Altitude measurement of low elevation target in complex terrain based on orthogonal matching pursuit

Li, Cunxu; Chen, Baixiao; Zheng, Yingbin; Yang, Minglei

doi:10.1049/iet-rsn.2016.0468

Cited by 15 publications

(6 citation statements)

References 23 publications

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“…Start a new iteration until a predefined convergence criterion is satisfied or the maximum number of iterations is reached. Since the convergence property of the EM algorithm [28] guarantees that the cost function

E (}{, \ln p ()(, X, \overset{false~}{bold-italicS}, α_{0}, α, γ))

increases at each process, the SBL framework guarantees strong sparsity of the solution.…”

Section: Proposed Algorithmmentioning

confidence: 99%

“…For DOA estimation in the presence of non-uniform noise, an improved SBL is applied to introduce priori sparsity and avoid model error in [27]. A block SBL developed in [28] was used to deal with the temporal correction caused by closely spaced correlated signals. Such an application indicates the flexibility of SBL in modelling complex structure and its excellent properties to reduce the structure and convergence errors in total.…”

Section: Introductionmentioning

confidence: 99%

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Altitude measurement of low‐elevation target for VHF radar based on weighted sparse Bayesian learning

Chen

Yang

et al. 2018

IET signal process.

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In this study, a novel method is proposed to deal with the problem of altitude measurement of a low-elevation target for very high frequency radars in complex terrains. The problem typically concerns about the direction-of-arrival (DOA) estimation method for closely spaced and correlated signals arose by multipath propagation, in which the multipath signal would be modulated by the rough and irregular reflecting surface, result in amplitude and phase perturbation and energy fluctuations of the receiving signals. A perturbation multipath propagation model is derived where the influence of irregular reflecting in complex terrain is taken as a random perturbation of the multipath signal. The spatial sparsity of the receiving signal is then exploited, and a weighted sparse Bayesian learning method is proposed to estimate both the random perturbation coefficients and DOA of the incident signal with high precision. Both the computer simulations and real data analysis indicate the efficiency and superior performance of the proposed method in dealing with altitude measurement in complex terrain.

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E (}{, \ln p ()(, X, \overset{false~}{bold-italicS}, α_{0}, α, γ))

increases at each process, the SBL framework guarantees strong sparsity of the solution.…”

Section: Proposed Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Altitude measurement of low‐elevation target for VHF radar based on weighted sparse Bayesian learning

Chen

Yang

et al. 2018

IET signal process.

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“…Reference [12] combined the alternating projection technique with the MUSIC algorithm, and acquired prior information to achieve low elevation angle estimation. Since the hybrid algorithm's cost function is a non-convex optimization problem, it cannot constantly guarantee that the algorithm converges to the global minimum optimal solution.ML algorithms can directly process coherent signals with perfect estimation performance under the condition of low signal-to-noise ratio(SNR), but the calculation consumption of the algorithm increased exponentially with the rising number of targets, and the consumption of computational calculation is huge, which makes it difficult to meet the real-time requirements [13]. Compressed sensing algorithms are able to directly estimate the DOA of coherent sources by utilizing the sparse characteristics of the target in the spatial domain, and most sparse reconstruction-based DOA estimation methods have better estimation performance under the condition of low signal-to-noise ratio (SNR) [11].…”

Section: Introductionmentioning

confidence: 99%

DOA Estimation Method in Echo-localization Ability for Human-beings

Ni¹

2022

HSET

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The principle of echo-localization processing of human beings is quite similar with animals like bats and dolphins, which has been widely applied in sonar. Direction of Arrival (DOA) methods plays an important role in estimating the radar, sonar and many other echo-localization communication fields. Typical methods in resolving echo-localization problems including classical (Multiple Signal Classification) MUSIC, (Estimation of Signal Parameter via Rotational Invariance Technique), ESPRIT and compressed sensing. The echo-localization methods and the resolving ambiguity methods via uniform array are systematically introduced in this paper. On this basis, the fast and accurate algorithms for human-being echo-localization in parameter (azimuth angle, elevation angle and range) estimation of signal are further introduced from time domain and frequency domain. The accuracy of the angle and distance estimation of the algorithm calculated by computer simulation experiment is over 99.9% and 97% respectively. The effectiveness of DOA method was verified according to MUSIC algorithm, which provides a rapid and accuracy information in echo-localization ability of human being.

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“…In practical applications, multiple reflection paths exist simultaneously in the complex terrain environment and the prior information is difficult to measure correctly. To deal with this problem, in [34,35], the influence of varied topography is regarded as a random perturbation, and the target altitude is measured by recovering signals using the sparse Bayesian learning (SBL) method or orthogonal matching pursuit method. Similarly, in [36,37], after establishing the multipath signal model in a complex environment with varied topography, the authors first estimate the main component of the signal subspace or the hybrid steer vector in which the information of target elevation angle and reflected signals directions are included.…”

Section: Introductionmentioning

confidence: 99%

Low elevation target altitude measurement for ubiquitous radar based on known transmitted waveform and sparse representation

Guo

Peng

Tian

et al. 2021

IET Radar Sonar & Navi

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The multipath effect deteriorates the performance of low elevation target altitude estimation with a ubiquitous radar strongly, especially in complex terrain environments in which multiple reflection paths exist simultaneously. This work shows how the authors reconstructed a multipath model for ubiquitous radar with a known waveform and took advantage of it to improve the altitude measurement performance when locating low‐altitude small targets in complex terrain environments. The authors first present a multipath propagation model for target elevation angle estimation in which not only received multipaths but also transmitted multipaths are taken into consideration. Then, a reconstructed elevation angle estimation model in which the waveform is known and the target elevation information is as same as the original model is derived. Finally, an innovative low elevation angle estimation algorithm based on a maximum likelihood estimator and a sparse representation is proposed. Simulations and experiment results verify that the proposed algorithm has a much more robust low elevation angle estimation performance than existing methods in complex terrain environments. Index Terms—Target altitude measurement, complex multipath effect, known transmitted waveform, sparse representation, ubiquitous radar.

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Altitude measurement of low elevation target in complex terrain based on orthogonal matching pursuit

Cited by 15 publications

References 23 publications

Altitude measurement of low‐elevation target for VHF radar based on weighted sparse Bayesian learning

Altitude measurement of low‐elevation target for VHF radar based on weighted sparse Bayesian learning

DOA Estimation Method in Echo-localization Ability for Human-beings

Low elevation target altitude measurement for ubiquitous radar based on known transmitted waveform and sparse representation

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