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In stochastic estimation problems, we aim to estimate an unknown state of interest given a set of measurements received from noisy sensory devices, such as radar, light detection and ranging, etc. A common model of the measurements’ random error is white Gaussian noise. This noise model is usually used to derive an estimator of the unknown state; accordingly, the state is described as a random variable with a Gaussian density. In some applications, the unknown state is known to take only positive values, e.g., estimating size or dimension. In such a case, the classical approaches based on Gaussian densities might fail, i.e., produce a negative value. In this article, a recursive Bayesian filter is proposed based on modeling the unknown state as Wishart distributed random matrix. This model ensures that the probability densities of the random variables are restricted to positive real values, even though the measurement’s noise is still modeled as white Gaussian noise. The feasibility of the proposed Bayesian–Wishart filter is demonstrated within the framework of extended target tracking. A target contour measurement model of a symmetric star-convex shape is presented and integrated in the proposed filter to track the target’s extent. [ABSTRACT FROM AUTHOR]

This article presents an image-based radar detector, named neighborhood difference order statistics (NDOS) detector. Different from the classic likelihood ratio detector, the proposed detector treats the echo spectrum as an image and determines the existence of a target by comparing the difference between the test cell and its adjacent cells with a threshold. The closed-form expressions of probabilities of detection and false alarm are derived under Gaussian noise background and Swerling I target model. It is proved that the detection performance of the proposed detector approaches the optimal likelihood ratio detector when the homogenous noise power is known. When the noise power is unknown, we modify the detector into cell-averaging (CA) NDOS detector by estimating the noise power. Analytical derivations show that the CA-NDOS detector holds the constant false alarm rate (CFAR) property. Moreover, CA-NDOS detector possesses a better detection performance compared with two typical CFAR algorithms under the condition of typical reference window size. [ABSTRACT FROM AUTHOR]

This article proposes two algorithms for the localization or motion analysis of a moving target, by using a number of stationed sensors observing the frequency of the emitted target signal. This is a challenging problem due to the complicated non-linear relation between the target location parameters and the observations. The first algorithm is closed-form through algebraic evaluation that is computationally efficient. The second is derived from semidefinite programming by relaxationthat is robust against noise. The situations of having target frequency known but erroneous and completely unknown are investigated, as well as the presence of sensor position errors. Sequential algorithms for motion analysis are proposed in addition to the two for reducing latency and complexity. The algorithms are analyzed and their performance is able to achieve the CRLB accuracy under Gaussian noise. [ABSTRACT FROM AUTHOR]

Many high-resolution maritime radars for sea-surface small target detection transmit pulses with large time-bandwidth-products to increase transmitter power and to use the pulse compression technique for high range resolution. On the sea surface, besides small targets of interest, there exist many uninteresting large objects such as big ships, reefs, and offshore platforms for oil production. Large objects have too strong radar returns and their range sidelobes often mask wide range intervals. In these range intervals, masked small targets are difficult to be detected directly. In this article, a complete scheme is proposed to find small targets masked by range sidelobes of large objects in high-resolution maritime radars. In this scheme, high-precision reconstruction of radar returns of large objects is crucial because tiny reconstruction errors are enough to mask weak returns of small targets of interest. An algorithm using the linear programming and oversampled high-resolution range profile model of a large object is given to obtain high-precision reconstruction of the strong radar returns of a large object. From residual radar returns removed the reconstructed strong returns, a near-optimal coherent detector under sea clutter modeled by the spherically invariant random vector with inverse Gamma texture plus colored Gaussian noise is constructed to find masked small targets. Simulated experiments and real measured radar data are used to verify the effectiveness of the proposed scheme and method and reveal their limitations. The results show that the proposed scheme and method are all effective unless small targets fall into the range mainlobe regions of large objects. [ABSTRACT FROM AUTHOR]

This article studies the problem of radar detection of correlated gamma-fluctuating targets in the presence of clutter described by compound models with correlated speckle. It models signals from a pulsed radar with pulse-to-pulse correlation of components of the range-gated returns from a target with added thermal noise and compound Gaussian clutter. If the correlation is not accounted for in a radar model, the required signal-to-interference ratio as predicted by the model for a given probability of detection will be incorrect, resulting in overestimated performance. Although more generally applicable, the focus of this article is on airborne maritime radar systems. Hence, $K$ -distributed sea clutter is used as the main example. Detection via square-law noncoherent pulse integration is formulated in a way that accommodates arbitrary partial correlation for both target radar cross section (RCS) and clutter speckle. The obstacle to including this degree of generality in previous work was the fact that Swerling’s original characterization of the standard RCS fluctuation classes based on specifying distributions for the power is not sufficient to accommodate inclusion of both sources of correlated returns (i.e., target and clutter speckle) in the case of gamma-fluctuating targets. An extension of the model is required at the quadrature component (i.e., voltage) level, as phase relationships can no longer be neglected. This is addressed in the present article, which not only postulates an extended model, but also demonstrates how to efficiently compute it, with and without a number of simplifying approximation schemes within the framework of the saddle-point technique. [ABSTRACT FROM AUTHOR]

