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target parameter estimation for OTFS-integrated radar and communications based on sparse reconstruction preprocessing

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Frontiers of Information Technology & Electronic Engineering 2024年第5期25卷 742-754页

作者： Zhenkai ZHANG Xiaoke SHANG Yue XIAO Ocean College Jiangsu University of Science and TechnologyZhenjiang 212003China

Orthogonal time-frequency space(OTFS)is a new modulation technique proposed in recent years for high Doppler wireless *** solve the parameter estimation problem of the OTFS-integrated radar and communications system,we propose a parameter estimation method based on sparse reconstruction preprocessing to reduce the computational effort of the traditional weighted subspace fitting(WSF)***,an OTFS-integrated echo signal model is ***,the echo signal is transformed to the time domain to separate the target angle from the range,and the range and angle of the detected target are coarsely estimated by using the sparse reconstruction ***,the WSF algorithm is used to refine the search with the coarse estimate at the center to obtain an accurate *** simulations demonstrate the effectiveness and superiority of the proposed parameterestimation algorithm.

关键词： Integrated radar and communications system Orthogonal time-frequency space target parameter estimation Sparse reconstruction Weighted subspace fitting

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Integrated Sensing and Communication With Massive MIMO: A Unified Tensor Approach for Channel and target parameter estimation

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2024年第8期23卷 8571-8587页

作者： Zhang, Ruoyu Cheng, Lei Wang, Shuai Lou, Yi Gao, Yulong Wu, Wen Ng, Derrick Wing Kwan Nanjing Univ Sci & Technol Sch Elect & Opt Engn Key Lab Near Range RF Sensing ICs Microsyst NJUST Minist Educ Nanjing 210094 Peoples R China Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310027 Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518055 Peoples R China Harbin Inst Technol Coll Informat Sci & Engn Weihai 264209 Peoples R China Harbin Inst Technol Sch Elect & Informat Engn Harbin 150001 Peoples R China Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW 2052 Australia

Benefitting from the vast spatial degrees of freedom, the amalgamation of integrated sensing and communication (ISAC) and massive multiple-input multiple-output (MIMO) is expected to simultaneously improve spectral and energy efficiencies as well as the sensing capability. However, a large number of antennas deployed in massive MIMO-ISAC raises critical challenges in acquiring both accurate channel state information and target parameter information. To overcome these two challenges with a unified framework, we first analyze their underlying system models and then propose a novel tensor-based approach that addresses both the channel estimation and target sensing problems. Specifically, by parameterizing the high-dimensional communication channel exploiting a small number of physical parameters, we associate the channel state information with the sensing parameters of targets in terms of angular, delay, and Doppler dimensions. Then, we propose a shared training pattern adopting the same time-frequency resources such that both the channel estimation and target parameter estimation can be formulated as a canonical polyadic decomposition problem with a similar mathematical expression. On this basis, we first investigate the uniqueness condition of the tensor factorization and the maximum number of resolvable targets by utilizing the specific Vandermonde structure. Then, we develop a unified tensor-based algorithm to estimate the parameters including angles, time delays, Doppler shifts, and reflection/path coefficients of the targets/channels. In addition, we propose a segment-based shared training pattern to facilitate the channel and target parameter estimation for the case with significant beam squint effects. Simulation results verify our theoretical analysis and the superiority of the proposed unified algorithms in terms of estimation accuracy, sensing resolution, and training overhead reduction.

关键词： Channel estimation Sensors estimation Training parameter estimation Tensors Wireless communication Integrated sensing and communication massive MIMO channel estimation target parameter estimation tensor decomposition

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Deep Unfolding Network for target parameter estimation in OTFS-based ISAC Systems 25

Deep Unfolding Network for Target Parameter Estimation in OT...

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IEEE Wireless Communications and Networking Conference (IEEE WCNC)

作者： Lin, Weizhi Zheng, Haifeng Feng, Xinxin Chen, Youjia Fuzhou Univ Coll Phys & Informat Engn Fujian Key Lab Intelligent Proc & Wireless Transm Fuzhou 350108 Peoples R China

ISBN: (纸本)9798350303582;9798350303599

target parameter estimation in high-speed scenarios is one of the main challenges in the integrated sensing and communication (ISAC) systems. In an ISAC system, the orthogonal time frequency space (OTFS) signal is able to successfully combat time-frequency-selective channels since the channel exhibits significant delay-Doppler (DD) sparsity characteristic. In this paper, we investigate the problem of parameter estimation of moving targets using OTFS modulation. We firstly derive signal model in the DD domain equivalent channel and recast the problem of parameter estimation into a compressed sensing (CS) problem. In order to improve the estimation performance, we then propose ADMM-Net by deep unfolding the iterations of the Alternating Direction Method of Multipliers (ADMM) algorithm into a deep learning network. Experimental results demonstrate that the proposed ADMM-Net algorithm outperforms the other methods in terms of estimation accuracy and running time for OTFS-based parameter estimation.

