Amplitude And Phase Estimation Research Articles

DOA Signal Identification Based on Amplitude and Phase Estimation for Subarray MIMO Radar Applications

The overlapped equal subarray transmit radar, which is also known as the Subarray Multiple-Input Multiple-Output radar, utilizes the key advantages simultaneously of both types of multi-antenna radar, i.e. the phased array and MIMO radars, so that it is able to detect multiple targets even though it has a radar cross section (RCS) of a weak or small target. In this paper, it is proposed to develop a parameter estimation approach called amplitude and phase estimation (APES). This approach provides improved resolution to the estimation of the amplitude and direction of arrival (DoA) of the target reflection signal on the radar compared to the existing conventional estimation methods such as least squares (LS). The formulation of the APES method on this radar is based on the tested parameters such as DoA and RCS and continuously being evaluated. The results show that the performance of the APES method of this radar can detect targets very precisely when the number of subarrays (M) is greater than the number of detection targets (P), precisely M > P. For the results of DoA and RCS accuracy from the APES method, this radar is more accurate than the LS when testing the angular resolution between the two targets, an angle resolution of 2° is obtained for the APES method which is superior to the LS with an angle resolution of 5.8°. In these conditions, the APES method is able to accurately distinguish between two targets while the LS method is only able to detect one target.

Fast Displacement Estimation of Multiple Close Targets with MIMO Radar and MUSICAPES Method

Interferometric radar is a hot research topic in manmade target displacement measuring applications, as it features high precision, a large operation range, and a remote multiple point measuring ability. Most one-dimensional interferometric radars use single-input single-output (SISO) radar architecture to achieve a high repetition measuring rate of more than 200 Hz; however, it cannot resolve multiple targets with the same radial range but different azimuth angles. This paper presents a multiple-input multiple-output (MIMO) radar that adopts a limited number of antennas (usually tens) to simultaneously improve azimuth resolution and achieve a high repetition measuring rate. A MUSICAPES algorithm is proposed, which is cascades the multiple signal classification (MUSIC) algorithm and the amplitude and phase estimation (APES) filter. The MUSIC algorithm is used to further improve the angular resolution of the small array. The APES is used to precisely recover the phases of the multiple close targets by suppressing their mutual interferences. Simulations and experiments with a millimeter-wave radar validate the performance of the proposed method.

MIMO-OFDM Joint Radar-Communications: Is ICI Friend or Foe?

Intercarrier interference (ICI) poses a significant challenge for OFDM joint radar-communications (JRC) systems in high-mobility scenarios. In this paper, we propose a novel ICI-aware sensing algorithm for MIMO-OFDM JRC systems to detect the presence of multiple targets and estimate their delay-Doppler-angle parameters. First, leveraging the observation that spatial covariance matrix is independent of target delays and Dopplers, we perform angle estimation via the MUSIC algorithm. For each estimated angle, we next formulate the radar delay-Doppler estimation as a joint carrier frequency offset (CFO) and channel estimation problem via an APES (amplitude and phase estimation) spatial filtering approach by transforming the delay-Doppler parameterized radar channel into an unstructured form. To account for the presence of multiple targets at a given angle, we devise an iterative interference cancellation based orthogonal matching pursuit (OMP) procedure, where at each iteration the generalized likelihood ratio test (GLRT) detector is employed to form decision statistics, providing as by-products the maximum likelihood estimates (MLEs) of radar channels and CFOs. In the final step, target detection is performed in delay-Doppler domain using target-specific, ICI-decontaminated channel estimates over time and frequency, where CFO estimates are utilized to resolve Doppler ambiguities, thereby turning ICI from foe to friend. The proposed algorithm can further exploit the ICI effect to introduce an additional dimension (namely, CFO) for target resolvability, which enables resolving targets located at the same delay-Doppler-angle cell. Simulation results illustrate the ICI exploitation capability of the proposed approach and showcase its superior detection and estimation performance in high-mobility scenarios over conventional methods.