Crowd management is an essential task to ensure the safety and smoothness of any event. Using novel technologies, including surveillance cameras, drones, and the communication techniques between security agents, the control of the crowd has become easier. Yet, the usage of such techniques is still not effective. This article presents an approach for crowd counting from drones' data. The proposed method exploits the dilated and scaled neural networks for feature extraction and density crowd estimation. A new dataset named ViseDrone2020 is used for training and testing of the proposed method. In order to compare the proposed method, we implemented 10 state-of-the-art methods and trained it on this dataset. The experiments show that the proposed model is more efficient for crowd counting compared with the implemented methods. Also, some of these methods give relatively accurate results in terms of estimated crowd numbers as well as the quality of estimated density maps. The proposed model was further evaluated on nondrone datasets, namely, UCF_QNRF, UCF_CC_50, and shanghaiTech_(A, B), which produced satisfying results for all the datasets. In addition, the proposed method was tested on noisy images where Gaussian noise and salt-and-pepper noise was applied to all the images of the dataset with a noise density of 0.02. The analysis showed that the quality of the density map as well as the quantity of the crowd count estimation is comparatively better than other existing methods without the presence of noise. After the acceptance, the code of the proposed method as well as 10 implemented methods will be available at: [Online]. Available: https://github.com/elharroussomar/Crowd-Conting-on-Drone-Data. [ABSTRACT FROM AUTHOR]

A new approach to exploit nonlinear reradiation of electronic circuits under test (ECUT) for classification of electronic devices using harmonic radar is proposed in this article. Unlike prior approaches, we develop a linear model to relate the measurements to the unknown parameters representing the nonlinear characteristics of the ECUT. Each nonlinear circuit under test has a distinguishable response to a power-varying single-tone or two-tone incident wave. As a result, each ECUT possesses a unique unknown deterministic vector of parameters derived from the proposed linear model. We use maximum likelihood estimator in the presence of complex white Gaussian noise based on the newly constructed linear model. The statistical features of the normalized estimated vectors of parameters are employed as distinguishing factors in classification of different nonlinear electronic devices using K-nearest neighbors classification method. Simulation results show that the proposed method of linear model with power-swept signals and estimated vectors of parameters successfully classifies nonlinear devices. [ABSTRACT FROM AUTHOR]

In this article, an algebraic solution for localizing a moving target using a nonstationary multistatic radar system is proposed. By utilizing the quadruple hybrid measurement set, which consists of time delay, Doppler shift, angle of arrival, and angle rate measurements, the proposed method can estimate the target position and velocity vectors via a simple one-stage weighted least squares estimator without the need for introduction of the nuisance parameters, which enables us to locate the target with the minimum number of antennas. The proposed estimator takes the uncertainty concerned with the position and velocity of the antennas in its design. The proposed estimator is shown to be approximately unbiased and efficient under small Gaussian noise level assumption, that is, the estimator achieves the smallest possible variance (the Cramér–Rao lower bound) among all other unbiased estimators. Numerical simulations demonstrate substantial performance improvement of the proposed estimator over the state-of-the-art methods presented for moving target localization. [ABSTRACT FROM AUTHOR]

This article addresses the multistatic localization of an object by exploiting the direct-blast and object-reflected signals, where the transmitter location is not known and only the sensor positions are available. We study the cases that either the object or the transmitter is moving, in a noncooperative environment at which unknown offsets are present in the time delay and Doppler shift measurements. We analyze thoroughly by the Cramer–Rao lower bound (CRLB) if the direct-blast signals are useful in increasing the object localization performance, albeit doing so introduces extra parameters of the transmitter for estimation. Performance comparison with the fully dynamic moving object and moving transmitter modeling is established. Algebraic solutions are derived for the localization problem and analysis is made to validate that the proposed estimators are able to reach the CRLB performance under Gaussian noise over the small error region. [ABSTRACT FROM AUTHOR]

