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The paper takes an in-depth analysis of the channel estimate model (CEM) in passive radar using the orthogonal frequency division multiplexing (OFDM) waveforms. The CEM has been intensively exploited in OFDM passive radar, where the channel estimates (CE) are obtained from the original digitized received signal (ODRS), and target detection is performed based on CEs. However, traditional CEM is derived neglecting the inter-carriers interference (ICI). The influence of the ICI on target detection has rarely been discussed previously. In fact, target with large power and Doppler frequency can induce strong ICI, which increases the noise floor, and thus imposes significant influence on the detection of the other targets, especially the weak targets. In this paper, we rederive the CEM taking the ICI into consideration, and obtain a new CEM. In the new CEM, a specific target has two components, i.e., the useful signal part, and ICI. We derive the useful signal to noise ratio (SNR) and ICI to noise ratio (ICINR) theoretically, and provide compact expressions. We reveal the relationship between the SNR and ICINR in the CE, and the SNR in the ODRS. Based on the theoretical derivation, the influence of ICI is analysed. We also discuss the elimination of the ICI. The influence of ICI can be eliminated by cancelling the target signal that induces the ICI from the CEM. A target signal cancellation method is developed based on the new CEM. Simulations demonstrate the effectiveness of the theoretical analysis of the CEM and the proposed cancellation method. [ABSTRACT FROM AUTHOR]

Time difference of arrival (TDOA) is essential in localization, communication, and navigation. Under ambient noise interference, the impulsive signal is transmitted over a long distance and reaches the sensor with a low signal-to-noise ratio (SNR). Aiming to achieve a precise time delay estimation in scenarios with low SNR, this paper extends the time-reassigned extracting transform (TRET) theory to TDOA and proposes a time difference rearrangement extracting transform (TDRT) algorithm. The implementation process of the proposed algorithm comprises three steps. Initially, a rough two-dimensional time delay estimation is obtained by calculating the partial derivative of the short-time cross-power spectrum with respect to the frequency variable. Secondly, a rearrangement operation separates the signal's time-frequency (TF) points from the noise. Thirdly, a refined TDOA estimation is obtained by inverse Fourier transforming TF points extracted from the time-delay energy ridge. Simulation results show that the TDRT algorithm is effective in time delay estimation. Furthermore, experimental results prove that the performance of the TDRT algorithm outperforms comparable algorithms in low SNR environments. [ABSTRACT FROM AUTHOR]

Real-time seismic signals are intertwined with different types of noises during the generation, acquisition, and transmission process. The enhanced data with high resolution assists to interpret and analyze records more accurately. In this paper, we propose a mathematical approach based on recently developed geometrical mode decomposition (GMD) and adaptive self-guided filter (ASGF) to attenuate noise from two-dimensional seismic data. GMD is first applied to decompose the 2D seismic data into a number of band-limited intrinsic mode functions. This algorithm is capable of separating the linear and non-linear seismic events into linear modes and optimizing the linear patterns within amplitude frequency modes. The noisy modes are selected and attenuated by an adaptive self-guided filter. The GMD method is experimentally verified with a strong theoretical background to address the directional features of the image. ASGF is an exceptional edge-preserving filter and hence the hybrid algorithm could utilize the advantages of both methods. Higher the signal-to-noise ratio, improving the resolution of the image and preserving the directional properties and edges of the seismic events are the paramount characteristics of the proposed model. The simulating results on both synthetic and real seismic data proved the technique is more promising compared to the existing methods. [ABSTRACT FROM AUTHOR]

This paper studies the causes and distribution of reconstruction errors in subtractive dither structure and provides solution to minimize these errors. We present the generation of reconstruction errors in two types of quantization, namely mid-riser and mid-tread, with their corresponding distributions. We then construct a 12-bit pipeline analog to digital converters (ADC) simulation model and use a 16-bit digital to analog converter (DAC) to convert the dither sequence into analog signals. We perform statistical analysis on the resulting data, verify the accuracy of the distribution, and find that reconstruction error only occurs when the dither signal accuracy surpasses that of the ADC. Additionally, we find that mid-tread quantization has the lowest probability of reconstruction error. Finally, we analyze the impact of reconstruction error on the signal to noise ratio (SNR) and spurious free dynamic range (SFDR) at various input frequencies. Our findings show that the reconstruction errors' effect on the SFDR and SNR of the input signal is less than 1 dB. [ABSTRACT FROM AUTHOR]

The paper presents a predictive filter based frequency estimation algorithm (PF-FE) for multiple complex sinusoids corrupted by additive white Gaussian noise. The proposed frequency estimator combines the noise suppression capability of an FIR filter with a least squares approach minimizing the sum of the squared errors of a prediction filter. The FIR filter coefficients are chosen such that the sinusoidal components are predicted with minimum distortion. Computer simulation results are given to contrast the performance of the proposed PF-FE method with six other state-of-the-art frequency-estimators and the Cramer-Rao lower bound. Furthermore, the paper provides a complexity analysis of the PF-FE method and compares it with those of the conventional MUltiple SIgnal Classification algorithm and some selected state-of-the-art methods from the literature. Performance of the proposed PF-FE method is validated with effective mean square error (EMSE) plots for model orders of three and five and under different observation lengths and signal-to-noise ratio values. Finally, the paper demonstrates how EMSE varies for the proposed method when the prediction filter length is increased from 20 to 75. After extensive simulations, it is observed that when filter length is larger than half the observation length the EMSE will start to degrade. [ABSTRACT FROM AUTHOR]

This paper treats the estimation of a flat fading Rayleigh channel with Jakes' Doppler spectrum model and slow fading variations. A common method is to use a Kalman filter (KF) based on an auto-regressive model of order p (AR(p)). The parameters of the AR model can be simply tuned by using the correlation matching (CM) criterion. However, the major drawback of this method is that high orders are required to approach the Bayesian Cramer–Rao lower bound. The choice of p together with the tuning of the model parameters is thus critical, and a tradeoff must be found between the numerical complexity and the performance. The reasonable tradeoff arising from setting p = 2 has received much attention in the literature. However, the methods proposed for tuning the model parameters are either based on an extensive grid-search analysis or experimental results, which limits their applicability. A general solution for any scenario is simply missing for p = 2 and this paper aims at filling this gap. We propose using a Minimization of Asymptotic Variance (MAV) criterion, for which a general closed-form formula has been derived for the optimal tuning of the model and the mean square error. This provides deeper insight into the behavior of the KF with respect to the channel state (Doppler frequency and signal to noise ratio). Moreover, the paper interprets the proposed solution, especially the dependence of the shape of the optimal AR(2) spectrum on the channel state. Analytic and numerical comparisons with first- and second-order algorithms in the literature are also performed. Simulation results show that the proposed AR(2)-MAV model performs better than the literature and similarly to AR(p)-CM models with p ≥ 15. [ABSTRACT FROM AUTHOR]