Backpropagation Neural Network Algorithm-Based Color Doppler Ultrasound Detection of Gestational Diabetes Mellitus and Perinatal Outcomes

In this work, the related risk factors and perinatal outcomes of pregnant women with gestational diabetes mellitus (GDM) were analyzed based on color Doppler ultrasound (CDU) diagnosis. Backpropagation (BP) algorithm-based CDU imaging algorithm (BPC) was constructed in this study and applied in CDU images of 80 pregnant women with GDM. Besides, amplitude and phase estimation (APES) and low-complexity adaptive beam (LCA) algorithms were introduced for comparison with BPC in turn. It was found that Dice similarity coefficient (96.44%), sensitivity (95.45%), and specificity (91.56%) of BPC were greater than those of APES (83.97%, 85.84%, 78.45%) and LCA (84.74%, 86.29%, 82.35%), while its running time (6.44 ± 1.39 s) was shorter than that of APES (11.87 ± 2.41 s) and LCA (13.76 ± 1.54 s) ( P < 0.05 ). Pregnant women in the experimental group (group B) were older than those in the control group (group A) ( P < 0.05 ). The pulsatility index (PI) and renal artery resistance index (RI) of fetuses in group B (0.95 ± 0.15) were higher than those of group A (0.57 ± 0.24) ( P < 0.05 ). In addition, pregnancy age, family history of hypertension, and abortion history were positively correlated with GDM ( P < 0.05 ). In conclusion, BPC could not only improve diagnosis accuracy in fetuses’ CDU images but also shorten calculation time. Pregnancy age, family history of hypertension, and abortion history were the related risk factors for GDM in pregnant women.

Research on Continuous Signal Mask Separation in Satellite Communication

With the rapid development of communication technology, satellite communication has been very popular in our life. However, due to the openness of satellite communication channel, the communication security has been a hot topic. The large signal masking technology is a potential solution to improve the safety of satellite communications in recent years. To achieve this technique, an efficient and reliable signal separation method must be supplemented at the receiver. Based on existing methods, there are problems of low accuracy of parameter estimation and complex estimation process. The improved scheme introduces the Amplitude and Phase Estimation(APES) based parameter estimation procedure, and proposes a new scheme consisting of APES algorithm, Costas loop and Least Mean Square (LMS) adaptive filter. Compared with existing methods, the simulation results show that the proposed method can achieve mask signal separation successfully, and can achieve better separation performance.

Adaptive Spectral Doppler Estimation Based on the Modified Amplitude Spectrum Capon.

In conventional ultrasound systems, the compromise between frequency and temporal resolution limits the quality of the spectrograms and the ability to track fast blood flows. The main objective of this study was to identify a method that could reduce spectral broadening over time by reducing the observations and improving the spectral resolution and contrast. This problem is more pronounced in the process of imaging at higher blood velocities when using a short Doppler signal observation window (OW) in adaptive methods. The proposed adaptive technique, which is based on the covariance matrix Eigen space and the amplitude spectrum Capon (ASC) algorithm, managed to improve the spectral resolution and contrast compared with other adaptive algorithms within a shorter observation time, and it offered a narrower power spectrum and a more accurate spectrogram over time in combination with a coherence-based post filter. All methods were tested through various simulations. First, an analysis was carried out by simulating the femoral artery flow and the time-independent parabolic flow using the Field II simulator. Then, the performance of the proposed method was evaluated under more realistic conditions using a computational fluid dynamics simulation of complex flow fields in a carotid bifurcation model. Afterward, in vivo clinical data on the hepatic vein were used to validate the proposed method. Finally, the accuracy of the velocity estimated by different methods was evaluated through a mean-square-error assessment. Not only could the proposed method show significant improvements using extreme small OWs, N=[2, 4] , in the simulated data in terms of frequency resolution and contrast, but it also managed to offer an improvement of 74%, 73.3%, 22.2%, and 50% in frequency resolution, and an increase of 96.5, 90.2, 49, and 31.5 dB in contrast using in vivo clinical data compared with the Capon, amplitude and phase estimation (APES), projection-based Capon, and projection-based APES, respectively, for the OW of N=4 .