It is always an important problem to recover sparse signals from observations corrupted by Gaussian noise and has been extensively investigated. In high-resolution maritime surveillance radars working at scan mode, ship classification, and recognition need to recover high-resolution range profiles (HRRPs) of ships from radar returns of several pulses with severe range sidelobe effect. Multipulse synthetic data by the aid of ship Doppler information are complex sparse signals corrupted by non-Gaussian correlated interference. In this article, a sparse recovery via iterative minimization (SRIM) method is proposed to estimate complex HRRPs of ships from multipulse synthetic data. The SRIM method adapts the non-Gaussianity nature of the interference in the multipulse synthetic data and ship complex HRRPs are modeled by the random sequences of the biparametric generalized Gaussian distributions (GGDs) (0 < p ≤ 1). In the SRIM method, the parameters of the GGD model are iteratively searched by the minimal criterion of the Kolmogorov–Smirnov distance of the residue and the interference model. The SRIM method is compared with the recent linear-programming-based method and the classic sparse learning via iterative minimization (SLIM) method by using simulated and measured radar data and the results show that the SRIM method obtains the better performance. [ABSTRACT FROM AUTHOR]

In this article, a target detection problem in homogeneous Gaussian noise with unknown covariance matrix is examined using multiple observations, which may be collected from multiple range cells, bands, and/or coherent processing intervals. In order to take into consideration mismatches of the target steering vector, we adopt a subspace model where the target steering vector is assumed to lie in a subspace spanned by the column vectors of a known matrix with unknown target coordinates. By exploiting persymmetric structures, we propose several adaptive detectors according to ad hoc modifications of generalized likelihood ratio test (GLRT), Rao test, and Wald test. It is found that the Rao test does not exist, whereas the two-step Wald test shares the same form as the two-step GLRT. Numerical examples show that the robustness of the proposed detectors is better than that of their counterparts in general. In particular, the proposed one-step GLRT is the most robust in most cases, and the proposed two-step GLRT and one-step Wald test can be more robust than the one-step GLRT in the case where the number of training data is small. [ABSTRACT FROM AUTHOR]

In this article, the problem of 3-D target localization from bistatic range measurements in multiple-input–multiple-output (MIMO) radars with distributed antennas is considered. A closed-form two-stage weighted least squares solution is developed, which is able to uniquely determine the target position with a smaller number of sensors (transmitters or receivers) compared to the other existing closed-form methods. The proposed method employs several weighted least-squares minimizations and does not require any initial solution guesses to give the target position estimate. A detailed theoretical performance analysis associated with the method as well as the Cramer–Rao lower bound (CRLB) are provided. The proposed estimator is shown theoretically to achieve the CRLB accuracy under small Gaussian noise. Computer simulations are included to corroborate the theoretical development and to compare the estimator performance with several previous approaches. The proposed algorithm in the considered example is shown to outperform the existing closed-form estimators. In the 3-D and in the case of a minimum number of sensors (i.e., two transmitters and two receivers) in a MIMO radar, the proposed method is also shown to be able to localize a target with high accuracy while the previous closed-form methods cannot uniquely determine the target position. [ABSTRACT FROM AUTHOR]

The problem of detecting rank-one signals is considered in the presence of subspace interference and Gaussian noise with unknown covariance matrix. A generalized likelihood ratio test (GLRT) detector has already been designed for this detection problem. In this correspondence, we provide analytical performance analysis for the GLRT detector in the mismatched case where the nominal target steering vector is not aligned with the true one. All theoretical results are confirmed using Monte Carlo techniques. It is revealed that the detector bears a constant false alarm rate property against the noise covariance matrix. The expressions derived for the probabilities of false alarm and detection can greatly facilitate the threshold setting and performance evaluations. Numerical examples indicate that the GLRT is sensitive to steering vector mismatches. [ABSTRACT FROM AUTHOR]

The homodyned-K (HK) distribution is a three-parameter density function used to study mixed-species radar and ultrasound scattering phenomena. We analyze the distribution that results from corrupting the HK with additive Gaussian noise (HKN). This modification enables analytic study of the HK in the presence of system thermal noise in radar and ultrasound systems. The moments of intensity of the HKN are derived and a new moment method parameter estimation algorithm is tested in simulation. [ABSTRACT FROM AUTHOR]