Electroencephalogram (EEG) signals contains a major role in examining the behavior of brain activity. Moreover, these signals are contaminated with artifacts, which may affect the EEG signal analysis. The EEG performs as the low spatial resolution in the unimodal analysis. Owing to the highly time-consumption procedure, the removal of artifact techniques cannot be applied in practical applications. In existing works, automatic artifact removal techniques are ineffective for eliminating the artifacts in the EEG signals. Thus, the core of this concept is to design a low complexity aware, intelligent artifacts removal model using deep learning. This paper attempts to introduce a 1-Dimensional-Convolutional Neural Network with a Weighted Deep Feature (1D-CNN-WDF), which acts as a filter to remove the unnecessary noise in EEG signals. The WDF extraction in 1D-CNN-WDF is developed by combining the Coyote Optimization Algorithm (COA), and Bird Swarm Algorithm (BSA) named Hybrid Bird-COA (HB-COA). The same hybrid algorithm is used for the tuning of model parameters in 1D-CNN-WDF. The analysis results confirm that the recommended method is more enriched than recently introduced methods when considering the improved Signal to Noise Ratio (SNR), less Mean Squared Error (MSE), and reduced computational complexity, which shows that the suggested method is attained the superior reconstruction quality. [ABSTRACT FROM AUTHOR]

Abstract Estimating the noise power spectral density (PSD) from the corrupted speech signal is an essential component for speech enhancement algorithms. In this paper, a novel noise PSD estimation algorithm based on minimum mean-square error (MMSE) is proposed. The noise PSD estimate is obtained by recursively smoothing the MMSE estimation of the current noise spectral power. For the noise spectral power estimation, a spectral weighting function is derived, which depends on the a priori signal-to-noise ratio (SNR). Since the speech spectral power is highly important for the a priori SNR estimate, this paper proposes an MMSE spectral power estimator incorporating speech presence uncertainty (SPU) for speech spectral power estimate to improve the a priori SNR estimate. Moreover, a bias correction factor is derived for speech spectral power estimation bias. Then, the estimated speech spectral power is used in "decision-directed" (DD) estimator of the a priori SNR to achieve fast noise tracking. Compared to three state-of-the-art approaches, i.e., minimum statistics (MS), MMSE-based approach, and speech presence probability (SPP)-based approach, it is clear from experimental results that the proposed algorithm exhibits more excellent noise tracking capability under various nonstationary noise environments and SNR conditions. When employed in a speech enhancement system, improved speech enhancement performances in terms of segmental SNR improvements (SSNR+) and perceptual evaluation of speech quality (PESQ) can be observed. [ABSTRACT FROM AUTHOR]

Abstract Studying high power amplifiers (HPAs) and phase noise problems for a multi-pair massive antenna relaying (MP-MAR) communication system is very important. These problems can cause distortions on the transmitted signals and rotate the signal constellation from symbol to another. This paper proposes a MP-MAR communication system with zero-forcing (ZF) relay beamforming under HPAs and phase noise problems. In this system, many transceivers can communicate with each other using multi-pair massive relay equipped with very large antenna arrays. This paper studies the hardware problems at the transmitters, the relay station and the receivers. We also analyze the instantaneous end-to-end signal-to-noise plus distortion ratios (SNDRs) and outage probability (OP) to measure the extent to which hardware problems can affect the system performance. This paper derives new closed-form expressions for the OP, accounting for hardware problems as in Theorem 1. We also propose a weighted least squares technique for the channel estimation to improve system performance. To facilitate comparison, OP expressions of the ideal MP-MAR system and the proposed MP-MAR system are provided. The analytical and simulation results reveal that the impact of hardware problems can reduce system performance, especially when the massive relay station is under HPAs and phase noise problems. [ABSTRACT FROM AUTHOR]

Abstract In this paper, selection combiner output (SCO) signal-to-noise ratio (SNR) cumulative distribution functions (cdfs) of multivariate correlated and identically distributed, and exponentially-correlated Nakagami- m fading environments for integer and non-integer fading parameter m are rigorously surveyed, and mathematically reviewed. Simulation results are obtained under two scenarios: (i) equal-channel-gain, and (ii) unequal-channel-gain. Numerical simulation, only under specific scenarios, appears to be effective to verify findings from different fading environments and different specific contributions. Schematic diagrams are employed to show (i) top-down view for progress on SCO cdfs of multivariate correlated Nakagami- m fading, and (ii) mathematical relations among existing results, from which knowledge gaps can systematically be identified. Cross-verification revealing mathematical links among the published results is shown in detail, so that unidentified special cases can be mathematically unified to their corresponding generalised cases. Useful applications of correlated fading environments are also proposed. Not just focusing on reviewing correlated fading contributions, this paper also serves as a comprehensive tutorial, consolidating key findings on this topic. [ABSTRACT FROM AUTHOR]

Abstract Image sequences allow visualizing dynamic systems and understanding their intrinsic characteristics. One first component of this dynamics is retrieved from the estimation of the velocity displayed on the sequence. Motion estimation has been extensively studied in the literature of image processing and computer vision. In this paper, we step beyond the traditional optical flow methods and address the problem of recovering the acceleration from the whole temporal sequence. This issue has been poorly investigated, even if this is of major importance for major data types, such as fluid flow images. Acceleration is defined as the space–time function resulting from the forces applied to the studied system. To estimate its value, we propose a variational approach where an energy function is designed to model both the motion and the acceleration fields. The contributions of the paper are twofold: first, we introduce a unified variational formulation of motion and acceleration under space–time constraints; second, we describe the minimization scheme, which allows retrieving the estimations, and provide the full information on the discretization schemes. Last, experiments illustrate the potentiality of the method on synthetic and real image sequences, visualizing fluid-like flows, where direct and precise calculation of acceleration is of primary importance. [ABSTRACT FROM AUTHOR]