Bridge Dynamic Cable-Tension Estimation with Interferometric Radar and APES-Based Time-Frequency Analysis

Dynamic cable-tension is an important bridge-health indicator. However, it is difficult to be measured precisely and efficiently. A remote bridge dynamic cable-tension measurement method is proposed. It uses an interferometric radar sensor, a time-frequency analysis technique, and a tension estimation approach based on a string-vibration-equation. One radar can measure the displacements of multiple cables aligned on one side of a bridge, at the same time. By solving the string vibration equation, each cable-tension is calculated from its fundamental frequency, which is obtained by time-frequency analyzing a short section of the cable’s whole displacement vector in an overlapped-piecewise manner. An adaptive amplitude and phase estimation (APES) algorithm is used to solve the frequency resolution deterioration problem due to the short duration. Simulations and field experiments with a K band interferometric radar validate that the proposed method is superior to traditional cable-tension measurements in terms of precision, robustness, and efficiency. The proposed method is of great application value in measuring and monitoring large cable-stayed bridges and cable-suspended bridges.

Persistent Scatterer Densification Through the Application of Capon- and APES-Based SAR Reprocessing Algorithms

Capon’s minimum-variance method (MVM) and amplitude and phase estimation (APES) spectral estimation algorithms can be applied to synthetic aperture radar (SAR) processing to improve the resolution and suppress sidelobe levels. In this paper, we use Capon-/APES-based SAR reprocessing algorithms to increase the persistent scatterer (PS) density in PS interferometry (PSI). We propose a PS candidate (PSC) selection algorithm applicable to the superresolution reprocessed images and the corresponding processing chain. The performance of the proposed algorithm is evaluated by a number of simulations and a stack of TerraSAR-X data. The results show that the Capon algorithm outperforms others in PSC selection. We present a full PSI time-series analysis on the PSCs extracted from the Capon-reprocessed stacks. The results show that the PS density is increased between 50% and 60%, while their interferometric quality is maintained.

Estimation and Modeling of Fluctuating Wind Amplitude and Phase Spectrum Using APES Algorithm Based on Field Monitored Data

The fluctuating wind power spectrum (FWPS) in given specifications could only represent the second-order probabilistic characteristic, which indicates that it is not capable of fully expressing the stochastic wind field. Estimation and modeling of the fluctuating wind amplitude spectrum (FWAS) as well as the fluctuating wind phase spectrum (FWPhS) by using measured wind velocity data can make up for the deficiencies mentioned above. A high-resolution nonparametric spectral estimation algorithm—amplitude and phase estimation (APES)—is used to estimate the FWAS and the FWPhS, using the field measured wind velocity data of a certain cable-stayed bridge in Shanghai, China. An empirical expression (eFWAS) is introduced by dimensional analysis to model the random FWAS, and its specific Davenport form is proposed according to field measured data. The parameters of the Davenport eFWAS model are estimated by using the above measured FWAS, and three specific applications of this model are put forward when different known conditions are met. Compared with the measured FWAS, the stochastic Davenport eFWAS model proposed in this paper can accurately describe the statistical properties of the local wind field and improve the modeling accuracy of the FWAS, which is important in antiwind structural design and safety assessment.

Low Complexity Moving Target Parameter Estimation for MIMO Radar Using 2D-FFT

In multiple-input multiple-output radar, to localize a target and estimate its reflection coefficient, a given cost function is usually optimized over a grid of points. The performance of such algorithms is directly affected by the grid resolution. Increasing the number of grid points enhances the resolution of the estimator but also increases its computational complexity exponentially. In this paper, two reduced complexity algorithms are derived based on Capon and amplitude and phase estimation (APES) to estimate the reflection coefficient, angular location, and Doppler shift of multiple moving targets. By exploiting the structure of the terms, the cost function is brought into a form that allows us to apply the two-dimensional fast Fourier transform (2D-FFT) and reduce the computational complexity of estimation. Using low resolution 2D-FFT, the proposed algorithm identifies suboptimal estimates and feeds them as initial points to the derived Newton gradient algorithm. In contrast to the grid-based search algorithms, the proposed algorithm can optimally estimate on- and off-the-grid targets in very low computational complexity. A new APES cost function with better estimation performance is also discussed. Generalized expressions of the Cramer–Rao lower bound are derived to assess the performance of the proposed algorithm.

