Super-resolution parameter estimation of monopulse radar by wide-narrowband joint processing

The angular resolution of radar is of crucial signifi-cance to its tracking performance.In this paper,a super-resolu-tion parameter estimation algorithm based on wide-narrowband joint processing is proposed to improve the angular resolution of wideband monopulse radar.The range cells containing resolv-able scattering points are detected in the wideband mode,and these range cells are adopted to estimate part of the target parameters by algorithms of low computational requirement.Then,the likelihood function of the echo is constructed in the narrow-band mode to estimate the rest of the parameters,and the parameters estimated in the wideband mode are employed to reduce computation and enhance estimation accuracy.Simu-lation results demonstrate that the proposed algorithm has higher estimation accuracy and lower computational complexity than the current algorithm and can avoid the risk of model mis-match.

Direction finding of bistatic MIMO radar in strong impulse noise

For bistatic multiple-input multiple-output(MIMO)radar,this paper presents a robust and direction finding method in strong impulse noise environment.By means of a new lower order covariance,the method is effective in suppressing impulse noise and achieving superior direction finding performance using the maximum likelihood(ML)estimation method.A quantum equilibrium optimizer algorithm(QEOA)is devised to resolve the corresponding objective function for efficient and accurate direc-tion finding.The results of simulation reveal the capability of the presented method in success rate and root mean square error over existing direction-finding methods in different application situations,e.g.,locating coherent signal sources with very few snapshots in strong impulse noise.Other than that,the Cramér-Rao bound(CRB)under impulse noise environment has been drawn to test the capability of the presented method.

Enhanced Wideband Frequency Estimation via FFT: Leveraging Polynomial Interpolation and Array Indexing

Accurate frequency estimation in a wideband digital receiver using the FFT algorithm encounters challenges, such as spectral leakage resulting from the FFT’s assumption of signal periodicity. High-resolution FFTs pose computational demands, and estimating non-integer multiples of frequency resolution proves exceptionally challenging. This paper introduces two novel methods for enhanced frequency precision: polynomial interpolation and array indexing, comparing their results with super-resolution and scalloping loss. Simulation results demonstrate the effectiveness of the proposed methods in contemporary radar systems, with array indexing providing the best frequency estimation despite utilizing maximum hardware resources. The paper demonstrates a trade-off between accurate frequency estimation and hardware resources when comparing polynomial interpolation and array indexing.

Direction-of-arrival estimation for co-located multiple-input multiple-output radar using structural sparsity Bayesian learning

This paper addresses the direction of arrival （DOA） estimation problem for the co-located multiple-input multiple- output （MIMO） radar with random arrays. The spatially distributed sparsity of the targets in the background makes com- pressive sensing （CS） desirable for DOA estimation. A spatial CS framework is presented, which links the DOA estimation problem to support recovery from a known over-complete dictionary. A modified statistical model is developed to ac- curately represent the intra-block correlation of the received signal. A structural sparsity Bayesian learning algorithm is proposed for the sparse recovery problem. The proposed algorithm, which exploits intra-signal correlation, is capable being applied to limited data support and low signal-to-noise ratio （SNR） scene. Furthermore, the proposed algorithm has less computation load compared to the classical Bayesian algorithm. Simulation results show that the proposed algorithm has a more accurate DOA estimation than the traditional multiple signal classification （MUSIC） algorithm and other CS recovery algorithms.

Direction-of-arrival estimation based on direct data domain (D3) method

A direction-of-arrival （DOA） estimation algorithm based on direct data domain （D3） approach is presented. This method can accuracy estimate DOA using one snapshot modified data, called the temporal and spatial two-dimensional vector reconstruction （TSR） method. The key idea is to apply the D3 approach which can extract the signal of given frequency but null out other frequency signals in temporal domain. Then the spatial vector reconstruction processing is used to estimate the angle of the spatial coherent signal source based on extract signal data. Compared with the common temporal and spatial processing approach, the TSR method has a lower computational load, higher real-time performance, robustness and angular accuracy of DOA. The proposed algorithm can be directly applied to the phased array radar of coherent pulses. Simulation results demonstrate the performance of the proposed technique.