Abstract In this paper a new autocorrelation method called half-length autocorrelation (HLA) is introduced. Unlike regular (biased or unbiased) autocorrelations, the exact statistical characteristics of the HLA can be calculated, which makes the analytical calculation of the variance of the frequency estimator possible. The HLA is also used in two different frequency estimation methods namely, Modified Covariance (MC) and Modified Pisarenko Harmonic Decomposition (MPHD). It is shown that the application of the HLA in these frequency estimators improves their performance, particularly in lower signal-to-noise ratios (SNR). The analytical calculation of the variance (mean square error) of the estimators is also provided in this paper. Simulation results are in agreement with the theoretical calculations. For the MPHD estimator using the HLA (MPHD-HLA), the estimated frequency is very close to the theoretical calculations for SNRs as low as −2 dB. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of noise reduction in non-stationary signals. The paper first describes a human physiology based time–frequency (TF) representation (HPTF) using Mel filterbanks. It is then used to improve a noise reduction algorithm that does not require any a priori information about the signal of interest and the noise. This algorithm is efficiently implemented using an original wavelet shrinkage method. The overall method results in an original TF denoising procedure that yields a denoised HPTF (DHPTF). From this representation, one can reconstruct a denoised time-domain signal and therefore define a new improved noise reduction algorithm, whose performance is evaluated and compared with other state-of-the-art methods. The performance assessment uses several criteria: (1) signal-to-noise-ratio (SNR), (2) segmental SNR (SSNR) and (3) mean square error (MSE). The results indicate an improvement of up to 4.72 dB with respect to (w.r.t.) SNR, 2.79 dB w.r.t. SSNR and 4.72 dB w.r.t. MSE for a speech database signals corrupted with four different noises. In addition, other applications such as EEG signal enhancement show promising results. [ABSTRACT FROM AUTHOR]

The cubic phase signal (CPS) can be determined by two physical quantities, the chirp rate and quadratic chirp rate. With this characteristic, this paper proposes the parameterized two-dimensional representation (PTDR), which can be seen as an extension of the chirp rate-quadratic chirp rate distribution (CRQCRD). The goal of the PTDR is to accurately estimate parameters of multicomponent CPSs under low signal-to-noise ratios (SNRs). The PTDR is simple and can be implemented by using the complex multiplication, fast Fourier transform (FFT) and nonuniform FFT (NUFFT). In this paper, the computational cost, resolution, cross term suppression and anti-noise performance are analyzed for the PTDR. With several synthetic examples, comparisons with other five algorithms are performed and demonstrate that the PTDR can achieve higher resolution, cross term suppression and anti-noise performance with a moderate computational cost. Finally, the PTDR is employed to reconstruct inverse synthetic aperture radar (ISAR) image of the fluctuating ship and the real radar data simulations validate the effectiveness of the PTDR-based ISAR imaging algorithm. [ABSTRACT FROM AUTHOR]

In radar specific emitter identification (SEI), various types of unintentional modulation on pulse (UMOP) are selected as the features for discriminating between different radars. Unintentional Phase Modulation on Pulse (UPMOP), a typical type of UMOP, can provide crucial information for identifying radars. In most radar SEI algorithms, sacrificing time efficiency for higher accuracy is a common trade-off. This paper proposes a method to solve this problem by combining denoised UPMOP sequences with an Attention-based Gated Recurrent Units (Attention-GRU) model, which showed an excellent performance. Firstly, the cause of UPMOP is analyzed and the phase observation model of radar emitter signals and mathematical model of UPMOP are given. Then, the least-squares method is used to eliminate the linear trend of the phase observation model and obtain a noised estimation of the UPMOP sequences. Thirdly, the uniform B-spline (UBS) curves are then used to fit the noised estimation, resulting in a denoised and refined UPMOP sequence. Finally, the Attention-GRU model is employed to extract features from the denoised UPMOP sequences to identify radar emitters automatically. Results from simulation and measured data experiments show that the overall recognition rate of the algorithm reaches over 93% and the algorithm has excellent performance, with high identification accuracy and relatively low time consumption, even in low signal-to-noise ratio (SNR) conditions. [ABSTRACT FROM AUTHOR]

This paper studies the capacity and error performance of a noncoherent index-modulated orthogonal frequency division multiplexing (OFDM-IM) system with binary coded subcarrier activation patterns. To that end, soft-decision (i.e., unquantized) and hard-decision (i.e., quantized) channels are considered, where the latter is equivalent to a binary asymmetric channel (BAC). By using maximum likelihood and maximum a posteriori criteria, crossover probabilities of the quantized channel are derived in order to characterize its capacity and probability of error in detecting IM signals. Capacity expressions are then given for both channels. Using the Blahut-Arimoto algorithm, an iterative algorithm is developed to find the optimal input distribution for the unquantized channel, while for the quantized channel, it is found by solving a nonlinear equation for its zero. It is shown that using a uniform input distribution instead of the optimal (nonuniform) one results in a negligible capacity loss, therefore binary codes with a uniform distribution of 1s and 0s are nearly optimal. Compared to the unquantized channel, it is shown that an approximate loss less than 2dB is incurred when quantization is used and this loss diminishes rapidly as signal-to-noise ratio (SNR) increases. In addition, it is demonstrated that the gap (in dB) between uncoded noncoherent OFDM-IM system and the capacity limit is generally wide, and the gap narrows when subcarrier clustering is considered. The findings in this paper can have theoretical and practical significance for understanding capacity limits and can provide some guidance for coding design in noncoherent OFDM-IM systems. [ABSTRACT FROM AUTHOR]

RGBW (RGB-White) CFAs (Color Filter Arrays) which contain white pixels as well as RGB pixels have been actively proposed, which is due to the higher SNR (Signal to Noise Ratio) characteristics of the white pixels. The higher SNR characteristics of the white pixels results in the reconstruction of color images with higher resolutions than those reconstructed with widely used RGB CFAs, especially in low illumination environments. Recently, we proposed a new RGBW demosaicking method which uses an RGBW CFA where the white pixels cover 75% of the CFA, and which reconstructs the color image from the RGBW pattern image by use of the colorization method. The use of a large number of white pixels, and the use of colorization as an interpolation method made it possible to reconstruct a color image with high SNR values. In this paper, we extend this method to reconstruct video sequences by extending the colorization method to the 3-D domain, i.e., we propose a 3-D color reconstruction method which takes an RGBW pattern video sequence as the input and produces a reconstructed color video sequence. We implement the colorization as a 3-D diffusion instead of solving an overall colorization matrix, since the computation is high for the 3-D domain. The 3-D diffusion flow stops at temporal edges, which prevents the false colorizing of moving objects. Furthermore, the 3-D diffusion flow also eliminates the incoherence between colors of neighboring frames, which eliminates the flickering artifact. One major difficulty in extending to the temporal axis is that the noise in the few color seeds can propagate throughout the 3-D domain resulting in many false colors. This is especially important in the case when the image/video is taken in low illumination, since the energy of the thermal noise becomes relatively large compared to the light energy making the RGBW pattern image/video very noisy. To prevent the propagation of false colors caused by the noise in the color seeds, we use the 3-D stacked white channels as a guidance for denoising, so that the denoising takes into account only the color seeds that belong to the same object region in the 3-D space. The stacked white channels also guide the 3-D diffusion throughout the 3-D space. Experimental results show that the image/video reconstructed by the proposed method has better visual quality than those reconstructed by conventional RGB/RGBW CFA based methods. Therefore, the proposed method can be used in surveillance camera applications to recover important information from noisy video sequences taken in very low illumination environment. [ABSTRACT FROM AUTHOR]