In this paper, we consider a pulse radar and study the tradeoff between integration time and scan rate for diverse target scattering models. At the design stage, we optimize the available degrees of freedom (namely, pulse train length and detection threshold) so as to maximize the detection rate, defined as the average number of detections from the target per unit of time, subject to a constraint on the false alarm rate, which is the average number of false alarms from the monitored area per unit of time. This objective function allows to carefully balance the contrasting needs for a large probability of detection (achievable through a large dwell time) and a short scan time. Closed-form solutions are provided for Swerling's Cases 1 and 3 target fluctuation and for the Marcum nonfluctuating model, while, for the Swerling's Cases 2 and 4, the solution is found with the aid of computer simulation. A thorough performance analysis is given to show the achievable tradeoffs among the principal system parameters under the different target models. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of detecting a multichannel signal embedded in subspace interference and Gaussian noise. The interference lies in a known subspace but with unknown coordinates, while the noise, in the general sense, consisting of thermal noise and clutter, has an unknown covariance matrix. To estimate the covariance matrix, it is assumed that there are sufficient signal-free training data. Two cases are considered, namely, the homogeneous environment (HE) and partially HE (PHE). The test and training data in the HE have the same noise covariance matrix, while the test and training data in the PHE share the same noise covariance matrix up to an unknown scaling factor. We derive the corresponding Wald tests both in the HE and PHE. Remarkably, these two detectors reject the clutter by using the sample covariance matrix, and then reject interference and integrate signal simultaneously through the oblique projection. Numerical examples show that the two proposed detectors can provide better detection performance than existing detectors in some situations. [ABSTRACT FROM AUTHOR]

This paper handles the problem of optimizing the scanning policy of a surveillance system in the presence of Rayleigh targets in a mixture of K-distributed clutter and Gaussian noise. Adopting the detection rate (DR) and false alarm rate (FAR) as performance measures, we show that the said optimization amounts to solving a constrained DR maximization, wherein the free parameters are the transmit beamwidth, the dwell time, and the detection threshold, whereas the constraints concern the maximum FAR and time on target. A thorough performance assessment is offered, aimed at eliciting the influence of the signal-to-noise and clutter-to-noise ratios, the clutter shape parameter, and the rate of change of the target response. The results indicate that, although a uniformly optimum transmit policy does not exist, some trends can be devised, which appears of particular relevance especially for systems that can avail themselves of some degree of cognition. [ABSTRACT FROM AUTHOR]

Parametric estimation of polynomial phase signals (PPSs) corrupted by an impulsive noise has been considered. The interpolation in joint-variable domain has been combined with the hybrid cubic phase function—high-order ambiguity function-based estimator to address this issue. Technique for estimating the percentage of impulses and threshold selection is also proposed. In addition, we propose the refinement procedure to further decrease the mean squared error of PPS parameters estimates. [ABSTRACT FROM AUTHOR]

This paper derives equations to bound missed-detection and false-alarm probabilities for a quadratic monitor subject to vector noise with an uncertain, non-Gaussian probability density function (PDF). The bounding equation offers utility for the verification of safety-of-life navigation systems, in terms of both robustness and computational efficiency. Robustness is achieved in that the noise PDF need not be known precisely, so long as the PDF can be appropriately bracketed. Computational efficiency is achieved by transforming non-Gaussian PDFs into a Gaussian form that contributes to fast, repeated evaluations across a great many fault scenarios. [ABSTRACT FROM AUTHOR]

Recent changes in U.S. policy call for spectrum sharing between radar and wireless communications. In order to inform choices of protection criteria for a radar sharing spectrum with communications systems, this paper models communications interference and its effects on a cell-averaging adaptive-threshold radar detector. First, we propose a statistical model for communications interference based on existing models and original simulations, and we find support for both Gaussian and non-Gaussian models, depending on the application. Then, we assess the impact of communications interference on radar detection. Our results suggest that when interference is not well modeled as white Gaussian noise, mean interference-to-noise ratio (INR) is not sufficient to characterize interference effects on radar, and additional interference characteristics such as kurtosis/impulsiveness and coherence time can significantly impact radar performance. We also find degradation in radar performance in relatively low-INR Gaussian interference (e.g., about $-6$ to -\text{2 dB} mean INR at the output of the matched filter). [ABSTRACT FROM AUTHOR]

This paper investigates the statistical performance of three adaptive detectors in the presence of subspace interference and Gaussian noise. The interference is deterministic and lies in a known subspace but with unknown coordinates, while the noise is Gaussian distributed with unknown covariance matrix. For performance evaluation, we consider a more general case, namely, the case of subspace signal mismatch, where the actual signal does not completely lie in the presumed signal subspace. We derive the exact statistical distributions of the detectors, and then obtain analytical expressions for probabilities of detection and false alarm. The theoretical study reveals that the interference and signal mismatch affects the detection performance through two angles, which are the angle between the interference subspace and actual signal, and the angle between the actual signal and presumed signal subspace after they are both projected onto the interference-orthogonalized subspace. Numerical examples are provided to verify the theoretical results. [ABSTRACT FROM AUTHOR]