QPSO-based algorithm of CSO joint infrared super-resolution and trajectory estimation

The midcourse ballistic closely spaced objects（CSO） create blur pixel-cluster on the space-based infrared focal plane,making the super-resolution of CSO quite necessary.A novel algorithm of CSO joint super-resolution and trajectory estimation is presented.The algorithm combines the focal plane CSO dynamics and radiation models,proposes a novel least square objective function from the space and time information,where CSO radiant intensity is excluded and initial dynamics（position and velocity） are chosen as the model parameters.Subsequently,the quantum-behaved particle swarm optimization（QPSO） is adopted to optimize the objective function to estimate model parameters,and then CSO focal plane trajectories and radiant intensities are computed.Meanwhile,the estimated CSO focal plane trajectories from multiple space-based infrared focal planes are associated and filtered to estimate the CSO stereo ballistic trajectories.Finally,the performance（CSO estimation precision of the focal plane coordinates,radiant intensities,and stereo ballistic trajectories,together with the computation load） of the algorithm is tested,and the results show that the algorithm is effective and feasible.

Super-resolution DOA estimation for correlated off-grid signals via deep estimator

This paper develops a deep estimator framework of deep convolution networks(DCNs)for super-resolution direction of arrival(DOA)estimation.In addition to the scenario of correlated signals,the quantization errors of the DCN are the major challenge.In our deep estimator framework,one DCN is used for spectrum estimation with quantization errors,and the remaining two DCNs are used to estimate quantization errors.We propose training our estimator using the spatial sampled covariance matrix directly as our deep estimator’s input without any feature extraction operation.Then,we reconstruct the original spatial spectrum from the spectrum estimate and quantization errors estimate.Also,the feasibility of the proposed deep estimator is analyzed in detail in this paper.Once the deep estimator is appropriately trained,it can recover the correlated signals’spatial spectrum fast and accurately.Simulation results show that our estimator performs well in both resolution and estimation error compared with the state-of-the-art algorithms.

Direction of arrival estimation method based on quantum electromagnetic field optimization in the impulse noise

In order to resolve direction finding problems in the impulse noise,a direction of arrival(DOA)estimation method is proposed.The proposed DOA estimation method can restrain the impulse noise by using infinite norm exponential kernel covariance matrix and obtain excellent performance via the maximumlikelihood(ML)algorithm.In order to obtain the global optimal solutions of this method,a quantum electromagnetic field optimization(QEFO)algorithm is designed.In view of the QEFO algorithm,the proposed method can resolve the difficulties of DOA estimation in the impulse noise.Comparing with some traditional DOA estimation methods,the proposed DOA estimation method shows high superiority and robustness for determining the DOA of independent and coherent sources,which has been verified via the Monte-Carlo experiments of different schemes,especially in the case of snapshot deficiency,low generalized signal to noise ratio(GSNR)and strong impulse noise.Beyond that,the Cramer-Rao bound(CRB)of angle estimation in the impulse noise and the proof of the convergence of the QEFO algorithm are provided in this paper.

Direction estimation for two steady targets in monopulse radar

Traditional monopulse radar cannot resolve two targets present in one range and Doppler cell by means of the monopulse ratio. A novel algorithm is proposed to estimate the directions of two steady targets with two pulses. The algorithm has a closedform expression and its variance is derived at high signal-to-noise ratios（SNRs）. Furthermore, the pulse pair selection criterion and the estimation method with multiple pulses are given. Finally, some numerical results are shown to validate the proposed algorithm and the effect of slight target fluctuations is tested.

A NOVEL UNIVERSAL PREPROCESSING APPROACH FOR HIGH-RESOLUTION DIRECTION-OF-ARRIVAL ESTIMATION

A novel universal preprocessing method is proposed to estimate angles of arrival,which is applicable to one-or two-dimensional high resolution processing based on arbitrarycenter-symmetric arrays (such as uniform linear arrays, equal-spaced rectangular planar arraysand symmetric circular arrays). By mapping the complex signal space into the real one, the newmethod can effectively reduce the computation needed by the signal subspace direction findingtechniques without any performance degradation. In addition, the new preprocessing scheme itselfcan decorrelate the coherent signals received on the array. For regular array geometry such asuniform linear arrays and equal-spaced rectangular planar arrays, the popular spatial smoothingpreprocessing technique can be combined with the novel approach to improve the decorrelatingability. Simulation results confirm the above conclusions.