In radar systems, prolonging the integration time helps to improve the detection and estimation performance of weak target. However, in a long integration time, target's high-order motion is non-ignorable, which causes high-order range migration (RM) and Doppler frequency migration (DFM) problems. The RM and DFM defocus the integration result and lead to detection and estimation performance loss. In this paper, a new coherent integration method is proposed for maneuvering radar target with jerk motion. First, we define a new cross correlation function basing on non-uniformly resampling technique. By using this function, we eliminate the range migration (RM) and Doppler frequency migration (DFM) caused by target's velocity and acceleration. Then, matched filtering process is performed to compensate the phase error caused by target's jerk motion. Finally, the integration is achieved by summing signal data in different range frequency cells and performing a two-dimensional fast Fourier transform (FFT). The proposed method is applicable for low signal-to-noise ratio (SNR) environment. Compared with the GRFT algorithm, the proposed method has a close integration performance and a lower computational complexity. The effectiveness is validated by simulations and real-data processing results. [ABSTRACT FROM AUTHOR]

In this paper, we evaluate the mean square error (MSE) performance of empirical characteristic function (ECF) based signal level estimator in a binary communication system. By calculating Cramér-Rao lower bound (CRLB) we investigate the performance of the ECF based estimator in the presence of Laplace and Gaussian mixture noises. We have derived an analytic expression for the variance of the ECF based estimator which shows that it is asymptotically unbiased and consistent. Simulation and analytic results indicate that the ECF based level estimator outperforms the previously proposed estimators in some signal to noise ratio (SNR) regions when the observation noise distribution is unknown. [ABSTRACT FROM AUTHOR]

In this paper, a joint transmit-receive beamspace parallel factor (B-PARAFAC) method is proposed for angle estimation in bistatic MIMO radar. Combining with the forward-backward averaging technique, the beamspace transformation technique is firstly extended to the tensor case, which utilizes the multidimensional structure of the received data. Specially, the transmit beamspace (TB) and receive beamspace (RB) matrices are obtained through convex optimization, whose matrix structure can be properly designed to construct a real-valued tensor model. Furthermore, different from the traditional discrete Fourier transform (DFT) beamspace filters, the bandwidth and sidelobe level of the proposed beamspace filters can be flexibly controlled as required, which can greatly improve the mainlobe-to-sidelobe ratio (MSR). We prove that the constructed real-valued tensor after beamspace transformation still follows a PARAFAC model. Compared with the unitary PARAFAC (U-PARAFAC) method, the proposed B-PARAFAC method has more attractive performance especially in low signal-to-noise ratio (SNR) and requires less computation. Simulation results demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Deep learning based compressed sensing imaging algorithms usually adopt supervised learning mechanism, which generally require massive high-quality images paired with the measured data in the training process. However, clear images are difficult to obtain in some specific application scenarios. In view of this, we propose a self-attention powered multi-scale compressed sensing imaging network named BNN-SANet with the Bayesian self-supervised learning manner in this paper. BNN-SANet can automatically learn effective feature representation from the measured data and reconstruct high-quality images without large-scale training samples. In particular, the self-attention based Transformer employed in BNN-SANet is effective to exploit global and long-range dependencies. Multi-scale dual path design in BNN-SANet performs well in extracting the semantic information at different scales to further improve the CS reconstruction quality. Experimental results illustrate that the proposed approach is substantially superior to previous compressed sensing imaging methods in terms of Peak Signal to Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM) and visual metrics. [ABSTRACT FROM AUTHOR]

In some actual image compressive sensing (CS) systems, the sparsity of the original image signals is unknown on the sampling side. Resources on the sampling side are also limited. Therefore, it is a challenging task to implement adaptive rate allocation on the sampling side of a CS system. In this paper, a novel adaptive rate allocation block CS scheme for images based on a hybrid sparsity estimation model is proposed. First, a cosine similarity sparsity estimation model is constructed by directly modeling the measurement results, and on the reconstruction side, a thresholding cosine transform sparsity estimation model is constructed according to the initial reconstructed image. Then, the sparsity estimates generated by the above two models are fused according to their respective characteristics using a Bayesian probability fusion algorithm. The advantage of the proposed scheme is that it can significantly improve the accuracy of the final sparsity estimates while limiting the computational complexity on the sampling side, which makes it exceptionally suitable for wireless sensor network applications. On the reconstruction side, we propose an energy-based adaptive global reconstruction model, that can further reduce block artifacts caused by block-based CS. The experiment results show that, compared with previous state-of-the-art schemes, the proposed scheme can achieve a significant improvement in the peak signal-to-noise ratio at the same sampling rate. [ABSTRACT FROM AUTHOR]

In this paper, an upper bound expression for the Q -function approximation is proposed. This expression defines the class of approximations that are upper bounds under the particular condition derived in the paper. The proposed upper bound approximation of the Q -function and the approximations derived from it are applied in signal to quantization noise ratio (SQNR) calculation of variance-matched Gaussian source scalar quantization. The proposed Q -function approximation having a simple analytical form and being a parametric one is optimized in terms of its parameter with respect to relative error (RE) of approximation for the particular problem observed. Specifically, three different optimization methods are proposed, so that different Q -function approximations are provided in the paper. Moreover, the manner for expansion of the obtained results is provided in order to make our proposal applicable for facilitating any mathematical analysis involving Q -function calculation. The results indicate that the proposed Q -function approximations not only outperform the numerous previously reported approximations in terms of accuracy, but also provide the derivation of the reasonably accurate closed-form formula for SQNR of variance-matched scalar quantization for the Gaussian source, which is not achievable with the application of the previously reported Q -function approximations of similar analytical form complexity. The results presented in this paper are applicable to many signal processing and communication problems requiring Q -function calculation. [ABSTRACT FROM AUTHOR]