In this paper we focus on deriving an approximate Cramer–Rao lower bound (CRLB) for the parameters of a multicomponent linear frequency modulated continuous wave (LFMCW) signal corrupted by complex additive white Gaussian noise. The approximation is necessary due to the discontinuities inherent in the mathematical model of the instantaneous phase of each LFMCW signal model. By comparing our approximate bound to a simulation of the maximum likelihood estimator (MLE) of the LFMCW parameters, we confirm our analysis. In general, the CRLB is a useful tool for feasibility studies or in evaluating the degree of suboptimality that non-MLE methods exhibit. For passive detection and estimation of LFMCW signals, the Generalized Likelihood Ratio Test and the associated MLE are difficult to implement in practice, primarily due to their large computational requirements. So, lower bounds on performance, such as those provided by the CRLB, are necessary to evaluate suboptimal methods that are more suited for practical implementations. [ABSTRACT FROM PUBLISHER]

In this paper, a novel predictive smooth variable structure filter (PSVSF) is presented for attitude synchronization during satellite formation flying (SFF). The strategy is proposed based on variable structure control concept and applies to any nonlinear system with modeling errors. Because the modeling errors in the PSVSF are not restricted to Gaussian noise only, the technique can deal with various kinds of uncertainties, parameter variations, or noises. It also has low computational burden; therefore, it is more general than other nonlinear estimation methods. Sufficient conditions to analyze the stability and characteristics of the PSVSF are presented. Simulations are performed to demonstrate the accuracy, robustness, and stability of the proposed PSVSF for the attitude synchronization estimation of the SFF system. [ABSTRACT FROM PUBLISHER]

We address the problem of adaptive radar detection of point-like targets in presence of Gaussian noise with unknown spectral properties, adopting a “second-order approach” to target modeling. In fact, the presence of a useful signal is modeled in terms of a rank-1 modification of the noise covariance matrix, similarly for the possible presence of a fictitious signal under the null hypothesis, aimed at increasing the selectivity of the detector. A set of homogeneous training data is assumed to be available. Results show that the proposed detectors can outperform natural competitors, especially assuming a limited number of training data. [ABSTRACT FROM PUBLISHER]

This paper investigates optimal sensor placement strategies for angle-of-arrival (AOA) localization in three-dimensional space. We adopt the A-optimality criterion to determine optimal sensor placements under the Gaussian measurement noise. A comprehensive analysis of optimal sensor-target geometries is provided with no restriction on the number of AOA sensors, sensor-target range, and noise variances. A resistor network analogy is also presented to enable quick determination of optimal sensor-target geometries. The analytical results are verified by extensive simulation studies. [ABSTRACT FROM PUBLISHER]

In this paper, improved signal processing techniques are developed for the analysis and classification of low probability of intercept (LPI) radar waveforms. The intercepted LPI radar signals are classified based on the type of pulse compression waveform. They are classified as linear frequency modulation, nonlinear linear frequency modulation, binary frequency shift keying, polyphase Barker, polyphase P1, P2, P3, P4, and Frank codes. The classification approach is based on the parameters measured from the preprocessed radar signal intercepted by electronic support (ES) or electronic intelligence (ELINT) system. First, signal embedded within the noise is estimated using Wigner Ville distribution to improve the signal-to-noise ratio (SNR). Next, features are extracted using the time-domain and frequency-domain techniques. Furthermore, parameters measured from the fractional Fourier transform are used for the classification. This type of techniques are required in various systems such as ES, electronic attack, radar emitter identification and multi input multi output (MIMO) radar applications. Extensive simulations are carried out with different LPI radar-modulated waveforms corrupted with additive white Gaussian noise of SNR up to –15 dB and impulse noise with 90% of noise density. The proposed algorithm outperforms the existing techniques of classification and can be used under strategic environment. [ABSTRACT FROM PUBLISHER]

In this paper, we derive a comprehensive forward model for the data collected by stripmap synthetic aperture radar (SAR) that is linear in the ground reflectivity parameters. It is also shown that if the noise model is additive, then the forward model fits into the linear statistical model framework, and the ground reflectivity parameters can be estimated by statistical methods. We derive the maximum likelihood (ML) estimates for the ground reflectivity parameters in the case of additive white Gaussian noise. Furthermore, we show that obtaining the ML estimates of the ground reflectivity requires two steps. The first step amounts to a cross-correlation of the data with a model of the data acquisition parameters, and it is shown that this step has essentially the same processing as the so-called convolution back-projection algorithm. The second step is a complete system inversion that is capable of mitigating the sidelobes of the spatially variant impulse responses remaining after the correlation processing. We also state the Cramer-Rao lower bound (CRLB) for the ML ground reflectivity estimates.We show that the CRLB is linked to the SAR system parameters, the flight path of the SAR sensor, and the image reconstruction grid.We demonstrate the ML image formation and the CRLB bound for synthetically generated data. [ABSTRACT FROM AUTHOR]