Apodized adaptive beamformer.

A number of studies aimed at improvement of ultrasound image quality, such as spatial resolution and contrast, have been conducted. Apodization is known as an important factor that determines image quality. However, in the case of amplitude and phase estimation (APES) beamforming, a kind of adaptive beamformer that has been employed in medical ultrasound recently, only rectangular apodization has been used in the previous studies. In this study, apodization was employed in adaptive beamforming, and its effects on image quality were examined in phantom experiments. We recently proposed a modified APES beamformer that reduces the computational complexity significantly using sub-aperture beamforming. In this study, the total receiving aperture was divided into four sub-apertures, and the APES beamforming was applied to the output from the four sub-apertures. Before the delay-and-sum (DAS) beamforming in each sub-aperture, echoes received by individual transducer elements were apodized with rectangular, Gaussian, and two Hanning functions, where the apodization with two Hanning functions realized lateral modulation of the ultrasonic field. The lateral spatial resolution was evaluated by the full width at half maximum of an echo from a string phantom, and the image contrast was evaluated using a cyst phantom. The modified APES beamformer realized a significantly better spatial resolution of 0.38 mm than that of the conventional delay-and-sum beamformer (0.67 mm), even with rectangular apodization. Using Gaussian apodization, the spatial resolution was further improved to 0.34 mm, and contrast was also improved from 4.3 to 5.1 dB. Furthermore, an image obtained by the modified APES beamformer with apodization consisting of two Hanning functions was better "tagged" as compared with the conventional DAS beamformer with the same apodization. Apodization was shown to be effective in adaptive beamforming, and an image obtained by the adaptive beamformer with lateral modulation seemed to have potential for improvement of the accuracy in measurement of tissue lateral motion.

Damping Estimation by 2D dr_APES and its Application to a Real Cable-Stayed Bridge

Introduced herein is a new structural damping identification approach based on a two-dimensional (2D) amplitude and phase estimation (APES) method. The original APES method is suitable for application to an undamped and complex harmonic vibration signal. Hence, it has to be modified for application to real damped vibration signals such as the vibration test signals in engineering structures. This modified approach will be named as dr_APES. It can transform one-dimensional (1D) signals in time domain into their corresponding signals in the 2D domain of frequency and damping factor. By applying this dr_APES approach, the three-dimensional (3D) amplitude spectrum, with peaks corresponding to the vibration modes, can be obtained for any given vibration signals. By accurately locating the coordinates of the peaks, modal frequencies and damping factors can be identified. Owing to the high-resolution of the location of 2D ordinates of the spectral line and the value of spectrum, the accurate location of peaks can be estimated, and therefore, the modal frequencies and damping factor can be accurately determined. This is demonstrated by a numerical case study. Moreover, by applying the proposed approach to a real onsite dynamic test on two cables in a cable-stayed bridge, the inherent damping of the two cables was identified accurately, thereby verifying the ability of proposed damping identification approach in meeting the requirement of weak damping characteristics identification in flexible structures such as naked cables.

Enhancement and Noise Statistics Estimation for Non-Stationary Voiced Speech

In this paper, single channel speech enhancement in the time domain is considered. We address the problem of modelling non-stationary speech by describing the voiced speech parts by a harmonic linear chirp model instead of using the traditional harmonic model. This means that the speech signal is not assumed stationary, instead the fundamental frequency can vary linearly within each frame. The linearly constrained minimum variance (LCMV) filter and the amplitude and phase estimation (APES) filter are derived in this framework and compared to the harmonic versions of the same filters. It is shown through simulations on synthetic and speech signals, that the chirp versions of the filters perform better than their harmonic counterparts in terms of output signal-to-noise ratio (SNR) and signal reduction factor. For synthetic signals, the output SNR for the harmonic chirp APES based filter is increased 3 dB compared to the harmonic APES based filter at an input SNR of 10 dB, and at the same time the signal reduction factor is decreased. For speech signals, the increase is 1.5 dB along with a decrease in the signal reduction factor of 0.7. As an implicit part of the APES filter, a noise covariance matrix estimate is obtained. We suggest using this estimate in combination with other filters such as the Wiener filter. The performance of the Wiener filter and LCMV filter are compared using the APES noise covariance matrix estimate and a power spectral density (PSD) based noise covariance matrix estimate. It is shown that the APES covariance matrix works well in combination with the Wiener filter, and the PSD based covariance matrix works well in combination with the LCMV filter.