The parameter estimation problem from noisy signal measurements plays a key role in several practical applications in the array signal processing area ranging from telecommunications and radar to biomedical and acoustics. The performance of parameter estimation techniques is sensitive to the signal-to-noise ratio (SNR) and severely degrades in noisy scenarios. Classical denoising using SVD low-rank approximation and its tensor counterpart known as higher order SVD (HOSVD) have been widely applied as a preprocessing step to improve the SNR of the received signal. In this paper, we propose the tensor-based multiple denoising (MuDe) approach that successively applies spatial smoothing, denoising and reconstruction to the noisy data. By taking into account the knowledge of the model order and by exploiting subarrays created by the spatial smoothing, we can successively denoise the data by means of HOSVD-based and SVD-based low-rank approximation for tensor and matrix data, respectively. We show that our proposed approach significantly reduces the noise level, allowing a more accurate estimation of parameters compared to state-of-the-art matrix-based and tensor-based techniques without decreasing the sensor array aperture. [ABSTRACT FROM AUTHOR]

Abstract In this paper, we propose a beamspace-based method for nominal direction-of-arrival (DOA) and angular spread estimation of incoherently distributed (ID) sources using a uniform linear array (ULA). Firstly, with generalized array manifold of the ULA, we obtain the beamspace array manifold by performing beamspace transformation on the received vector of two overlapping subarrays, and further derive the beamspace shift invariance structure via designing appropriate beamforming matrix. Next, the total least squares approach is used to estimate the nominal DOAs of ID sources. Finally, with the DOA estimates, the corresponding angular spreads are obtained by means of the central moments of the angular distribution. The proposed method does not involve any spectral search and reduces the dimension of matrix operations, thus it is obviously more efficient than the traditional algorithms. Simulation results indicate that our proposed algorithm is comparable to the existing algorithms when the number of sensors is large. [ABSTRACT FROM AUTHOR]

Abstract This paper analyzes the effect of windowing and additive wideband noise on the estimates returned by the real-valued Taylor-based Weighted Least Squares (TWLS) algorithm when applied to noisy sine-wave with constant amplitude and phase. The Taylor's series of the signal time-varying phasor are truncated to the second order and the two-term or three-term Minimum Sidelobe Level (MSL) and Maximum Sidelobe Decay (MSD) windows are considered. Expressions for the estimation variances are derived that hold even when the sine-wave amplitude and phase exhibit slow and limited, but significant, time variations. Then they are compared with the unbiased Cramér–Rao Lower Bounds (CRLBs) for constant parameter sine-waves in order to analyze the estimator statistical efficiency. The accuracies of the derived expressions are verified through computer simulations and validated by experimental results. [ABSTRACT FROM AUTHOR]

Abstract Consider a scenario in a two-tier cellular network wherein all subscribers adjust their transmission-times to be synchronously received at the macro base-station (MBS). This transmission timing arrangement causes asynchronous reception of signals and inter-carrier interference (ICI) at the femto base-station (FBS). In this paper, an iterative interference suppression scheme for reducing the ICI at the FBS is presented. By assuming a number of macro-users (MUs) and a single femto-user (FU), the mean square error (MSE) cost-function is first defined, based on which, the closed-forms for 3 underlying parameters, i.e., precoder, equalizer and the automatic gain controller are then derived by proposing a partial derivative-based sub-optimization scheme. To counteract the interrelation of the aforementioned parameters, an iterative algorithm is further proposed which results in finding a local-optimum point of the cost-function. To demonstrate the successful interference suppression performance of the proposed scheme, a per-subcarrier interference power analysis is also derived. The simulation results demonstrate a reasonable convergence after 6 iterations. Moreover, for the proposed scheme, a close bit error rate (BER) performance to that of a system without any timing-induced interference at low and medium SNRs (i.e., <20 dB) and a comparable performance at higher SNRs (i.e., 20 dB & <30 dB) are observed. [ABSTRACT FROM AUTHOR]

Abstract The one-bit compressive sampling (CS) framework aims at alleviating the quantization burden on analog-to-digital converters by quantizing each sample to one bit, i.e., capturing just the signs of samples. Motivated by one-bit CS theory, this paper addresses a new type of data acquisition system to recover spectrally sparse signals. This system is composed of a random demodulator and a one-bit quantizer. The former yields the signal compressed samples while the latter records the sign of each sample. With the observation sign data, the signal is eventually recovered by using the binary iterative hard thresholding algorithm. Through numerical experiments, we demonstrate that our scheme is high-efficient for spectrally sparse signal recovery in the situations of low signal-to-noise ratio, stringent bit budget and weak sparsity. [ABSTRACT FROM AUTHOR]

In this paper, we propose a fractional pilot reuse (FPR) scheme to mitigate pilot contamination in massive multiple-input multiple-output (MIMO) system. In FPR, users are classified into the cell-center and the cell-edge ones according to their signal-to-interference-plus-noise ratios (SINRs) at the receiver side. Then, the cell-edge users with low SINRs in different cells use different pilots or orthogonal pilots to avoid strong interference from adjacent cells while the cell-center users with high SINRs in different cells reuse the same pilots to improve the pilot efficiency. In order to identify the cell-center users and the cell-edge ones, we first approximate the SINR as a function of the distance between the user and the base station (BS) and then find the relationship between the network throughput and the distance threshold. The optimal distance threshold is found to maximize the network throughput. The analytical and simulation results demonstrate advantages of the proposed scheme compared with the conventional schemes. [ABSTRACT FROM AUTHOR]

We consider identifying the source position directly from the received source signals. This direct position determination (DPD) approach has been shown to be superior in terms of better estimation accuracy and improved robustness to low signal-to-noise ratios (SNRs) to the conventional two-step localization technique, where signal measurements are extracted first and the source position is then estimated from them. The localization of a wideband source such as a communication transmitter or a radar whose signal should be considered deterministic is investigated in this paper. Both passive and active localization scenarios, which correspond to the source signal waveform being unknown and being known respectively, are studied. In both cases, the source signal received at each receiver is partitioned into multiple non-overlapping short-time signal segments for the DPD task. This paper proposes the use of coherent summation that takes into account the coherency among the short-time signals received at the same receiver. The study begins with deriving the Cramér–Rao lower bounds (CRLBs) of the source position under coherent summation-based and non-coherent summation-based DPDs. Interestingly, we show analytically that with coherent summation, the localization accuracy of the DPD improves as the time interval between two short-time signals increases. This paper also develops approximate maximum likelihood (ML) estimators for DPDs with coherent and non-coherent summations. The CRLB results and the performance of the proposed source position estimators are illustrated via simulations. [ABSTRACT FROM AUTHOR]

