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The carrier-to-noise density ratio (C/N0) is considered an important parameter describing the GPS receiver performance. This paper compares the performance of two popular coarse-acquisition (C/A) C/N0 algorithms appearing in literature: the variance summing method (VSM) [1, 2], and the power ratio method (PRM) [3, 4], in terms of their estimates in 1) additive white Gaussian noise (AWGN), 2) narrowband continuous wave interference (CWI), 3) their response to quantization and saturation effects, and their 4) dynamic range. The algorithms were implemented as a part of a software receiver. Two Li GPS data sets are examined; one was obtained from a GPS raw data collection setup, while the other was obtained from a GPS signal simulator. The collected set was stored with almost constant C/N0 level while the simulated one contained variable C/N0 levels. The effect of adding AWGN on the C/N0 estimate was directly proportional with the noise power. The C/N0 estimates suffered more when the CWI frequency was closer to the IF of the receiver. The PRM suffered from saturation at higher C/N0 levels. The VSM showed good tracking at high C/N0 levels and better immunity to limited quantization levels, while its C/N0 estimate suffered from rapid fluctuations in power levels when sudden power steps occurred. [ABSTRACT FROM AUTHOR]

Quasi-constant envelope phase-shift keying (PSK) is analyzed to assess its ability to overcome nonlinearities caused by fully saturated RF power amplifiers in communications systems. These modulations can achieve bit error rate (BER) performance comparable to linear BPSK in additive white Gaussian noise (AWGN) channel. Quasi-constant envelope offset quadrature PSK (OQPSK) is presented as a design example. At a BER = 10-5, the SNR degradation caused by fully saturated power amplifiers is 0.1 dB. The simulated BER matches analytically derived results. For a communications system employing the quasi-constant envelope OQPSK and a rate 1/2 convolutional code with K = 7, the demodulation performance is degraded by 0.25 dB at a HER = 10-5 when a fully saturated power amplifier is employed. [ABSTRACT FROM AUTHOR]

The theory and technique of angle-of-arrival (AOA) estimation using random-noise or other stochastic transmit waveforms is addressed. The additional uncertainties induced by signal itself and the statistical complexity of the received signals result in major challenges. The statistical properties of the random-noise interferometer and monopulse radar system are studied and compared theoretically using an approximation method. Furthermore, a random-noise coherent correlation receiver (CCR) architecture is proposed. The concept of mean monopulse characteristic curve (MMCC) is introduced. Experimental results using an X-band random-noise monopulse radar system validate the theoretical predictions of random-noise monopulse characteristics and suggest potential applications such as surveillance, imaging, and maneuvering target tracking. [ABSTRACT FROM AUTHOR]

A fast and convenient alignment method is proposed. To improve the speed of convergence, we used rotation vectors instead of traditional Euler angles. Furthermore, we developed an algorithm to automatically tune the measurement noise covariance matrix using adaptive Kalman filtering. Finally, the developed algorithms were applied to an aerial imaging system to automatically geo-locate the centers of the images. [ABSTRACT FROM AUTHOR]

An algorithm to detect and estimate a linear mixture of deterministic signals corrupted by white Gaussian noise is presented. The number of signals is assumed to be unknown, and the noise power can be either known or unknown. The algorithm is based on an information-theoretic criterion in which the probability of false alarm can be adjusted; typical information criteria, such as the Akaike (AIC) and the minimum description length (MDL) criteria, can be regarded as particular cases of it for given probabilities of false alarm. The proposed approach includes the use of the atomic decomposition and the expectation maximization (EM) algorithms to efficiently approximate the signal maximum likelihood estimate. For the first time, upper-bounds for the probabilities of underestimation and overestimation of the number of signals are obtained. In addition, the constant false-alarm rate (CFAR) characteristic is shown, and the statistical efficiency of the signal parameter estimation is discussed and illustrated by simulation. Numerical experiments show the suitability of the algorithm for signal interception by using synthetic and real-life radar signals. [ABSTRACT FROM AUTHOR]

This study is concerned with multi-target tracking (MTT). The Cramér-Rao lower bound (CRB) is the basic tool for investigating estimation performance. Though basically defined for estimation of deterministic parameters, it has been extended to stochastic ones in a Bayesian setting. In the target tracking area, we have thus to deal with the estimation of the whole trajectory, itself described by a Markovian model. This leads up to the recursive formulation of the posterior CRB (PCRB). The aim of the work presented here is to extend this calculation of the PCRB to MTT under various assumptions. [ABSTRACT FROM AUTHOR]

Generalized likelihood ratio tests (GLRTs) are derived for the problem of detecting targets in hyperspectral Sages. These detectors are derived under the assumptions that the signals from the materials in the image mix linearly and that the noise in the system is Gaussian. It is also assumed that the abundances of the signals from the various materials in a pixel must sum to one. This constraint models the fact that the material abundances in a pixel are just the fraction of the pixel that they occupy. Under these assumptions, detectors are derived which outperform the detectors derived without the sum-to-one constraint. [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]

Considers the problem of detecting distributed targets in compound-Gaussian noise with unknown statistics. Assumption that a set of secondary data, free of signal components is available to estimate the covariance matrix of the disturbance; Assessment of detection strategies based on the Rao test and the Wald test; Comparison of the performance of the Wald test.