GNSS signals affected by additive channel noise are approximately Gaussian, but often the presence of interference may change that distribution.We propose a blind detection method based on the Giannakis-Tsatsanis Gaussianity test to evaluate the presence of narrowband and wideband non-Gaussian interference. This method, resorting to the sample fourth-order autocumulants, is compared to other Gaussianity tests. The asymptotic covariance matrix of the estimated autocumulant vector is analytically determined, thus reducing the complexity of the proposed technique. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem of detecting a multichannel signal in the presence of interference. The signal lies in a known subspace but with unknown coordinate. The interference is assumed to be orthogonal to the signal in the whitened space. We propose an effective detector, according to the criterion of the generalized likelihood ratio test (GLRT). The proposed GLRT has better detection performance than the corresponding Rao andWald tests. Remarkably, it is found that the GLRT in the presence of orthogonal interference has the same form as that in the absence of interference. However, the orthogonal interference does affect the detection performance of the detector. We derive closed-form expressions of the probabilities of detection and false alarm for the proposed detector, which are verified by Monte Carlo simulations. [ABSTRACT FROM AUTHOR]

In this paper, we propose to derive an approximate theoretical distribution under the null hypothesis of the low-rank adaptive normalized matched filter (LR-ANMF). This detector is used to detect a target when the disturbance is composed of a low-rank Gaussian contribution (called clutter) and an additive white Gaussian noise. In the LR-ANMF, the estimated covariance matrix is replaced by the estimated orthogonal projector onto the subspace clutter. The method to derive this distribution is based on perturbation analysis and assumes a steering vector far from the clutter and a large clutter-to-noise ratio. Simulations on a space?time adaptive processing example validate our theoretical result. The impact of both hypotheses is also studied. [ABSTRACT FROM PUBLISHER]

Automatic target recognition (ATR) of synthetic aperture radar (SAR) target chips is a difficult problem, complicated by the number of nuisance parameters typically present in SAR imagery. This also complicates performance analysis because fully sampling the space of nuisance parameters in an evaluation dataset is intractable. ATR algorithms that first quantize pixel intensity values have been shown to be effective for SAR ATR due to hypothetically reducing the sensitivity to these nuisance parameters. Here we study the performance of two such algorithms, multinomial pattern matching and quantized grayscale matching, and compare them with the traditional mean squared error (MSE) template matching based classification approach. Our approach is to approximate the decision statistic of each algorithm as a Gaussian random variable (RV) parameterized by the noise power, or alternatively signal-to-noise ratio (SNR). This allows the analytic prediction of algorithm performance when the noise process of test images differs from that of the dataset used to train each algorithm, without having to rely on costly empirical simulation. We verify our results in simulations utilizing the AFRL "Civilian Vehicle" dataset. [ABSTRACT FROM AUTHOR]

This paper proposes a method for chaotic radar signal (CRS) design with ultralow sidelobe performance. We first modulate the group delay (MGD) of a radar signal using a chaotic mapping sequence in the frequency domain, and then we transform the radar signal to its time domain form by inverse Fourier transformation. The designed CRS has ultralow sidelobe owing to its flat power spectrum density (PSD). Meanwhile, its ambiguity function is thumbtack, and the amplitudes of both in-phase and quadrature components are close to Gaussian noise. Both numerical simulation and experimental results demonstrate the ultralow sidelobe performance of the designed CRS. [ABSTRACT FROM PUBLISHER]

The interacting multiple model (IMM) estimator outperforms fixed model filters, e.g. the Kalman filter, in scenarios where the targets have periods of disparate behavior. Key to the good performance and low complexity is the mode mixing. Here we propose a systematic approach to mode mixing when the modes have states of different dimensions. The proposed approach is general and encompasses previously suggested solutions. Different mixing approaches are compared, and the proposed methodology is shown to perform very well. [ABSTRACT FROM PUBLISHER]

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]