关键词： Orthogonal Time Frequency Space (OTFS) integrated sensing and communication target parameter estimation deep unfolding network ADMM

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Multidimensional Spectral Analysis for Radar target parameter estimation Survey and Benchmarks 11

Multidimensional Spectral Analysis for Radar Target Paramete...

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11th European Radar Conference (EuRAD)

作者： Gierlich, Roland Airbus Def & Space Radar Signal Proc Grp Ulm Germany

ISBN: (纸本)9782874870378

Most state-of-the-art radar processors are based on sequential, one-dimensional signal processing stages. In this paper we investigate the suitability of modern multidimensional spectral analysis methods for the estimation of radar target parameters. A short survey of both classical and more advanced high-and superresolution multidimensional spectrum estimation techniques like 2D-AR, 2D-Capon, and 2D-MUSIC is given. The performance of these techniques is evaluated by systematic benchmarks, including sensitivity towards noise, spectral resolution, high signal dynamics, and multi-tone capacity. The benchmark results are assessed with regard to joint spatial and temporal radar parameter estimation.

关键词： multidimensional spectral analysis high-resolution spectrum estimation target parameter estimation

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Research on Anti-Range Migration Sparse Reconstruction Algorithm in target parameter estimation of Frequency-Agile Radar

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2023年 72卷 1-13页

作者： Wang, Hao Wang, Feng Hohai Univ Coll Comp & Informat Nanjing 211100 Peoples R China

The frequency agile radar (FAR) target parameter estimation scenario based on sparse reconstruction faces the problem of high-resolution range migration (HRRM), which corresponds to phase interference that matches the target atoms in the dictionary matrix and forms a pseudopeak structure. In this article, we propose an anti-range migration sparse reconstruction (ARMSR) algorithm for recovering the range-Doppler parameters of high-velocity targets. The main feature of this algorithm is its hybrid processing structure, which introduces 1-D high-resolution IFFT processing into the sparse recovery model. Based on the range imaging gain compensated by different Doppler phases, the velocity index set is preliminarily determined, and then the compensation function for the range migration phase is calculated. Using the measurement vector compensated by the migration component as input, a subdictionary matrix was constructed at super-resolution intervals in the neighborhood of the range profile and velocity index set. The proposed algorithm effectively overcomes the influence of range migration components and reduces pseudo peaks in conventional sparse reconstruction planes. In addition, the proposed algorithm performs parameter estimation in a high-resolution neighborhood space, resulting in improved computational efficiency and resolution of target parameters. The simulation results demonstrate that the proposed algorithm has significant reconstruction advantages in scenarios with high-velocity targets and super-resolution.

关键词： Frequency agile radar (FAR) high-resolution range migration (HRRM) high-resolution range profile (HRRP) sparse reconstruction algorithm target parameter estimation

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A target parameters estimation Method for CP-OTFS-Based Terrestrial Radar System

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IEEE WIRELESS COMMUNICATIONS LETTERS 2025年第1期14卷 233-237页

作者： Miao, Qilong Liang, Jing Zhang, Ge Han, Jiachen Univ Elect Sci & Technol China Chengdu 610056 Peoples R China

Orthogonal time-frequency space (OTFS) modulation, which exhibits significant advantages in high-speed mobile communication scenarios, holds strong potential in the field of radar applications. The existing state-of-the-art target parameter estimation methods for cyclic prefix OTFS (CP-OTFS) based radar systems suffer from inaccuracies in target scattering coefficient (TSC) estimation under conditions of multiple targets, low bandwidth and short duration. In this letter, the equivalent matched filter (EMF) algorithm is designed. Compared to the matched filter (MF) algorithm, EMF intuitively explains the input-output relationship of CP-OTFS based radar, but it remains susceptible to interference from multiple targets. In order to mitigate this impact, the two-stage matched filter-based minimum mean squared error (TS-MF-MMSE) algorithm, a refined TSC estimation algorithm for terrestrial CP-OTFS based radar system is proposed. Simulation results demonstrate the superior performance of proposed algorithm in TSC estimation in comparison with EMF and the state-of-the-art.

关键词： Radar estimation Signal processing algorithms Symbols Matched filters Receivers OFDM Delays Wireless communication Spaceborne radar CP-OTFS target parameter estimation JRC minimum mean squared error

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parameter estimation in PMCW MIMO Radar Systems With Few-Bit Quantized Observations

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2022年 70卷 810-821页

作者： Wu, Chao-Yi Zhang, Tianyi Li, Jian Wong, Tan F. Univ Florida Dept Elect & Comp Engn Gainesville FL 32611 USA

We consider the problem of target parameter estimation in phase modulated continuous wave (PMCW) multiple-input multiple-output (MIMO) radar systems with quantized observations. We derive the Cramer-Rao bound (CRB) for jointly estimating targets' amplitudes, time delays, Doppler shifts, and directions. The derived bound provides an efficient method to analyze the estimation performance achieved by different quantization schemes. We also devise the maximum likelihood (ML) estimator for the considered parameters, and a direct grid-based method (DGM) is introduced to obtain the ML estimates. To obtain the ML estimates more efficiently, a two-stage scheme is proposed. In the proposed scheme, we first formulate the estimation problem as a sparse signal recovery problem and modify the sparse learning via iterative minimization (SLIM) approach to solve it. Next, a RELAX-based iterative algorithm is proposed to refine the estimates. Simulation results show that the proposed scheme can approach the CRB. Simulation results also show that using quantized observations does not affect the estimation performance significantly when the targets' amplitudes are similar.

