Joint Azimuth-Elevation/(-Range) Estimation of Mixed Near-Field and Far-Field Sources Using Two-Stage Separated Steering Vector-Based Algorithm

Liang, Junli; Ding, Lijian; Zeng, Xianju; Wang, Wenyi; Zhang, Jiulong; Chen, and Hongyang

doi:10.2528/pier10110104

Cited by 18 publications

(17 citation statements)

References 44 publications

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“…In part 2, the performances of the two best techniques among them are compared with existing traditional algorithms [10,11] by using MSE as a figure of merit. Every time, the number of sensors in both subarrays is taken to be same.…”

Section: Resultsmentioning

confidence: 99%

“…In this subsection, we compared the RMSE of DE-ASA and PSO-ASA with existing nonheuristic techniques [10,11] in the presence of noise. We considered two sources,…”

Section: Comparison With Other Techniques Using Root Mean Square Erromentioning

confidence: 99%

“…The other properties of this fitness function are its ease in concept, ease in implementation, robustness against noise, and requirement of single snapshot. The performances of PSO, DE, Particle Swarm Optimization hybridized with Active Set Algorithm (PSO-ASA), and Differential Evolution hybridized with Active Set Algorithm (DE-ASA) are not only compared with each other but also with some traditional techniques available in literature [10,11] using RMSE as figure of merit.…”

Section: Introductionmentioning

confidence: 99%

“…In [8][9][10] different schemes are discussed but they not only are computationally expensive but also have the problem of pair matching between elevation and azimuth angles. To solve the pair matching problem, a two-stage separated steering vectorbased algorithm is proposed in [11] but it has higher MSE and is computationally expensive as it requires more than 400 snapshots to achieve the results. It also fails to estimate the amplitude of sources which is also sometimes an important parameter to be estimated.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Multiple Target Localization with Bistatic Radar Using Heuristic Computational Intelligence Techniques

Zaman

Qureshi

Rehman

et al. 2015

International Journal of Antennas and Propagation

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on its receiver. Let the transmitter of this Bistatic radar send coherent signals using a subarray that gives a fairly wide beam with a large solid angle so as to cover up any potential relevant target in the near field. We developed Heuristic Computational Intelligence (HCI) based techniques to jointly estimate the range, amplitude, and elevation and azimuth angles of these multiple targets impinging on the CSCA. In this connection, first the global search optimizers, that is,are developed separately Particle Swarm Optimization (PSO) and Differential Evolution (DE) are developed separately, and, to enhance the performances further, both of them are hybridized with a local search optimizer called Active Set Algorithm (ASA). Initially, the performance of PSO, DE, PSO hybridized with ASA, and DE hybridized with ASA are compared with each other and then with some traditional techniques available in literature using root mean square error (RMSE) as figure of merit.

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Section: Resultsmentioning

confidence: 99%

“…In this subsection, we compared the RMSE of DE-ASA and PSO-ASA with existing nonheuristic techniques [10,11] in the presence of noise. We considered two sources,…”

Section: Comparison With Other Techniques Using Root Mean Square Erromentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Multiple Target Localization with Bistatic Radar Using Heuristic Computational Intelligence Techniques

Zaman

Qureshi

Rehman

et al. 2015

International Journal of Antennas and Propagation

View full text Add to dashboard Cite

show abstract

“…The main work of array signal processing is the estimation of parameters and signals by utilizing temporal and spatial information from the samples of receive antenna arrays [2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Constrained Trilinear Decomposition With Application to Array Signal Processing

Liu¹,

Jiang²,

Yang³

et al. 2012

PIER

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Abstract-This paper links the constrained trilinear tensor model into array signal processing. The structure properties of baseband signal, such as the Constant-Modulus (CM) and Finite Alphabet (FA) structures which are already known in the receiving array, are exploited in trilinear decomposition. Two novel algorithms for constrained trilinear decomposition are proposed and applied to array signal processing. The distinguishing features of the proposed model and algorithms compared to the traditional trilinear signal processing methods are: (i) the proposed model has a better performance and lower computation complexity. (ii) it can still work well even if degeneracy of factors are involved in the data model, which is not valid in traditional algorithms. Simulation results are presented to illustrate the application of the constrained trilinear decomposition to array signal processing and evaluate the performance of the proposed algorithms in DOAs estimation.

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Far‐field DOA estimation and near‐field localization for multipath signals

Elbir

Tuncer

2014

Radio Science

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In direction finding and localization applications, multipath signals are important sources of error for parameter estimation. When the antenna array receives multipath reflections which are coherent with the far‐field line‐of‐sight signal, estimating the far‐ and near‐field components becomes an important problem. In this paper, a new method is proposed to estimate the direction‐of‐arrival (DOA) of the far‐field source and to localize its near‐field multipaths. Far‐field source DOA is estimated using calibration of the antenna array. A near‐to‐far transformation is proposed for the estimation of the near‐field source DOA angles. In order to estimate the near‐field range parameters, a compressive sensing approach is presented where a dictionary with near‐field sources with different ranges is employed. As a result, the proposed method estimates the far‐field and near‐field source DOAs as well as the range and the signal amplitudes of the near‐field sources. This method is evaluated using close‐to‐real world data generated by a numerical electromagnetic tool, where the array and transmitter are placed in an irregular terrain and array data are generated using full 3‐D propagation model. It is shown that unknown source parameters can be estimated effectively showing the potential of the proposed approach in applications involving high‐frequency direction finding and indoor localization.

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Joint Azimuth-Elevation/(-Range) Estimation of Mixed Near-Field and Far-Field Sources Using Two-Stage Separated Steering Vector-Based Algorithm

Cited by 18 publications

References 44 publications

Multiple Target Localization with Bistatic Radar Using Heuristic Computational Intelligence Techniques

Multiple Target Localization with Bistatic Radar Using Heuristic Computational Intelligence Techniques

Constrained Trilinear Decomposition With Application to Array Signal Processing

Far‐field DOA estimation and near‐field localization for multipath signals

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