Ambiguity Resolution in Direction of Arrival Estimation with Linear Antenna Arrays Using Differential Geometry

Linear antenna arrays(LAs)can be used to accurately predict the direction of arrival(DOAs)of various targets of interest in a given area.However,under certain conditions,LA suffers from the problem of ambiguities among the angles of targets,which may result inmisinterpretation of such targets.In order to cope up with such ambiguities,various techniques have been proposed.Unfortunately,none of them fully resolved such a problem because of rank deficiency and high computational cost.We aimed to resolve such a problem by proposing an algorithm using differential geometry.The proposed algorithm uses a specially designed doublet antenna array,which is made up of two individual linear arrays.Two angle observation models,ambiguous observation model(AOM)and estimated observation model(EOM),are derived for each individual array.The ambiguous set of angles is contained in the AOM,which is obtained from the corresponding array elements using differential geometry.The EOM for each array,on the other hand,contains estimated angles of all sources impinging signals on each array,as calculated by a direction-finding algorithm such as the genetic algorithm.The algorithm then contrasts the EOM of each array with its AOM,selecting the output of that array whose EOM has the minimum correlation with its corresponding AOM.In comparison to existing techniques,the proposed algorithm improves estimation accuracy and has greater precision in antenna aperture selection,resulting in improved resolution capabilities and the potential to be used more widely in practical scenarios.The simulation results using MATLAB authenticates the effectiveness of the proposed algorithm.

FAST CAPACITY ESTIMATION ALGORITHMS FOR MANETS USING DIRECTIONAL ANTENNAS

In this letter,capacity estimation for Mobile Ad hoc NETworks (MANETs) using direc- tional antennas are studied.Two Matrix-based Fast Calculation Algorithms (MFCAs) are proposed to estimate the network capacity in a network scenario in which there is no channel sharing among multiple sessions and traffic is sensitive to delay with an end-to-end delay constraint.The first algo- rithm MFCA-1 is used to estimate network capacity in a situation where all links have the same delay. It estimates the maximum number of k-hop sessions in a network based on the k-hop adjacency matrix of the network.The second algorithm MFCA-2 is used to estimate network capacity in a situation where different links may have different delays.It calculates the maximum number of sessions in a network with an end-to-end delay constraint based on the adjacency matrix and the link-delay matrix of the network.Numerical and simulation results show that both MFCA-1 and MFCA-2 can calculate network capacity much faster than the well-known Brute-Force Search Algorithm (BFSA) but with the same accuracy.

Wideband direction-of-arrival estimation based on cubic spline function

A new direction-of-arrival （DOA） estimation algorithm for wideband sources is introduced, The new method obtains the output of the virtual arrays in the signal bandwidth using cubic spline function interpolation techniques. The narrowband high- resolution algorithm is then used to get the DOA estimation. This technique does not require any preliminary knowledge of DOA angles. Simulation results demonstrate the effectiveness of the method.

Computationally efficient MUSIC based DOA estimation algorithm for FMCW radar

This paper proposes low-cost yet high-accuracy direction of arrival(DOA)estimation for the automotive frequency-modulated continuous-wave(FMcW)radar.The existing subspace-based DOA estimation algorithms suffer fromeither high computational costs or low accuracy.We aim to solve such contradictory relation between complexity and accuracy by using randomizedmatrix approximation.Specifically,we apply an easily-interpretablerandomized low-rank approximation to the covariance matrix(CM)and R∈C^(M×M)throughthresketch maties in the fom of R≈OBQ^(H).Here the approximately compute its subspaces.That is,we first approximate matrix Q∈C^(M×z)contains the orthonormal basis for the range of the sketchmatrik C∈C^(M×z)cwe whichis etrated fom R using randomized unifom counsampling and B∈C^(z×z)is a weight-matrix reducing the approximation error.Relying on such approximation,we are able to accelerate the subspacecomputation by the orders of the magnitude without compromising estimation accuracy.Furthermore,we drive a theoretical error bound for the suggested scheme to ensure the accuracy of the approximation.As validated by the simulation results,the DOA estimation accuracy of the proposed algorithm,eficient multiple signal classification(E-MUSIC)s high,closely tracks standardMUSIC,and outperforms the well-known algorithms with tremendouslyreduced time complexity.Thus,the devised method can realize high-resolutionreal-time target detection in the emerging multiple input and multiple output(MIMO)automotive radar systems.

High-Precision Time Delay Estimation Based on Closed-Form Offset Compensation

To improve the estimation accuracy,a novel time delay estimation(TDE)method based on the closed-form offset compensation is proposed.Firstly,we use the generalized cross-correlation with phase transform(GCC-PHAT)method to obtain the initial TDE.Secondly,a signal model using normalized cross spectrum is established,and the noise subspace is extracted by eigenvalue decomposition(EVD)of covariance matrix.Using the orthogonal relation between the steering vector and the noise subspace,the first-order Taylor expansion is carried out on the steering vector reconstructed by the initial TDE.Finally,the offsets are compensated via simple least squares(LS).Compared to other state-of-the-art methods,the proposed method significantly reduces the computational complexity and achieves better estimation performance.Experiments on both simulation and real-world data verify the efficiency of the proposed approach.