Based on persymmetric structures in received signals, we consider the adaptive detection problem in colored Gaussian noise with unknown persymmetric covariance matrix in a multiple-input, multiple-output (MIMO) radar with spatially dispersed antennas. To this end, a set of secondary data for each transmit-receive pair is assumed to be available. A MIMO version of the persymmetric generalized likelihood ratio test (MIMO-PGLRT) detector is proposed. A closed-form expression for the probability of false alarm of this detector is derived. In addition, a MIMO version of the persymmetric sample matrix inversion (MIMO-PSMI) detector is also developed. Compared to the MIMO-PGLRT detector, MIMO-PSMI has a simpler form and is computationally more efficient. Numerical examples are provided to demonstrate that the proposed two detection algorithms can significantly alleviate the requirement of the amount of secondary data and allow for a noticeable improvement in detection performance. [ABSTRACT FROM AUTHOR]

The extended Kalman filter (EKF) and the unscented Kalman filter (UKF) are among the most popular estimation methods. The smooth variable structure filter (SVSF) is a relatively new sliding mode estimator. In an effort to use the accuracy of the EKF and the UKF and the robustness of the SVSF, the filters have been combined, resulting in two new estimation strategies, called the EK-SVSF and the UK-SVSF, respectively. The algorithms were validated by testing them on a well-known target tracking computer experiment. [ABSTRACT FROM AUTHOR]

The random hypersurface model (RHM) is introduced for estimating a shape approximation of an extended object in addition to its kinematic state. An RHM represents the spatial extent by means of randomly scaled versions of the shape boundary. In doing so, the shape parameters and the measurements are related via a measurement equation that serves as the basis for a Gaussian state estimator. Specific estimators are derived for elliptic and star-convex shapes. [ABSTRACT FROM PUBLISHER]

The uniformly most powerful (UMP) rank-order analog to the cell-averaging constant false alarm rate (CA-CFAR) detector is developed for both the single and multi-scan case. The detector is capable of false alarm control in arbitrary noise while achieving maximum detection probability for the case of a Swerling II target in Gaussian noise. Relationships with earlier tests are discussed. A divide-and-conquer method is developed that allows for rapid implementation of a large class of two-sample rank tests. [ABSTRACT FROM AUTHOR]

The problem of state space estimation of linear systems in an unknown non-Gaussian noise field is considered. A finite Gaussian mixture model (GMM) is used to model the non-Gaussian measurement noise with unknown statistics. A variational Bayesian expectation maximization (VBEM) algorithm is proposed to estimate the system states as well as the unknown parameters. In the variational Bayesian expectation (VBE) step, approximate inference is established to estimate the system state. The Gaussian mixture parameters are then updated in the variational Bayesian maximization (VBM) step. We also derive the true marginal posteriors to verify the performance of the proposed VBEM method. Computer simulations show that the proposed method has an improved estimation performance compared with other conventional approaches. [ABSTRACT FROM AUTHOR]

This research considers the linear-FM (LFM) of a noise radar waveform for resolving targets when channel noise and four popular radar sidelobe weighting functions are considered. By using large time-bandwidth products and systematically varying the phase scaling factor $\kappa$, results from the digital matched filter output provide evidence that the LFM noise waveform 1) has range-Doppler resolution similar to conventional chirp waveforms and 2) has a low probability of intercept (LPI) similar to random noise waveforms. We acquire the results using a computer-based simulation and verify the location of target peaks using the chirp waveform output for both stationary and moving target cases. [ABSTRACT FROM PUBLISHER]

The application of the superresolution multiple signal classification (MUSIC) algorithm is extended to overcome the Doppler frequency estimation limitations due to the Fourier transform- (FT-) based techniques for ultrawideband (UWB) random noise radar signals. Simulated and experimental results are compared for single and multiple moving targets within a range bin, for narrowband (200 MHz) and UWB (1 GHz) noise radar signals. The simulated percentage error in Doppler estimation is parameterized as a function of the velocity ratio of two targets in a given range bin. [ABSTRACT FROM PUBLISHER]

Erroneous measurement of solar array voltage and current degrades the performance of hill-climbing, maximum power point tracking (MPPT) systems. This degradation is observed as a reduced climbing rate, erroneous settling point, and/or random operating point excursions. The effect of measurement bias and noise on MPPT performance is analyzed. Tracking problems are then classified according to their cause, which allows for easier debugging of a faulty tracker. The effectiveness of several error-mitigating techniques is then studied, and recommendations are given accordingly. Conclusions of the analysis are then experimentally verified. [ABSTRACT FROM AUTHOR]