Examines the problem of direction of arrival (DOA) estimation of a single point source in white Gaussian noise under one-bit quantization. Problem formulation; Discussion on the quadrivariate orthant probability and the one-bit Cram é r-Rao bound for a two-sensor array; Description on a set of DOA estimators for one-bit clipped data; Monte Carlo simulation results.

Discusses the subject of detection of spatially distributed targets in non-Gaussian noise with unknown statistics. Factors that can contribute to the improvement of high resolution radar performance; Detection of distributed targets embedded in non-Gaussian noise; Ways to make a detector fully adaptive.

Investigates the receiver operating characteristics of an ultrawideband random-noise radar. Description of the radar system; Relationship between input variables and cross-correlation coefficient; Characteristic function and joint PDF for integrator outputs.

Describes the nonlinear filtering schemes for systems which allow non-Gaussian noise. Description of the most probable trajectory filtering approach; Hybrid approximation to nonlinear systems.

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.

Presents information on a study on the adaptive coherent radar detection of Swerling I targets against compound-Gaussian clutter. Problem statement and clutter model; Performance analysis; Concluding remarks.

Presents information on a study on the radar detection of incoherent pulse trains in compound-Gaussian disturbance with known spectral density. Problem formulation; Design of the detector; Conclusions.

Focuses on a study that developed approximate expressions for the receiver operating characteristics of two binary detectors, one based on random-vector observations and the other seeking continuous-time Gaussian signals in white Gaussian noise. Performance approximation; Random-vector observations; Gaussian signals in white Gaussian noise.

Focuses on a study on the distribution of the envelope of a sum of random sine waves and Gaussian Noise. Quasi-Edgeworth series; Sine wave examples; Series approximations.

Looks at the use of minimax detectors, to locate signals with unknown parameters in additive white Gaussian noise, highlighting research conducted on several minimax strategies for real and complex signals. Examination of distribution-free minimax filters for real signals; Use of favorable distribution in minimax filter design.

Presents signal-plus-order statistic CFAR (S + OS), a constant false alarm rate (CFAR) test using distributed sensors. Problem for detecting a Rayleigh fluctuating target in Gaussian noise; Comparisons of various schemes based on numerical study involving a two-sensor network; Summary and conclusion of the study.

Presents on a suboptimal algorithm for modulation classification to classify the general M-ary phase-shifted keying (MPSK) signal buried in additive white Gaussian noise (AWGN). Phase model of general MPSK signals; Derivation of phase probability density functions; Structure of suboptimal classifier; Performance evaluation and comparison of the classifier; Conclusion of the study.

Studies the design of constant false-alarm rate tests for detecting a weak signal in Gaussian noise with an unknown spatial covariance. Test studied; Results shown by the study; Detailed information on the experiment.

Presents the front cover/table of contents for this issue of this publication. [ABSTRACT FROM PUBLISHER]

A closed-form expression is derived for the diagonally-loaded generalized likelihood ratio test (GLRT) probability of detection considered by Ayoub and Haimovich [1]. [ABSTRACT FROM AUTHOR]

Presents the table of contents for this issue of the periodical. [ABSTRACT FROM AUTHOR]

The inertial navigation system (INS)/GPS integration system is designed by employing an adaptive filter that can estimate measurement noise variance using the residual of the measurement. To verify the efficiency of the proposed loosely-coupled INS/GPS integration system, simulations were performed by assuming that GPS information has large position errors. The simulation results show that the proposed integration system with the adaptive filter is more effective in estimating the position and attitude errors than a system that uses the extended Kalman filter. [ABSTRACT FROM PUBLISHER]

Discrete linear optimal and unbiased transversal filters are proposed for onboard receiver channels to estimate continuous/discrete time-varying state-space signal models. The solutions are first found in the batch form. For optimal filtering, the mean square initial state function is determined a posteriori by the discrete algebraic Riccati equation. The unbiased filter is also represented in the iterative Kalman-like form with no requirements for noise and initial state. The filtering error bound is found in the three-sigma sense via the noise power gain (NPG). A fast iterative algorithm for NPG is also supplied. A comparison with the Kalman channel is given employing the harmonic two-state space model. [ABSTRACT FROM PUBLISHER]

