A frequency domain estimation and compensation method for system synchronization parameters of distributed-HFSWR

To analyze the influence of time synchronization error,phase synchronization error,frequency synchronization error,internal delay of the transceiver system,and range error and angle error between the unit radars on the target detection performance,firstly,a spatial detection model of distributed high-frequency surface wave radar(distributed-HFSWR)is established in this paper.In this model,a method for accurate extraction of direct wave spectrum based on curve fitting is proposed to obtain accurate system internal delay and frequency synchronization error under complex electromagnetic environment background and low signal to noise ratio(SNR),and to compensate for the shift of range and Doppler frequency caused by time-frequency synchronization error.The direct wave component is extracted from the spectrum,the range estimation error and Doppler estimation error are reduced by the method of curve fitting,and the fitting accuracy of the parameters is improved.Then,the influence of frequency synchronization error on target range and radial Doppler velocity is quantitatively analyzed.The relationship between frequency synchronization error and radial Doppler velocity shift and range shift is given.Finally,the system synchronization parameters of the trial distributed-HFSWR are obtained by the proposed spectrum extraction method based on curve fitting,the experimental data is compensated to correct the shift of the target,and finally the correct target parameter information is obtained.Simulations and experimental results demonstrate the superiority and correctness of the proposed method,theoretical derivation and detection model proposed in this paper.

Particle swarm optimization for rigid body reconstruction after micro-Doppler removal in radar analysis

The rotating micro-motion parts produce micro-Doppler(m-D)effects which severely influence the quality of inverse synthetic aperture radar(ISAR)imaging for complex moving targets.Recently,a method based on short-time Fourier transform(STFT)and L-statistics to remove m-D effects is proposed,which can separate the rigid body parts from interferences introduced by rotating parts.However,during the procedure of removing m-D parts,the useful data of the rigid body parts are also removed together with the m-D interferences.After summing the rest STFT samples,the result will be affected.A novel method is proposed to recover the missing values of the rigid body parts by the particle swarm optimization(PSO)algorithm.For PSO,each particle corresponds to a possible phase estimation of the missing values.The best particle is selected which has the minimal energy of the side lobes according to the best fitness value of particles.The simulation and measured data results demonstrate the effectiveness of the proposed method.

Three-Dimensional Interferometric ISAR Sensors Imaging for the Ship Target with Two-Dimensional Sparsity

There are great challenges for traditional three-dimensional( 3-D) interferometric inverse synthetic aperture radar( In ISAR) imaging algorithms of ship targets w ith 2-D sparsity in actual radar imaging system. To deal w ith this problem,a novel 3-D In ISAR imaging method is proposed in this paper.First,the high-precision gradient adaptive algorithm w as adopted to reconstruct the echoes in range dimension. Then the method of minimizing the entropy of the average range profile w as applied to estimate the parameters w hich are used to compensate translation components of the received echoes. Besides,the phase adjustment and image coregistration of the sparse echoes w ere achieved at the same time through the approach of the joint phase autofocus. Finally,the 3-D geometry coordinates of the ship target w ith 2-D sparsity w ere reconstructed by combining the range measurement and interferometric processing of the ISAR images. Simulation experiments w ere carried out to verify the practicability and effectiveness of the algorithm in the case that the received echoes are in 2-D sparsity.

Multiframe track-before-detect method based on velocity filtering in mixed coordinates

In this paper,a velocity filtering based track-before-detect algorithm in mixed coordinates is presented to address the problem of integration loss caused by inaccurate motion model in polar coordinate sensors.Since the motion of a con-stant velocity(CV)target is better modeled in Cartesian coordi-nates,the search of measurements for integration in polar sensor coordinates is carried out according to the CV model in Cartesian coordinates instead of an approximate model in polar sensor coordinates.The position of each cell is converted into Cartesian coordinates and predicted according to an assumed velocity.Then,the predicted Cartesian position is converted back to polar sensor coordinates for multiframe accumulation.The use of the correct model improves integration effectiveness and consequently improves algorithm performance.To handle the weak target with unknown velocity,a velocity filter bank in mixed coordinates is presented.The influence of velocity mis-match on the performance of filter bank is analyzed,and an effi-cient strategy for filter bank design is proposed.Numerical re-sults are presented to demonstrate the effectiveness of the pro-posed algorithm.

High-Resolution ISAR Imaging Based on Sparse Iterative Covariance-Based Estimation

High-resolution of Inverse Synthetic Aperture Radar (ISAR) in the azimuth direction can be achieved by increasing the coherent accumulation angle of the target rotation.However, in practice, the coherent accumulation angle may be small.In this paper, a novel algorithm for high-resolution ISAR imaging based on the SParse Iterative Covariance-based Estimation (SPICE) is proposed.As a nonparametric sparse spectrum estimation algorithm, the SPICE algorithm does not need to set any parameters and it converges globally, so it can realize high quality imaging with limited measurements.In addition, a fast implementation of the SPICE algorithm based on the Gohberg-Semencul (G-S) factorization is introduced in this paper.The ISAR imaging of simulated and measured data was analyzed to illustrate the effectiveness of the novel approach.

Track-before-detect for bistatic radar based on pseudo-spectrum

Track-Before-Detect(TBD) is an efficient method to detect dim targets for radars. Conventional TBD usually follows an approximate motion model of the target, which may cause an inaccurate integration of the target energy. A TBD technique on basis of pseudo-spectrum in mixed coordinates adopting an accurate motion model for bistatic radar system is developed in this paper.The predicted position in bistatic polar plane is derived according to a nonlinear function that exactly describes the constant Cartesian velocity motion. Then around the predicted position, a pseudo-spectrum is formulated and its samples are accumulated to the integration frame for energy integration. The evolution of the state and the procedure of accumulation of the target energy are derived elaborately. The superior performance of the proposed method is demonstrated by some simulations.

Multiframe weak target track-before-detect based on pseudo-spectrum in mixed coordinates

Traditional multiframe Track-Before-Detect(TBD)may incur adverse integration loss resulting from model mismatch in sensor coordinates.Its suboptimal integration strategy may cause target envelope degradation.To address these issues,a pseudo-spectrum-based multiframe TBD in mixed coordinates is proposed firstly.The data search for energy integration is conducted based on an accurate model in the x-y plane while target energy is integrated based on pseudo-spectrum in sensor coordinates.The algorithm performance is improved since the model mismatch is eliminated,and the pseudo-spectrum based integration facilitates well maintained target envelope.The detailed multiframe integration procedure and theoretical target integrated envelope are derived.Secondly,to cope with the unknown target velocity,a velocity filter bank based on pseudo-spectrum in mixed coordinates is proposed.The effect of velocity mismatch on algorithm performance is analyzed and an efficient method for filter bank design is presented.Thirdly,a parameter estimation method using characteristics of integrated envelope is presented for improved target polar position and Cartesian velocity estimation.Finally,numerical results are provided to demonstrate the effectiveness of the proposed method.