Combining the APES and Minimum-variance Beamformers for Adaptive Ultrasound Imaging.

In recent years, adaptive minimum-variance (MV) beamforming has been successfully applied to medical ultrasound imaging, resulting in simultaneous improvement in imaging resolution and contrast. MV has high resolution and hence can provide accurate estimates of the target locations. However, the MV amplitude estimates are significantly biased downward, especially when occurring the errors in model parameters. The amplitude and phase estimation (APES) beamformer gives much more accurate amplitude estimates at the target locations, but at the cost of lower resolution. To reap the benefits of both MV and APES, we have proposed a modified APES (MAPES) beamformer by adding a parameter which controls the trade-off between spatial and amplitude resolutions. We have also proposed an adaptive beamformer which combines the MV and APES. The proposed beamformer first estimates the peak locations using the MV estimator and then refines the amplitude estimates at these locations using the MAPES estimator. By using simulated and experimental data-point targets as well as cyst phantoms-we show the efficacy of the proposed beamformers.

Effect of element directivity on adaptive beamforming applied to high-frame-rate ultrasound.

High-frame-rate ultrasound is a promising technique for measurement and imaging of cardiovascular dynamics. In high-frame-rate ultrasonic imaging, unfocused ultrasonic beams are used in transmit and multiple focused receiving beams are created by parallel beamforming using the delay and sum (DAS) method. However, the spatial resolution and contrast are degraded compared with conventional beamforming using focused transmit beams. In the present study, the minimum variance beamformer was examined for improvement of the spatial resolution in high-frame-rate ultrasound. In conventional minimum variance beamforming, the spatial covariance matrix of ultrasonic echo signals received by individual transducer elements is obtained without considering the directivity of the transducer element. By omitting the element directivity, the error in estimation of the desired signal (i.e., the echo from the focal point) increases, and as a result, the improvement of the spatial resolution is degraded. In the present study, the element directivity was taken into account in estimation of the spatial covariance matrix used in minimum variance beamforming. The effect of the element directivity on adaptive beamforming was evaluated by computer simulation and basic experiments using a phantom. In parallel beamforming with the conventional DAS beamformer, the lateral spatial resolution, which was evaluated from the lateral full width at half maximum of the echo amplitude profile in the basic experiment, was 0.50 mm. Using conventional amplitude and phase estimation (APES) beamforming, the lateral spatial resolution was improved to 0.37 mm. The lateral spatial resolution was further improved to 0.30 mm using the modified APES beamforming by considering the element directivity. Image contrast and contrast-to-noise ratios, respectively, were -12.3 and 6.5 dB (DAS), -32.8 and -11.3 dB (APES), and -7.0 and 3.1 dB (modified APES).

Reduced-rank STAP for target detection in heterogeneous environments

In an airborne radar context, heterogeneous situations are a serious concern for space-time adaptive processing (STAP), where the required secondary training data have to be target free and homogeneous with the tested data. Consequently, the performance of these detectors is severely impacted when facing a heavily heterogeneous environment. Single data-set algorithms such as the maximum likelihood estimation detector (MLED) algorithm, based on the amplitude and phase estimation (APES) method, have proved their efficiency in overcoming this problem by only working on primary data. However, restricting the estimation domain solely to the primary data often implies an inaccurate estimation of the covariance matrix. In this paper, we demonstrate that we can use reduced-rank STAP on the single data-set APES method to increase the performance of the STAP processing. We also introduce an algorithm that reduces the computational cost of the standard subspace-based algorithms based on eigenvalue decomposition. The results on realistic data show that reduced-rank methods outperform traditional single data-set methods in detection and in clutter rejection.

