless sensor networks (WSNs) using time-of-arrival (TOA) measurements is studied. In addition to zero-mean errors in TOA measurements we consider other sources of error such as non-line-of-sight (NLOS) propagation and anchor uncertainty to make our technique more useful in practice, where the presence of these errors...
In a cognitive radio (CR) scenario, we study the joint problem of spectrum sensing and jamming detection. Modelling the scenario as a multiple hypothesis testing problem, we analyse the probability of detection of the optimal detector in the sense of Neyman-Pearson theorem. We derive one exact form in terms of a series and a closed-form version. Moreover, we evaluate the asymptotic probability of...
In this paper the numerical behavior of different sine wave fitting methods is investigated. In addition to the Three- and Four-Parameter Least Squares Fits, also the Maximum Likelihood and the Quantile Based Estimator methods suffer from similar numerical problems that may disturb the result of the ADC test. Suggestions are given in order to improve the performance of the investigated algorithms.
Binary Output Systems (BOSs) generate Bernoulli distributed outputs with the given parameter. Such systems are quite common in various fields, and the system performance is usually measured by success rate or correct rate. Traditional parameter optimization methods utilize system performance approximations calculated by averaging the binary outputs. The binary outputs are used only once in the approximation...
Interferometry is a powerful method to estimate direction of arrival (DOA) in seafloor mapping. Its main difficulty derives from a 2π-ambiguity in phase, complicated by the presence of noise. Here, the Vernier method is explained and its performance is analyzed. This performance is compared against that of three other DOA estimators, namely the multiple signal classification (MUSIC) algorithm and...
If there are significant amounts of data missing, this requires special algorithms for system identification. Such methods have been previoulsy developed and typically result in iterative procedures for the parameter estimation. Since missing data could be viewed as irregular sampling (decimation) of the signals, it is obvious that there is a risk for aliasing. In this case aliasing manifests itself...
The purpose of this paper is to formulate and study the problem of system identification with Gaussian noise and quantized observations. The prime examples that we study are Gaussian AR(1)-systems and the simplest Gaussian linear regression. The main results of the paper are the development of a randomization technique for the effective solution of the likelihood equation and computational experiments...
The problem of estimating the time-of-arrival (TOA) of a known signal in the presence of interferences and multipath propagation is addressed. This problem, is essential in high precision receivers of the Global Navigation Satellite Systems. This paper presents the maximum likelihood TOA estimator when an antenna array is used in the receiver. The desired signal impinges the array with a known direction-of-arrival...
By using multiple receiving antennas and modeling co-channel interference (CCI) together with the noise as additive temporally white Gaussian noise with some spatial color, CCI may be suppressed. This paper proposes spatio-temporal interference rejection combining by modeling the CCI and noise as an autoregressive Gaussian process. In this way, the joint spatial-temporal properties of the CCI may...
Estimating the frequencies, amplitudes and phases of sinusoids in noise is a problem which arises in many-applications. The aim of the methods in this paper is to achieve computational efficiency and near-ML performance (i.e. low bias, variance and threshold SNR), in problems such as vibration or audio analysis where the number of tones may be large (e.g. > 20). An approach has recently been published...
This paper examines the application of nonlinear signal processing techniques to the development of adaptive equalisers for frequency domain multiple access (FDMA) and multi-user detectors for code division multiple access (CDMA). Current issues are discussed and key problems identified.
We address the problem of estimation of the fractional-power spectrum of certain classes of symmetric, alpha-stable (SαS) processes. We start with a summary of the key definitions and results from the theory of stationary, harmonizable SαS processes and proceed to discuss the performance of fractional-power periodograms. Next, we present a high resolution fractional-power spectrum estimation algorithm...
In many signal processing applications, one has to solve an overdetermined system of linear equations Ax ≈ b, while minimizing the errors on A and b. The Total Least Squares (TLS) method calculates corrections ΔA and Δb such that (A + ΔA)x = b + Δb and ||[ΔA Δb]||F is minimal. The resulting parameter vector x is ä Maximum Likelihood (ML) estimate when the noise on the different entries of [A b] is...
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