Explosion limits estimation and process optimization of direct propylene epoxidation with H2 and O2

Direct propylene epoxidation with H2 and O2,an attractive process to produce propylene oxide(PO),has a potential explosion danger due to the coexistence of flammable gases(i.e.,C3 H6 and H2)and oxidizer(i.e.,O2).The unknown explosion limits of the multi-component feed gas mixture make it difficult to optimize the reaction process under safe operation conditions.In this work,a distribution method is proposed and verified to be effective by comparing estimated and experimental explosion limits of more than 200 kinds of flammable gas mixture.Then,it is employed to estimate the explosion limits of the feed gas mixture,some results of which are also validated by the classic Le Chatelier’s Rule and flammable resistance method.Based on the estimated explosion limits,process optimization is carried out using commercially high and inherently safe reactant concentrations to enhance reaction performance.The promising results are directly obtained through the interface called gOPT in gPROMS only by using a simple,easy-constructed and mature packed-bed reactor,such as the PO yield of 13.3%,PO selectivity of 85.1%and outlet PO fraction of 1.8%.These results can be rationalized by indepth analyses and discussion about the effects of the decision variables on the operation safety and reaction performance.The insights revealed here could shed new light on the process development of the PO production based on the estimation of the explosion limits of the multi-component feed gas mixture containing flammable gase s,inert gas and O2,followed by process optimization.

Radial Basis Function Interpolation and Galerkin Projection for Direct Trajectory Optimization and Costate Estimation

This work presents a novel approach combining radial basis function(RBF)interpolation with Galerkin projection to efficiently solve general optimal control problems.The goal is to develop a highly flexible solution to optimal control problems,especially nonsmooth problems involving discontinuities,while accounting for trajectory accuracy and computational efficiency simultaneously.The proposed solution,called the RBF-Galerkin method,offers a highly flexible framework for direct transcription by using any interpolant functions from the broad class of global RBFs and any arbitrary discretization points that do not necessarily need to be on a mesh of points.The RBF-Galerkin costate mapping theorem is developed that describes an exact equivalency between the Karush-Kuhn-Tucker(KKT)conditions of the nonlinear programming problem resulted from the RBF-Galerkin method and the discretized form of the first-order necessary conditions of the optimal control problem,if a set of discrete conditions holds.The efficacy of the proposed method along with the accuracy of the RBF-Galerkin costate mapping theorem is confirmed against an analytical solution for a bang-bang optimal control problem.In addition,the proposed approach is compared against both local and global polynomial methods for a robot motion planning problem to verify its accuracy and computational efficiency.

Modified Eigendecomposition DOA EstimateAlgorithms and Field Test Studiesfor Super-Resolution

The signal direction of arrival (DOA) estimate algorithm based on the eigendecomposition of the modified covariance matrix is introduced in this paper. A field test system consisting of a 4-element linear array and a meter band radar is also presented, which is applied to the experimental studies of the algorithms in the practical performances. The results of the test indicate that when SNR is only 5.85 dB, two airplanes being 0.25 beam width apart in azimuth can be resolved clearly.

Direct Adaptive Control Based on Gradient Estimation

A control method of direct adaptive control based on gradient estimation is proposed in this article. The dynamic system is embedded in a linear model set. Based on the embedding property of the dynamic system, an adaptive optimal control algorithm is proposed. The robust convergence of the proposed control algorithm has been proved and the static control error with the proposed method is also analyzed. The application results of the proposed method to the industrial polypropylene process have verified its feasibility and effectiveness.

Decoupled estimation of frequency-dependent IQI and channel for OFDM systems with direct-conversion transceivers

The in-phase and quadrature-phase imbalance (IQI) is one of the major radio frequency impairments existing in orthogonal frequency division multiplexing (OFDM) systems with direct-conversion transceivers. During the transmission of the communication signal, the impact of IQI is coupled with channel impulse responses (CIR), which makes the traditional channel estimation schemes ineffective. A decoupled estimation scheme is proposed to separately estimate the frequency-dependent IQI and wireless channel. Firstly, the generalized channel model is built to separate the parameters of IQI and wireless channel. Then an iterative estimation scheme of frequency-dependent IQI is designed at the initial stage of communication. Finally, based on the estimation result of IQI, the least square algorithm is utilized to estimate the channel-related parameters at each time of channel variation. Compared with the joint estimation schemes of IQI and channel, the proposed decoupled estimation scheme requires much lower training overhead at each time of channel variation. Simulation results demonstrate the good estimation performance of the proposed scheme.