Instead of using the empirical algebraic expression offered by H. Urkowitz for noncoherent gain factor calculations, standard MATLAB commands may be successfully applied. [ABSTRACT FROM PUBLISHER]

This correspondence shows that the expression for the marginal density of the kinematic state in [1], which is valid only if the kinematic state is fully observable by the sensor, can be generalized. Our second comment concerns the direct integration of a scaling factor for the extent estimate. Thirdly, we show an equivalent update step based on sequential processing of single measurements (instead of the indirect parameters "mean measurement" and "measurement spread" in [1]). [ABSTRACT FROM PUBLISHER]

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]

A method is proposed for joint detection of independent, identically-distributed (IID) zero-mean bivariate Gaussian noise sequences. Received sequences are used to estimate ratio statistics that span a three-dimensional basis, and deviations from an asymptotic form are used to generate the final detection statistic. Sensitivities of the measure to statistical deviations are shown. The technique is applied to radiometric detection and is shown to achieve performance close to ideal energy detection, but without requirements for noise estimation. [ABSTRACT FROM AUTHOR]

Pseudonoise (PN) regenerative ranging on-board acquisition performance is assessed in terms of the acquisition time for a given probability of acquisition at a reference signal-to-noise ratio (SNR). So far the detection of the correct ranging code phase alignment has been analyzed by a maximum likelihood (ML) estimation with no amplitude quantization at the chip matched filter output. Despite its optimality in an additive white Gaussian noise (AWGN) channel, an ML estimation finds a maximum value also when no ranging signal (i.e., noise-only) is present at the receiver input thus resulting in a false alarm (FA) detection. On the contrary a suboptimum threshold comparison estimation of the correct ranging code phase alignment can overcome such a hindrance. This work reports the on-board PN regenerative ranging acquisition performance based on threshold comparison with 3-bits soft-quantized correlators along with the relevant trade-offs with respect to the ML estimation. [ABSTRACT FROM AUTHOR]

Interference cancellation techniques in multi-channel radar and communication systems, such as adaptive beamforming, are only effective if the response of each channel used is well matched. Though hardware variations between channels limit the intrinsic channel response match, digital equalization techniques improve channel-to-channel matching, as measured by the channel-pair cancellation ratio (CPCR). Digital receiver channel nonlinearities such as analog-to-digital converter (ADC) saturation and third-order nonlinearities, however, limit the level of channel matching achievable. We compare the effect of the use of linear frequency modulation (LFM) and band-limited Gaussian noise measurement (BLGN) signals on CPCR. We determine the impact of ADC saturation and analog-component third-order nonlinearities on each measurement signal through analysis, simulation, and experiments using a pair of S-band digital receivers. We show that the maximum achievable CPCR is lower for BLGN than LFM measurement signals due to ADC saturation and third-order nonlinearities. [ABSTRACT FROM AUTHOR]

The purpose of the work presented here is to determine which signal model, stationary or cyclostationary, is more suitable for signal detection in different representative cases. We consider different detectors depending on the available signal knowledge. As cyclostationarity-based detectors, the multi-cycle, single-cycle, and a new detector for unknown signals, denoted maximum spectral correlation amplitude (MSCA) detector, are analyzed. The MSCA detector performs an efficient search on the whole cyclic spectrum based on the fast Fourier transform (FFT) accumulation method (FAM) for cyclic-correlation estimation. Analytical approximations of the false alarm and detection probabilities of the MSCA detector are derived. Additionally, the optimum radiometer and a channelized radiometer are included in the work as radiometric detectors. All these detectors are designed for stationary white Gaussian noise (WGN), their sensitivity is evaluated also for different nonideal environments, which comprise nonstationary or non-Gaussian noise. Our results show that, in general, cyclostationarity-based detectors only find application when the noise power is unknown and it is not possible to use a CFAR (constant false alarm rate) strategy for the radiometer. [ABSTRACT FROM AUTHOR]

This paper gives a detailed performance analysis for the novel radar long-time coherent integration method, i.e., Radon-Fourier transforms (RFT). Some important properties of RFT, e.g., two-dimensional (2D) impulse response, 2D translational invariance, multitarget linear additivity, linear signal-to-noise ratio gain in additive white Gaussian noise (AWGN), as well as the 2D correlation function of transformed AWGN, are derived for continuous and discrete RFT, respectively. However, because of discrete pulse sampling, finite range resolution, and limited integration time, the "blind-speed sidelobes (BSSL)" of discrete RFT may inevitably appear in real applications. Although the BSSL are reduced with the increase of the blind-speed integer, they may still lead to false alarms or loss detections in a real multitarget scenario. Based on the analytic expression derived for BSSL, the causes of BSSL are analyzed and the effective BSSL suppression methods are proposed. Finally, numerical experiments are also provided to demonstrate the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