Adaptive DOA Estimation in MIMO Radar based on Canonical Correlation Analysis Method

In multiple-input multiple-output (MIMO) radar, independent waveforms are transmitted from different antennas, and the target parameters are estimated via the linearly independent echoes frim different targets.Several adaptive approaches are directly applied to target angle and target amplitude estimation, including Capon, APES(amplitude and phase estimation). The CCA (canonical correlation analysis) approach is first proposed to estimate target locations which has high peak amplitudes, then a gradient-based algorithm is presented to improve the target angle estimation accuracy based on CCA approach without spectrum peak searching. With an initial angle, the angle sequence is iteratively updated with adaptive steps and converges to local peaks which indicate the target locations. Simulations show that the target angle accuracy is improved, and the common DOA (direction-of -arrive) problem is avoided.

Splitting Matching Pursuit Method for Reconstructing Sparse Signal in Compressed Sensing

In this paper, a novel method named as splitting matching pursuit (SMP) is proposed to reconstructK-sparse signal in compressed sensing. The proposed method selectsFl (Fl>2K)largest components of the correlation vectorc, which are divided intoFsplit sets with equal lengthl. The searching area is thus expanded to incorporate more candidate components, which increases the probability of finding the true components at one iteration. The proposed method does not require the sparsity levelKto be known in prior. The Merging, Estimation and Pruning steps are carried out for each split set independently, which makes it especially suitable for parallel computation. The proposed SMP method is then extended to more practical condition, e.g. the direction of arrival (DOA) estimation problem in phased array radar system using compressed sensing. Numerical simulations show that the proposed method succeeds in identifying multiple targets in a sparse radar scene, outperforming other OMP-type methods. The proposed method also obtains more precise estimation of DOA angle using one snapshot compared with the traditional estimation methods such as Capon, APES (amplitude and phase estimation) and GLRT (generalized likelihood ratio test) based on hundreds of snapshots.

Spectral doppler estimation utilizing 2-D spatial information and adaptive signal processing

The trade-off between temporal and spectral resolution in conventional pulsed wave (PW) Doppler may limit duplex/triplex quality and the depiction of rapid flow events. It is therefore desirable to reduce the required observation window (OW) of the Doppler signal while preserving the frequency resolution. This work investigates how the required observation time can be reduced by adaptive spectral estimation utilizing 2-D spatial information obtained by parallel receive beamforming. Four adaptive estimation techniques were investigated, the power spectral Capon (PSC) method, the amplitude and phase estimation (APES) technique, multiple signal classification (MUSIC), and a projection-based version of the Capon technique. By averaging radially and laterally, the required covariance matrix could successfully be estimated without temporal averaging. Useful PW spectra of high resolution and contrast could be generated from ensembles corresponding to those used in color flow imaging (CFI; OW = 10). For a given OW, the frequency resolution could be increased compared with the Welch approach, in cases in which the transit time was higher or comparable to the observation time. In such cases, using short or long pulses with unfocused or focused transmit, an increase in temporal resolution of up to 4 to 6 times could be obtained in in vivo examples. It was further shown that by using adaptive signal processing, velocity spectra may be generated without high-pass filtering the Doppler signal. With the proposed approach, spectra retrospectively calculated from CFI may become useful for unfocused as well as focused imaging. This application may provide new clinical information by inspection of velocity spectra simultaneously from several spatial locations.

Improved STAP algorithm based on APES

Space-time adaptive processing (STAP) has been proven to be one of the best techniques capable of detecting weak moving targets in strong clutter environment and has been widely applied in airborne ground moving target indication (GMTI) radar. This paper applies an amplitude and phase estimation (APES) approach to two aspects of the STAP algorithm. Firstly, APES is applied to accurately describe the clutter characteristic in angle-Doppler domain. Then, APES is incorporated into the standard STAP algorithm to improve its performance without increasing transmitting/receiving channel and pulse number. The experimental examples show that the detection performance can be improved by using the APES technique, as well as the high computational complexity can be avoided.