The majority of the existing adaptive beamforming methods utilize the interference-plus-noise covariance matrix (INCM) to prevent the signal of interest (SOI) from being suppressed. However, the removal of the SOI component will lead to a decrease in the performance at low input signal-to-noise ratio (SNR). In this paper, a novel interference sources covariance matrix (ISCM) diagonal loading method is proposed to improve the robustness of the beamformer. First of all, we employ the peaks of the Capon spectrum to calculate the corresponding nominal steering vectors (SVs), and then accurate SVs of the SOI and interferences are obtained by subspace projection. Instead of utilizing the orthogonality, the proposed method preserves the non-orthogonality between SVs to estimate an accurate signal-plus-interference sources covariance matrix (SISCM), and then obtains a reconstructed array covariance matrix (RACM). Furthermore, we conduct diagonal loading on ISCM and construct a modified array covariance matrix (MACM) to keep the interference-to-signal ratio (ISR) after loading at a proper large value. Simulation results demonstrate the robustness of the proposed method, and indicate that the proposed method not only has the low computational complexity but also performs well at both low and high input SNR. [ABSTRACT FROM AUTHOR]

Transparent relay is a frequently used solution to enhance signal coverage with low implementation cost. However, the nonlinear distortion caused by cascaded amplification in the relay systems degenerates the system performance, increases the analysis complexity, and brings challenges to the system-level optimization. This paper analytically studies the system-level optimization of symbol error rate (SER) performance in nonlinear OFDM relay systems, where both power amplifiers (PAs) in transmitter and transponder show clear nonlinearity. Firstly, the system SER is expressed as a function of receiver signal-to-noise ratio (SNR), and the latter is further simplified to a polynomial-based function of PA parameters and system operating points. Secondly, the asymptotic behavior and Hessian Matrix (HM) of the receiver SNR are analyzed, and it is found that the SNR has an upper bound when dual nonlinearity of the transmitter PA and transponder PA are considered. Thirdly, this paper derives the analytical expressions of the optimal PA operating points corresponding to the optimal SER performance for the first time. Finally, the expression of the SNR upper bound is presented, and the derived functions are found to have the merit of low complexity, which suits the application in transparent relay systems with limited computational resources. [ABSTRACT FROM AUTHOR]

The instantaneous frequency (IF) is a very important feature of nonstationary signals in numerous applications. The first overview of the concept and application of the IF estimators is presented in seminal papers by Boashash. Since then, a significant knowledge has been gained about the performance of the IF estimators. This knowledge has been used not only for development of various IF estimators but also for introduction of novel time–frequency (TF) representations. The IF estimation in environments characterized by low signal-to-noise (SNR) has achieved significant benefits from these theoretical developments. In this paper, we review some of the most important developments in the last two decades related to the concept of the IF, performance analysis of IF estimators, and development of IF estimators for low SNR environments. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of single-channel noise reduction in the short-time Fourier transform (STFT) domain. Many algorithms have been developed to solve this important problem, most of which generally assume that the STFT coefficients in different frequency bands are uncorrelated, so the noise reduction is achieved by applying a gain function to the STFT of the noisy speech in each frequency band. However, this assumption is not accurate and the STFT coefficients of speech signals between neighboring frequency bands are correlated in practice due to the use of small lengths of the fast Fourier transform (FFT) and overlap add/save techniques in implementation. This paper formulates the noise reduction problem by taking into account the interband correlation using the so-called bifrequency spectrum. Based on this formulation, a single-channel minimum variance distortionless response (MVDR) filter is derived, which is shown to be able to significantly improve the signal-to-noise ratio (SNR) and meanwhile maintain the desired speech not much distorted. Simulations are presented to justify the claimed merits of the developed MVDR filter. [ABSTRACT FROM AUTHOR]

Recently, deep neural networks (DNNs) were successfully introduced to the speech enhancement area. Conventional DNN-based algorithms generally produce over-smoothed output features which deteriorate the quality of the enhanced speech. In addition, their performance measures calculated in the linear frequency scale do not match the human auditory perception where the sensitivity follows the Mel-frequency scale. In this paper, we propose a novel objective function for DNN-based speech enhancement algorithm. In the proposed technique, a new objective function which consists of the Mel-scale weighted mean square error, and temporal and spectral variations similarities between the enhanced and clean speech is employed in the DNN training stage. The proposed objective function helps to compute the gradients based on a perceptually motivated non-linear frequency scale and alleviates the over-smoothness of the estimated speech. In the experiments, the performance of the proposed algorithm was compared to the conventional DNN-based speech enhancement algorithm in matched and mismatched noise conditions. From the experimental results, we can see that the proposed algorithm performs better than the conventional algorithm in terms of both the objective and subjective measures. [ABSTRACT FROM AUTHOR]

Several papers have been published recently on the quasi-maximum likelihood (QML) approach for the polynomial phase signals (PPS) parameter estimation. The QML is powerful alternative to phase differentiation (PD) based techniques. It achieves the Cramer–Rao lower bound (CRLB) for low SNR values even for high-order PPS. The QML is generalized for signals with non-polynomial modulation and can work for impulsive noise environment. Since it is based on the linear time–frequency (TF) representation, short-time Fourier transform (STFT), it is suitable for estimation of multicomponent (mc) PPS. The goal of this review article is to summarize all developments of this tool. The QML versions for both one-dimensional (1-D) and two-dimensional (2-D) signals, for known and unknown order of phase polynomial, and for non-polynomial frequency modulated (FM) signals, are presented. In addition, estimation of mc and aliased PPSs sampled bellow the sampling theorem rate are also considered as well as application of the random sample consensus (RANSAC) algorithm for further reduction of the signal-to-noise ratio (SNR) threshold. Generalization of the QML approach to the direction-of-arrival (DOA) of the PPS signals impinging on the sensor array network with some open issues in the QML theory and applications are also addressed. Several examples demonstrate accuracy and advantages of the reviewed techniques. [ABSTRACT FROM AUTHOR]

The aim of this paper is to design an algebraic and robust fractional order differentiator to estimate both the Riemann–Liouville and the Caputo fractional derivatives with an arbitrary order of an unknown signal in noisy environment, without knowing the model defining the signal. For this purpose, a new class of fractional order Jacobi orthonormal functions is firstly introduced. Secondly, the truncated fractional order Jacobi orthonormal series expansion is applied to filter the noisy signal, whose fractional derivative is used to estimate the desired one. Thus, the obtained differentiator is exactly given by an integral formula which depends on a set of design parameters. Thirdly, by applying the generalized Taylor's formula, some error analysis is provided. In particular, error bounds are given, which permit to study the design parameters' influence. Fourthly, a digital fractional order differentiator is deduced in discrete noisy case. Finally, by comparing with two existing fractional order differentiators, numerical results are given to illustrate the accuracy and the robustness of the proposed fractional order differentiator. [ABSTRACT FROM AUTHOR]