In the general framework of radar detection, estimation of the Gaussian or non-Gaussian clutter covariance matrix is an important point. This matrix commonly exhibits a particular structure: for instance, this is the case for active systems using a symmetrically spaced linear array with constant pulse repetition interval. We propose using the particular persymmetric structure of the covariance matrix to improve the detection performance. In this context, this work provides two new adaptive detectors for Gaussian additive noise and non-Gaussian additive noise which is modeled by the spherically invariant random vector (SIRV). Their statistical properties are then derived and compared with simulations. The vast improvement in their detection performance is demonstrated by way of simulations or experimental ground clutter data. This allows for the analysis of the proposed detectors on both real Gaussian and non-Gaussian data. [ABSTRACT FROM AUTHOR]

In the work presented here, we contribute to two extensions to the sequential Monte Carlo (SMC) or particle filtering approach for on-line sinusoidal frequency tracking in [1]. First, we propose to employ multiple dynamical models for better parameter tracking at different times in the light of observations as the unknown frequency and its rate of change are time varying. Second, \alpha-stable noise, which is a generalization of the traditional Gaussian process, is considered as the additive disturbance in the observed signal. In other words, the proposed method is required to estimate both the unknown time-varying frequency and the characteristics of the \alpha-stable process. According to computer simulation results, it is found that the proposed multiple-model method performs better in terms of smaller estimation error than the case when only a particular single model is employed. [ABSTRACT FROM PUBLISHER]

It is well known that ultra-wideband (UWB) noise radar achieves high-resolution imaging of targets and terrain. The wide bandwidth yields fine range resolution, while the noise waveform provides immunity from detection, interference, and interception. Having multiple noise radars networked with each other provides significant benefits in target detection and recognition. We propose a scheme to modify the UWB noise radar to supplement it with secure multi-user network communication capabilities, thus architecting the UWB noise-orthogonal frequency-division multiplexing (OFDM) netted radar. The salient features of this multi-functional radar system include surveillance with embedded security-enabled OFDM-based communications, multi-user capability, and physical layer security. The system's physical layer design is analyzed for the composite UWB noise-OFDM waveform's dual performance, communications reliability, confidentiality, and message integrity protection. We also present critical issues that underscore the need for a unique medium access control (MAC) algorithm as a duplex multiplexing technique for this ad hoc multi-radar communications network. [ABSTRACT FROM PUBLISHER]

We investigate decentralized detection in clustered sensor networks with hierarchical multi-level fusion. We focus on simple majority-like fusion strategies, leading to closed-form analytical performance evaluation. The sensor nodes observe a binary phenomenon and transmit their own data to an access point (AP), possibly through intermediate fusion centers (FCs). We investigate the impact of uniform and nonuniform clustering on the system performance, evaluated in terms of probability of decision error on the phenomenon status at the AP. Our results show that, under a majority-like fusion rule, uniform clustering leads to the minimum performance degradation, which depends only on the number of decision levels rather than on the specific clustered topology. We then extend our approach, taking into account the impact of spatial variations of the phenomenon, noisy communication links, and weighed fusion rules. Finally the proposed distributed detection schemes are characterized with simulation and experimental results (relative to IEEE 802.15.4-based networks), which confirm the analytical predictions. [ABSTRACT FROM PUBLISHER]

In this paper, a heuristic detector is proposed to detect range-spread targets in white Gaussian noise using multiple consecutive high- resolution range profiles (HRRPs) received from a high-resolution radar (HRR). The detector consists of refiners of HRRPs and a cross-correlation integrator of refined HRRPs. Based on the fact that strong scattering cells are sparse in target HRRPs, nonlinear shrinkage maps are designed to refine received HRRPs before integration, by which most of the noise-only cells in received HRRPs are suppressed while strong scattering cells most probably relevant to target signature are preserved. Since the target's scattering geometry is almost unchanged except for range walking during integration, the refined target HRRPs from consecutive pulses are highly similar while refined noise-only HRRPs are dissimilar due to randomicity. The modified correlation matrix of multiple refined HRRPs is used to measure their similarity. The test statistic, a weighted integration of the entries of the modified correlation matrix, is constructed for target detection. The proposed detector does not depend on a strict target return model and can work in mild conditions. The real target data and simulated noise are used to evaluate the detector, and the experimental results show that it achieves better detection performance than some existing methods. [ABSTRACT FROM AUTHOR]

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]