An efficient method for parameter estimation and separation of multi-component LFM signals

LFM signals are widely used in the fields of radar and communication etc., which make signals detection and parameters estimation a very important technology in multicomponent LFM signals. To solve the problems of error detection caused by the cross-terms in WVD-Hough methods and other modified WVD distribution methods, and low parameter estimation precision due to poor timefrequency concentration in S-Hough transform, this paper proposed a detection and parameter estimation algorithm of multicomponent LFM signals based on reassigned generalized S-transform and Hough transform. Firstly calculate the time-frequency distribution of multicomponent LFM signals by generalized S-transform. Secondly reassign the S-transform time-frequency distribution to improve the time-frequency concentration property. Finally implement signal detection and parameter estimation by Hough transform to the reassigned S-transform TFD. The simulation results show that this algorithm has good performance in detecting multicomponent LFM signals and estimating the parameters in low SNR case.

Parameter estimation of chirp signals based on fractional Fourier transform

Structural instantaneous frequency identification based on synchrosqueezing fractional Fourier transform

This paper describes a new algorithm for parameter estimation of multichannel single-tone signals. The algorithm works in the frequency domain and uses a least square optimization to estimate the signals' amplitudes, phases and common frequency. The performance of the algorithm is shown on both simulated and measured signals.

A novel algorithm for the direction of arrival (DOA) estimation based on the fractional Fourier transform (FRFT) is proposed. Firstly, using the properties of FRFT and mask processing, Multi-component LFM sig-nals are filtered and demodulated into a number of stationary single frequency signals. Then the one-dimensional (1-D) direction estimation of LFM signals can be achieved by combining with the tradi-tional spectrum search method in the fractional Fourier (FRF) domain. As for the multi-component LFM signals, there is no cross-term interference, the mean square error (MSE) and Cramer-Rao bound (CRB) are also analyzed which perfects the method theoretically, simulation results are provided to show the validity of our method. The proposed algorithm is also extended to the uniform circular array (UCA), which realizes the two-dimensional (2-D) estimation. Using the characteristics of time-frequency rotation and demodulation of FRFT, the observed LFM signals are demodulated into a series of single frequency ones; secondly, operate the beam-space mapping to the single frequency signals in FRF domain, which UCA in array space is changed into the virtual uniform circular array (ULA) in mode space; finally, the DOA estimation can be realized by the traditional spectral estimation method. Compared with other method, the complex time-frequency cluster and the parameter matching computation are avoided; meanwhile enhances the esti-mation precision by a certain extent. The proposed algorithm can also be used in the multi-path and Doppler frequency shift complex channel, which expands its application scope. In a word, a demodulated DOA esti-mation algorithm is proposed and is applied to 1-D and 2-D angle estimation by dint of ULA and UCA re-spectively. The detailed theoretical analysis and adequate simulations are given to support our proposed al-gorithm, which enriches the theory of the FRFT.

The synchrosqueezing transform is a time-frequency (TF) analysis tool to process non-stationary signals. Unfortunately, it does not produce accurate TF results when faced with LFM signals. In this paper, we propose synchrosqueezing-Hough transform (SS-HT) to address this problem. First, we introduce Hough transform to obtain more accurate instantaneous frequency (IF) estimation. Then, in order to improve the TF concentration, a new TF rearrangement operator is constructed. Furthermore, we demonstrated that SS-HT has the ability to reconstruct the signal. The experimental results prove that SSHT can not only obtain high TF concentration, but also improve the accuracy of IF estimation and parameters estimation of LFM signals.

Parameter estimation of amplitude-modulated polynomial phase signals embedded in additive white Gaussian noise is considered. The amplitude modulation is modeled as the sum of a real-valued deterministic function and a zero mean correlated stationary random process. It is shown that cyclic moments-based estimators, previously proposed for parameter estimation of polynomial phase signals modulated by stationary random processes, can be adapted to the more general signal model considered here. The covariance matrices of the cyclic moments-based amplitude and phase parameter estimators are derived for large sample lengths. Using this result, it is shown how the lags can be chosen to minimize the large-sample variances of the cyclic moments-based phase parameter estimators. Comparisons with the Cramer-Rao bounds are performed under the assumption of a Gaussian modulating process. The theoretical derivations are confirmed by simulation results.

The aim of this work is the parameter estimation of polynomial-phase signals (PPSs) embedded in white noise. The proposed estimation algorithm is a generalization of the method based on the polynomial-phase transform (PPT), able to solve an ambiguity problem appearing when applying the PPT to PPSs having the same highest order phase coefficients. The proposed approach is based on the intersection of two (or more) signal subspaces sharing only the useful components but not the undesired spurious harmonics. Different signal subspaces are obtained by exploiting a multilag definition of the PPT.

To attain fast and accurate parameters estimation of LFM signal with low SNR, a novel estimation method is presented. In this method, LFM is received with dual channel preamplifier so that noises in LFM signals are uncorrelated, parameters are estimated by using modulus of received LFM signals fractional order cross spectrum and cubic splines interpolation. Numerical simulation shows that the presented method is with good noise immunity, high precision of parameters estimation.