In financial applications, it is common practice to fit return series by AutoRegressive Moving-Average (ARMA) models with Generalized AutoRegressive Conditional Heteroscedastic (GARCH) errors. In this paper, we develop a complex-valued ARMA-GARCH model for the sea clutter modeling application. Compared with the AR-GARCH model, the additionally introduced MA terms make the proposed model capable of considering the dependence of conditional variances of adjacent echo measurements as model coefficients, improving the modeling precision by taking advantage of the strong correlations between adjacent measurements. Based on the complex-valued ARMA-GARCH process for sea clutter modeling, we further develop a sea surface target detection algorithm. By analyzing a large number of the practical sea clutter data, we evaluate its performance and show that the proposed sea surface target detector offers a noticeable improvement for the probability of detection, comparing with the state-of-the-art AR-GARCH detector. [ABSTRACT FROM AUTHOR]

In this paper, an automatic adaptive method for identification and separation of the useful information content, from the background noise of time–frequency distributions (TFD) of multicomponent nonstationary signals, is presented. The method is based on an initial segmentation of the TFD information content by the K-means clustering algorithm, that partitions the initial data set in order to obtain K classes containing elements with similar amplitudes. It is shown that the local Rényi entropy (LRE) can accurately distinguish classes containing noise from classes with the useful information content, as a consequence of their basic structural differences in the time–frequency plane. Simulations are run to compare the performance of the proposed adaptive algorithm for blind separation of useful information from background noise (i.e. blind amplitude threshold) and non-adaptive (hard) amplitude TFD threshold procedures. Simulation results indicate that the proposed method performs better or closely to the best of five blindly chosen hard thresholds. The limitation of efficient hard-thresholding is the need of previous knowledge of the signal's structure and SNR or visual evaluation. [ABSTRACT FROM AUTHOR]

With a long synthetic aperture time, the energy of the ground moving target will be defocused due to the effect of range migration (RM) and Doppler frequency migration (DFM). This paper combines Radon transform and the modified Lv's distribution (MLVD), then proposes RMLVD to eliminate RM and estimate the motion parameters of target. It is validated that the proposed method can estimate the equivalent slant acceleration and jerk simultaneously. Besides, the proposed method can realize coherent integration with a low computational load and eliminate the Doppler ambiguity effect. The simulation results show that the proposed method has an excellent detection performance in the multiple targets or low signal-to-noise ratio (SNR) environment. [ABSTRACT FROM AUTHOR]

The time delay estimation (TDE) is an important and fundamental part in wireless communication signal processing, which has been widely used in localization, intelligent navigation, and radio monitoring. However, it remains a challenging task to support such TDE mechanisms with super-resolution in impulsive noise and multipath environments. Aiming at improving the TDE performance in both impulsive noise and multipath environments, in this paper, a novel super-resolution TDE method is proposed by using the defined exponential kernel correlation (EKC) function. In the proposed TDE method, EKC function is defined to suppress the non-Gaussian impulsive noise. Specifically, the theoretical analysis is also given to prove its boundedness and its effectiveness in non-Gaussian impulsive noise suppression. Further, a novel super-resolution TDE method is proposed by combining EKC function and multiple signal classification (MUSIC) method, which can significantly improve the estimation performance in terms of resolution and accuracy with both non-Gaussian impulsive noise and multipath environments. Finally, simulation experiments demonstrate that the proposed super-resolution TDE method maintains good performance even with a low characteristic exponent and low generalized signal-to-noise ratio (GSNR). [ABSTRACT FROM AUTHOR]

Code-division multiplexing (CDM) has become the focus of designing orthogonal waveforms of multiple-input multiple-output (MIMO) radars as it occupies fewer resources than time-division multiplexing (TDM) and frequency-division multiplexing (FDM). As one of the standard CDM waveforms, conventional inter-pulse phase-coded waveform possesses non-ideal diversity performance. That is, the orthogonality of the signals will reach the threshold after the codes extend to a certain length. To circumvent the coding restrictions, space-time coding (STC) makes use of spatial resources. This paper proposes a multi-frame STC method to improve the orthogonality among waveforms. Specifically, we first design the orthogonal transmitted signals and corresponding matched filters for MIMO radars. Then the signal expressions and the radar ambiguity function (AF) are deduced to verify the proposed method theoretically. Compared with conventional STC, such a method can save half of the time domain resources and make the most of coding resources. Furthermore, the output signal-to-noise ratio (SNR) is well improved as the proposed method effectively suppresses the cross-correlation energy of the waveform. The proposed multi-frame STC waveform can be easily implemented in practical radar applications because of its constant waveform amplitude. The theoretical analysis and simulation results demonstrate the effectiveness of the method. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, a digital filter bank structure is proposed for the reconstruction of uniformly sampled bandlimited signals from their N-th order nonuniform samples. The proposed filter bank structure is arrived at after incorporating polyphase-domain filtering operations and discrete Fourier transform (DFT) modulation to an existing filter bank framework. In this paper, an idea is also presented, so that uniform samples can be reconstructed from N-th order nonuniform samples using the structures based on recurrent nonuniform sampling. A comparison of the computational complexity and the signal-to-noise ratio (SNR) performance is also given for various structures existing in the literature. [Copyright &y& Elsevier]

Abstract: The aim of this paper is the presentation of the designing of asymptotic unrestricted polar quantizers with square cells, since it is known that square cells give minimal moment of inertia, which leads to minimization of distortion. Until now, polar quantizers with square cells have been designed only for the optimal companding function and their performances have been analyzed only for the stationary variance. In this paper, the design is done in a general manner, i.e. it is valid for any companding function and performances are analyzed in the wide range of variances. After that, this general design is applied for the logarithmic μ-law companding function. It is important that expressions for the numbers of magnitude and phase levels are obtained in the closed form, which simplifies the design. It is shown that the proposed polar quantizer has better performances (more than 3 dB higher the maximal and the average SQNR (signal-to-quantization noise ratio)) than the corresponding scalar quantizer with μ-law. Simulation is performed, and it is shown that theoretical and simulation results are matched very well. It is shown that the proposed polar quantizer can be used both for stationary and non-stationary signals, choosing the appropriate value of μ. This quantizer can be widely used, for all signals with Gaussian distribution. [Copyright &y& Elsevier]