关键词： Quantization (signal) Radar Maximum likelihood estimation MIMO radar Radar imaging Standards Sensors Millimeter wave phase modulated continuous wave radar multiple-input multiple-output target parameter estimation sparse signal recovery Cramer-Rao bound

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A Sparse Reconstruction Algorithm Based on Constrained Inhomogeneous Grid Optimization

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CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2023年第8期42卷 4809-4835页

作者： Wang, Hao Wang, Feng Hohai Univ Coll Comp & Informat West Focheng Rd 8 Nanjing 211100 Peoples R China

The dictionary grid mismatch problem in sparse processing is an important factor affecting parameter reconstruction. The existing grid correction algorithms are limited by the initial grid division and the accurate estimation of the support set vector. Furthermore, these methods fail if multiple scattering points fall into the same grid interval. In this paper, a constrained inhomogeneous grid optimization sparse processing algorithm is proposed. The proposed method includes two sub-processes, namely initial grid optimization and the support set vector correction. The optimization of the initial grid is to use the idea of the coordinate descent method to iteratively generate an updated grid vector, which is used for the inhomogeneous fission of the grid around the target from coarse to dense. Moreover, under the constraint of dictionary atom maximum correlation, multiple scattering points are separated in different grids, which improves the search accuracy of support set atoms. The correction process of the support set vector is to use the first-order Taylor expansion of the dictionary grid to linearly approximate the real parameters of the target, to achieve adaptive correction of dictionary mismatched atoms. According to the simulation experiment of the frequency agile radar scenario, the proposed algorithm can achieve higher range-Doppler parameter joint estimation accuracy in the multi-scattering point scenario in comparison with the conventional sparse recovery algorithm and grid point correction algorithm.

关键词： Sparse signal processing Off-grid Grid optimization target parameter estimation

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Fast coherent integration method for moving target detection with random PRI variation

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ELECTRONICS LETTERS 2020年第1期56卷 41-+页

作者： Zhu, Weiqiang Liu, Zhiling Xu, Huajian Nanjing Elect Equipment Inst Nanjing 210007 Jiangsu Peoples R China

This Letter addresses the coherent integration problem for the moving target detection in a pulse repetition interval (PRI)-staggered radar system. Due to the random sampling, the complex coupling effects between range and azimuth will severely degrade the target parameter estimation and coherent integration performance. To deal with this issue, a fast coherent integration detection method with random azimuth sampling is proposed, where range migration is firstly compensated by applying the range-frequency reversal transform. Then. the random phase fluctuation caused by the staggered PRI can be efficiently removed by applying the non-uniform FFT technique. Finally, a moving target can be well focused. Simulated results are provided to validate the effectiveness of the proposed algorithm.

关键词： radar signal processing target parameter estimation fast coherent integration method object detection Radar equipment, systems and applications fast coherent integration detection method coherent integration performance fast Fourier transforms coherent integration problem radar imaging random PRI variation complex coupling effects random azimuth sampling radar detection Doppler radar Optical, image and video signal processing moving target detection Integral transforms range-frequency reversal range migration random phase fluctuation Signal processing and detection parameter estimation random sampling pulse repetition interval-staggered radar system staggered PRI

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Interference-to-noise ratio estimation in long-term evolution passive radar based on cyclic auto-correlation

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ELECTRONICS LETTERS 2021年第9期57卷 375-377页

作者： Wang, Haitao Lyu, Xiaoyong Zhong, Liping Guilin Univ Elect Technol Sch Informat & Commun Guilin Peoples R China Zhengzhou Univ Sch Informat Engn Zhengzhou 450001 Peoples R China

This letter considers the interference-to-noise ratio (INR) estimation problem in long-term evolution (LTE)-based passive bistatic radar. The INR is of great importance in the evaluation of interference level, the prediction of the target detection capability and the target parameter estimation. Traditional passive radar uses the cross-correlation function between the reference and surveillance signals to estimate the INR. This is not applicable to LTE-based passive radar due to the uncorrelation between the interference and reference signals in LTE-based passive radar. In this letter, we propose a cyclic auto-correlation method to estimate the INR. Simulation results show the effectiveness of the proposed method.

关键词： interference level interference-to-noise ratio estimation problem LTE-based passive radar target detection capability Signal processing and detection INR estimation problem traditional passive radar long-term evolution passive radar Long Term Evolution Mobile radio systems cross-correlation function Radar equipment, systems and applications passive radar surveillance signals object detection correlation methods Electromagnetic compatibility and interference Radar theory target parameter estimation cyclic auto-correlation parameter estimation radiofrequency interference

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