This paper addresses the issue of detection and parameter estimation of multicomponent Polynomial Phase Signals (mc-PPS’s) embedded in noise based on high-order ambiguity function (HAF). We first show how existing PHAF-based techniques (PHAF—product HAF) are inadequate mainly in providing reliable detection for mc-PPS. The main contribution of this paper is that we present a novel parameter estimation method. Firstly, given a set of time delay, it gives rise to a set of estimates of phase coefficients based on HAF. Then it produces a final estimate of phase coefficient by means of voting. The new method improves the probability of detection and estimation accuracy while avoiding the issue of threshold selection. Computer simulations are carried out to illustrate the advantage of the proposed method over existing techniques.

The frequency space of modern radar system has been extended to a wide range. To intercept the modern radar signals with high interception probability, the wideband receiving is of significance to the radar reconnaissance receiver. The Nyquist folding receiver (NYFR) is a novel ultra-wideband receiving architecture, which requires a small amount of equipment. Because the mono-pulse radar signal is widely used in radar systems, the multiple mono-pulse radar signals will be intercepted by the NYFR. The parameter estimation of multiple simultaneous arrival mono-pulse radar signals intercepted by the NYFR using periodic chirp local oscillator is considered. The definition of Periodic fractional autocorrelation (PFA) is proposed. Based on the PFA, a novel fast parameter estimation method is given. The proposed estimation approach can estimate the multiple NYFR outputs under frequency aliasing condition and the computational complexity of the proposed method is low. Simulation results show the effectiveness of the proposed method.

LFM signals are widely applied in radar, communication, sonar and many other fields. LFM signals received by these systems contain a lot of noise and outliers. In order to suppress the interference of strong impulse noise on target signals and realize the accurate estimation of LFM signal parameters, the impulse noise of echo signals need to be filtered. In this paper, to solve the problem of poor performance of LFM signal parameter estimation based on fractional Fourier transform in impulse noise, alpha stable distribution is used to establish the mathematical model of impulse noise. The proposed fastest tracking differentiator with an adaptive tracking factor is used to suppress the strong impulse noise, and fractional Fourier transform is used to estimate the parameter of the LFM signals. The experimental results show that the proposed fastest tracking differentiator with an adaptive tracking factor has a good filtering performance. It can effectively filter the impulse noise in the echo signal and allows the FrFT method to accurate estimate the parameters of the LFM signals in strong impulse noise.

A novel method using artificial neural network with back-propagation algorithm to detect and separate LFM signal is proposed. This method trains the network by LFM signal mixed with Gauss noise. Simulation result shows the trained BP neural network can eliminate noise effectively. In addition, if the learning sample is a multicomponent LFM signal, the trained network can separate the LFM signal component conveniently. Theoretical analysis and simulation results show that the proposed method has low computational complexity and good performance

Multi-buoy sonar systems achieve target localization by receiving broadband Linear Frequency Modulation signals emitted from the transmitter. Accurate estimations of the parameters of Linear Frequency Modulation signals significantly enhance the localization accuracy. Linear Frequency Modulation signals can be focused into the fractional domain through Fractional Fourier Transform, but this increases the computational complexity. In marine environments, the low signal-to-noise ratio and multipath effects degrade the parameter estimation accuracy further. To address these issues, this paper proposes a fast estimation algorithm based on the Fractional Fourier Transform and a Gradient Subtraction-Average-Based Optimizer. This algorithm leverages the impulsive characteristics of Linear Frequency Modulation signals after Fractional Fourier Transform transformation, using the Fractional Fourier Transform as the fitness function. The Gradient Subtraction-Average-Based Optimizer algorithm includes three enhancement strategies: chaotic mapping initialization, a Golden Sine Algorithm, and an adaptive t-distribution variational operator. The simulation results demonstrate that the Gradient Subtraction-Average-Based Optimizer algorithm improves the issues of low diversity in the search agents, imbalanced global and local search capabilities, and susceptibility to local optima. A comparative analysis and statistical testing confirm that under a low signal-to-noise ratio and multipath effect conditions, the Gradient Subtraction-Average-Based Optimizer algorithm not only ensures real-time parameter estimation but also improves the estimation accuracy. The results of the parameter estimation provide reliable data support for subsequent target localization.

By using compressive measurements, parameter estimation of LFM signal can be performed without completely reconstructing the original signal. Currently, estimation methods based on compressive measurements will not get good results for LFM signal with narrow-band interference. That is because, by using sparse decomposition of mixed-signal with a single dictionary, narrow-band interference cannot be separated effectively. Aiming at this situation, an improved parameter estimation method based on morphological component analysis is proposed in this paper, in which compositions with morphological differences in the mixed-signal are sparsely represented using corresponding dictionaries which are constructed according to different compositions. For the sparse representation of the mixed-signal is improved, the method leads to better parameter estimation of LFM signal. The experimental results indicate that in compare with the conventional method, with the same number of sampling, by using the method proposed in this paper, parameter estimation of LFM signal with narrow-band interference is more accurate.