A novel hyperchaotic coding scheme and corresponding optimal selection method are proposed to obtain the hyper chaotic logistic phase code (HCLPC) signal, which exhibits great electronic counter-counter measure (ECCM) performance in the presence of digital radio frequency memory (DRFM) repeat jammers and netted radar systems. Similar to the random noise signal, the HCLPC signal has an ideal average ambiguity function and cross ambiguity function. However, the HCLPC signal can overcome the difficulties of reference signal storage and transmitter-receiver isolation for random signals. High range sidelobe would exist for finite length of transmit signal in practice. The Tikhonov regularization constrained total least squares algorithm (TRCTLSA) is first proposed to range sidelobe suppression of the HCLPC signal when data inaccuracy and white Gaussian noise is taken into account, and a global optimal solution to this algorithm is given. Simulation shows that it has a better sidelobe suppression effect than other ways. When Gaussian-noise variance is less than 0.01, the sidelobe level can decrease to the order of -55 dB, but the signal-to-noise ratio (SNR) loss is less than 0.05 dB. As a digital sidelobe suppression filter procedure, it can be widely used in noise reduction environments, image enhancements, and easily ported to sidelobe suppression applications of other signals as well. [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]

The extreme weakness of a global navigation satellite system (GNSS) signal makes it vulnerable to a wide variety of interfering signals, falling within the GNSS frequency bands. One of the main classes of these disturbing signals is represented by continuous wave interference (CWI), which include all signals that can be effectively represented as pure sinusoids. This paper deals with the performance of GNSS signal acquisition in the presence of CWI. An original model describing the impact of CWI on signal acquisition is proposed and the expressions of both false alarm and detection probabilities are mathematically formulated and validated by Monte Carlo simulations. The paper also investigates the role of the interference amplitude and frequency along with the impact of different system parameters on the detection and false alarm probabilities. [ABSTRACT FROM AUTHOR]

Code tracking is an important function of receivers for Global Positioning System (GPS) and other global navigation satellite systems (GNSS). This paper and its antecedent provide analytical expressions for performance of code-tracking loops using early-late discriminators, under small-error conditions. Expressions are provided for output signal-to-noise-plus-interference ratio (SNIR) and code-tracking error, for arbitrary signal spectra, and Gaussian noise and interference having arbitrary spectral shapes. This second paper addresses noncoherent early-late processing (NELP) for given receiver precorrelation bandwidth and given early-late spacing, comparing the results to results for coherent early-late processing (CELP) and to a lower bound (LB) on code-tracking error. Theoretical expressions are derived and compared, and numerical results are provided to examine the effect of different modulation designs and interference conditions. [ABSTRACT FROM AUTHOR]

Code tracking is an important function of receivers for Global Positioning System (GPS) and other global navigation satellite systems (GNSS). This paper and its sequel provide analytical expressions for performance of code-tracking loops using early-late discriminators, under small-error conditions. Expressions are provided for output signal-to-noise-plus-interference ratio (SNIR) and code-tracking error for arbitrary signal spectra, and Gaussian noise and interference having arbitrary spectral shapes. This first paper addresses coherent early-late processing (ELP) for given receiver precorrelation bandwidth and given early-late spacing, also providing a tight lower bound on code-tracking error independent of discriminator design. Theoretical expressions are derived, showing that code-tracking accuracy depends on more than merely signal-to-noise ratio and early-late spacing-the shape of signal and interference spectra are important, as is the receiver precorrelation bandwidth. [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]

A method for computing the PAM representation for weak continuous phase modulation (CPM) is presented and applied to the modulation defined in the enhanced flight termination system (EFTS) standard. The pulse-amplitude modulation (PAM) representation was used to formulate a reduced-complexity detector whose performance is within 0.7 dB of maximum likelihood detection and 5.6 dB better than limiter-discriminator detection in the additive white Gaussian noise (AWGN) environment. The complexity of the reduced-complexity detector is less than 25% that of the maximum likelihood detector but, unlike the limiter-discriminator detector, requires a carrier phase PLL. In the presence of phase noise, the reduced-complexity detector outperforms limiter-discriminator detection when the RMS frequency deviation due to phase noise is less than 10% of the bit rate. [ABSTRACT FROM AUTHOR]

In this paper we consider the problem of detecting range spread targets in the presence of Gaussian disturbance with unknown covariance matrix. To this end we resort to the modified generalized likelihood ratio test (MGLRT) and devise a robust detector, the orthogonal rejection MGLRT (OR-MGLRT), capable of deciding whether some observations contain a useful target or if they contain a signal belonging to the orthogonal complement of the useful subspace. Hence we investigate the possibility of exploiting the new algorithm as the second stage of a double threshold receiving structure, obtained cascading the MGLRT and the OR-MGLRT. At the analysis stage, we assess the performance of the new double threshold decision rule, both in the matched and mismatched signal cases, also in comparison with previously proposed receivers. The results show that the new test allows for a wide range of compromises between the detection and the rejection performance. [ABSTRACT FROM AUTHOR]