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We focus on the design of a Kalman filter-based algorithm to track multiple targets in range and azimuth. The detection stage implements a multitarget strategy aimed at collecting and eventually processing all target measurements. Existing tracks are updated using a joint probabilistic data association (JPDA) algorithm to handle the data association problem.We model target echoes as coherent pulse trains in white Gaussian noise with unknown power. The performance analysis assumes a maritime scenario and shows that the proposed tracker outperforms more conventional approaches. [ABSTRACT FROM AUTHOR]

In the work presented here, we consider state and parameter estimation of a semi-nonlinear Markov jump system in a non-Gaussian noise environment. The non-Gaussian measurement noise is approximated by a finite Gaussian mixture model (GMM). We propose a maximum likelihood (ML) solution to this state estimation problem which leads to two expectation-maximization (EM) algorithms. The first is a batch EM method which takes all the available data in the conditional expectation of the state in the E-step. An interacting multiple model (IMM) smoother is employed to evaluate the conditional expectation of the state by which a suboptimal estimate of system state is directly obtained. The Gaussian mixture parameters are then updated in the M-step. The second is a recursive EM algorithm which results from a stochastic approximation procedure and uses a standard IMM filter. For performance evaluation, posterior Cramer-Rao bound (PCRB) on the state estimation is adopted. Simulation results verify the effectiveness of the oposed algorithms. [ABSTRACT FROM AUTHOR]

This paper describes an efficient model to describe an autoregressive (AR) signal with slowly-varying amplitude in additive white Gaussian noise (WGN). Even a simple low-order AR model becomes complicated by varying amplitude and additive white noise. However, by approximating the signal amplitude as piecewise-constant, an efficient filtering approach can be applied in order to compute the maximum likelihood (ML) estimate for the entire data record. The model is efficient both in terms of having a compact set of parameters and in the computational sense. Simulation results are provided. The algorithm has applications in signal modeling for underwater acoustic signals, particularly active wideband signals such as explosive sources. [ABSTRACT FROM AUTHOR]

Blind source separation (BSS) and blind beamforming schemes attempt to separte additive source signals, collected at a sensor array, and their direction-of-arrival (DOA). To this extent, many different and powerful algorithms have been produced to accomplish these tasks. However, unlike an optimal or adaptive method, an algebraic method for BSS and blind beamforming has the relative advantage of simplicity. This paper investigates such technique that provides closed-form, algebraic expression for BSS and beamforming in terms of performance and reliability. We provide these results, as well as a summary of the properties, and possible uses for this technique, in contrast to more optimal, adaptive techniques. [ABSTRACT FROM AUTHOR]

Presents information on a study which discussed an algorithm for high resolution time delay estimation in Gaussian noise. Application of time-delay estimation; Results; Conclusions.

Presents information on a study which addressed the problem of state density estimation using nonlinear additive Gaussian noise measurements. How to address the problem of discrete-time state estimation; Structure of a proposed algorithm for density estimation; Results; Conclusions.

This paper presents alternate forms of a signal tracking algorithm for Global Navigation Satellite System (GNSS) receivers that use a maximum likelihood estimation (MLE) technique. The cost function of the MLE for estimating signal parameters such as code delay, carrier phase, and Doppler frequency is used to derive discriminator functions to create error signals from incoming and reference signals. Assuming a code-free signal and an additive white Gaussian noise, we derive an efficient, practical form of general purpose signal tracking algorithms by using a noniterative MLE approach for arbitrary spreading codes and modulation schemes in accordance with computational efficiency. Two versions of an MLE algorithm are derived. The first is a coherent MLE algorithm derived from the gradient of the log-likelihood cost function for signal parameters. The second is a noncoherent MLE algorithm derived on the basis of complex domain signal that is insensitive to carrier phase error and data bits, thereby eliminating the coupling effect between the carrier phase and Doppler frequency. Analytical test results demonstrate the performance of the proposed algorithms in comparison with those from conventional approaches in terms of pull-in-range, coupling effect, and computational efficiency. [ABSTRACT FROM PUBLISHER]

In a GNSS receiver the measure of C/N_0 is not only supplementary information provided to the user together with the PVT (position, velocity, time), but it is also fundamental information to determine the status of the tracking subsystems and to control the receiver in critical environments. A classic approach to estimate the C/N_0 in a navigation receiver is known as the narrowband-wideband power ratio method (NWPR). We investigate the suitability of different estimation methods, properly adapted from the digital communications world where a wide range of literature is available on the problem of estimating the signal-to-noise power ratio (SNR) of M-PSK modulations in additive white Gaussian noise (AWGN). The objective here is to identify a subset of candidate algorithms for SNR estimation, previously proposed for communications receivers. These methods are compared in specific situations of interest, such as weak signals or high signal-to-noise conditions obtained with high-gain antennas, and considering software defined radio receiver implementations. Simulation campaigns and a set of real GPS-L1 signals are used to test and compare the performance of the selected algorithms in a broad range of C/N_0 values (from 20 to 70 dBHz), where the classic NWPR method is used as an authoritative benchmark. We conclude that a number of low-complexity algorithms for SNR estimation, based on a simple communication signal model, can be tailored to digital GNSS receivers. The performance is comparable with respect to the NWPR approach, but the complexity is reduced and possible improvements can be foreseen using the tested algorithms. [ABSTRACT FROM AUTHOR]