Abstract: This paper proposes fast variants of the discrete S-transform (FDST) algorithm to accurately extract the time localized spectral characteristics of nonstationary signals. Novel frequency partitioning schemes along with band pass filtering are proposed to reduce the computational cost of S-transform significantly. A generalized window function is introduced to improve the energy concentration of the time–frequency (TF) distribution. An application of the proposed algorithms is extended for detection and classification of various nonstationary power quality (PQ) disturbances. The relevant features required for classification were extracted from the time–frequency distribution of the nonstationary power signal patterns. An automated decision tree (DT) construction algorithm was employed to select optimal set of features based on a specified optimality criterion for extraction of the decision rules. The set of decision rules thus obtained were used for identification of the PQ disturbance types. Various single as well as simultaneous power signal disturbances were considered in this paper to prove the efficiency of proposed classification scheme. A comparison of the classification accuracies with techniques proposed earlier, clearly demonstrates the improved performance. The major contributions of this manuscript are new FDST algorithms for fast and accurate time–frequency representation and an efficient classification algorithm for identifying PQ disturbances. The advantages of the classification algorithm are (i) accurate feature derivation from the TF distribution and optimum feature selection by the DT construction algorithm, (ii) robust performance at different signal-to-noise ratios, (iii) simple decision rules for classification, and (iv) recognition of simultaneous PQ events. [Copyright &y& Elsevier]

Polarization diverse design of the transmit waveforms based on the property of the target scattering matrix provides better performance than transmitting waveforms with fixed polarizations, especially for mainlobe interference suppression applications. In this work, we propose a radar system combines the advantages of multiple-input–multiple-output (MIMO) systems with the advantages offered by optimally choosing the transmitter polarizations, in order to achieve a better mainlobe interference suppression performance. The polarization diversity is employed in the transmit array, while the receive array adopts 2-D vector sensors to measure the horizontal and vertical components of the received signal. The optimal transmitter polarizations can be obtained by maximizing the output signal-to-interference-plus-noise ratio (SINR) in each coherent processing interval (CPI). In addition, the discrimination method for the target and the interference is studied in this paper, along with the target scattering matrix estimation method. Simulation results demonstrate that interference can be effectively discriminated and suppressed with the radar configuration, and better interference suppression performance is achieved with the optimal transmitter polarizations. [ABSTRACT FROM AUTHOR]

Due to the advances on electronics, applications of antenna array signal processing are becoming more frequent. When employing antenna arrays for beamforming, the signal to interference and noise ratio (SINR) should be assessed. Many factors can affect the SINR such as the array element positioning error and the direction of arrival (DOA) estimation error. In these cases, the assessment is traditionally performed via the SINR average obtained using Monte Carlo (MC) simulations. However, this approach requires a great amount of realizations that demand a high computational effort and processing time due to its slow convergence. In this paper, we propose a low complexity performance assessment of the average SINR via unscented transformation. Compared to MC simulations, our proposed method requires only a few trials and has a negligible computational complexity, yet giving a comparable SINR when the DOA estimation is perturbed. When the antenna elements positioning is perturbed, a multivariate scenario arises. For multivariate scenario the proposed scheme has an exponential increase in complexity, therefore, still being advantageous for a small number of antennas. [ABSTRACT FROM AUTHOR]

The phase retrieval (PR) problem of recovering an image from its Fourier magnitudes is an important issue. Several PR algorithms have been proposed to address this problem. Recent efforts of exploiting sparsity were developed to improve the performance of PR algorithms, such as the reconstruction quality, robustness to noise, and convergence behavior. In this paper, we propose a novel sparsity-based algorithm, which can adaptively learn an orthogonal sparsifying transform, and reconstruct the image simultaneously from the Fourier magnitudes. However, the estimated images at the early iterations are extremely bad. Training samples from these images cannot provide much useful information for sparsifying transform learning. To avoid unnecessary updating, an orthogonal sparsifying transform learning method based on transfer learning is proposed. Through transfer learning, we transfer the fixed sparsifying transform to an adaptive one. We apply this new sparsifying transform learning method to PR, and exploit the alternating directions method of multipliers (ADMM) technique to solve the formulated problem. Since the learnt sparsifying transform is adaptive to data, it favors better sparsity. Using this learnt sparsifying transform for image reconstruction can improve the reconstruction quality at low oversampling ratios. Experimental results show that the proposed PR algorithm can improve nearly 6 dB compared with the recently proposed PR-TIHP- l 1 algorithm in terms of the average PSNR (Peak Signal to Noise Ratio) at oversampling ratio 2.47, 2.53, 2.59. Moreover, our algorithm is robust to noise and has better convergence behavior heuristically. [ABSTRACT FROM AUTHOR]

This paper presents an acoustic local positioning system (ALPS) suitable for indoor positioning of portable devices such as smartphones or tablets, based on the transmission of high frequency CDMA-coded signals from a fixed network of beacons. The main novelty of the proposed ALPS is its capability to mitigate the effects of multipath propagation by performing an accurate estimation of the Line-of-Sight Time-of-Flights (LOS-TOF) through the Matching Pursuit algorithm. Signal detection, multipath cancellation and positioning estimation all take place within the portable device, which provides a graphical representation of the updated position in less than a second. The performance of the Matching Pursuit algorithm is analyzed in a real scenario and the results show that the proposed method is capable to retrieve the multipath-free System Availability under strong multipath conditions with SNR levels as low as 0 dB. [ABSTRACT FROM AUTHOR]

Linearly frequency modulation (LFM) pulse train and linearly stepped frequency (LSF) pulse train are mostly used in radar systems. However, the estimation performance of target motion parameters may be affected by the high recurrent lobe levels and the range–Doppler coupling phenomenon appearing in ambiguity function (AF). In multi-target scenarios, the estimation performance becomes even worse. The Costas frequency-modulation coded (CFMC) LFM pulse train has an ideal thumbtack-shaped AF, thus it can provide motion parameter estimation with high precision. However, the estimation of target motion parameter for the coherent CFMC LFM pulse train has not been investigated in depth. In this paper, we first analyze the properties of the AF of the CFMC LFM pulse train. Based on its convexity and narrow mainlobe width, a fast method to implement the maximum likelihood estimator (MLE) is proposed to estimate the motion parameters. To reduce the computation complexity, the Chirp-Z Transform (CZT) is introduced. Then, the Cramer–Rao lower bounds (CRLBs) on range, velocity and acceleration for frequency-modulation coded (FMC) pulse train are derived. It is shown that the CRLBs are relevant to the frequency coding patterns. Finally, Monte Carlo simulations are performed to verify the performance of the MLE. The results show that the performance of our proposed method can achieve the CRLBs when the signal-noise ratio (SNR) is higher than the threshold SNR. [ABSTRACT FROM AUTHOR]