Two‐dimensional DOA estimation for noncoherent and coherent signals using subspace approach

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A new multistage direction estimation (MSDE) method using two parallel uniform linear arrays (ULAs) is proposed, which estimates two‐dimensional (2D) direction‐of‐arrivals (DOAs) of a mixture of noncoherent and multigroup of coherent signals, sequentially. At first, we estimate 2‐D DOAs of noncoherent signals, next, utilize an iterative procedure to make the estimation accuracy better, then omit the effect of noncoherent signals by the oblique projectors. Now, to estimate the 2D DOAs of each group separately, the group with the minimum coherent signals is decorrelated by the forward spatial smoothing, then an initial 2D DOA estimation is calculated and the estimation accuracy of this group is improved by using an iterative procedure. Afterward, the information of the signals whose DOAs are estimated, is omitted by oblique projectors. We repeat in the same way until the 2D DOAs of all coherent groups are estimated. It is shown in the simulation results section that the proposed method has good performance.

Section: Simulation Resultsmentioning

confidence: 97%

Section: Simulation Resultsmentioning

confidence: 99%

Section: Complexity Comparisonmentioning

confidence: 99%

“…Thus the computation flops is about

ℓ_{3} [2 M^{2}] t_{n} + 𝒪 (ℓ_{2} ℓ_{3} M^{3} K_{M}) + 𝒪 (ℓ_{1} M^{3} K_{n}^{2}),

where

K_{M} = \max {K_{p}}

for p = 1, 2, … , P . Also, according to the computational complexity of PUMA, 15 OPADE, 16 and FBSS‐DPR 8,21 calculated in detail in Reference 15, the computational flops of PUMA 15 is about

5 M^{2} t_{n} + 𝒪 (ℓ_{5} (K_{n} + P) {(M - K + 1)}^{3}) + 𝒪 (ℓ_{4} {(M - K)}^{3} {(K_{n} + P)}^{2})

, where

ℓ_{5} \approx 𝒪 (K)

and

ℓ_{4} \approx 𝒪 (1)

, respectively. The computational flops of OPADE 16 is

[2 (2 M - (K_{n} + P)) (K_{n} + P) +

…”

Section: Complexity Comparisonmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Multistage direction‐of‐arrival estimation approach for noncoherent and multigroup coherent signals

Shahimaeen

Dehghani

2020

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Subspace leakage reduced nonuniform sparse Fibonacci‐like planar array to estimate azimuth and elevation angles of both Angle of Arrival and Angle of Departure

Vethanayagam¹,

Narasimhan²

2020

Estimation of azimuth and elevation angles of both Angle of Arrival and Angle of Departure is a challenging research topic in 5G smart antenna technology. The key contributions of this work involve two novel algorithms implemented in a uniform planar array for the estimation of the arrival and departure angles using the received signal from the coherent users. One of the two algorithms reduces search complexity without any reduction in the angular resolution of the estimated angles. This is done through selection of active antennas in the uniform planar array to make it sparse without creating angle ambiguity. The second method improves the angular resolution in estimating the angles through reduction in the subspace leakage that occurs between the signal and noise subspace. Numerical simulations prove the proposed two algorithms producing a high angular resolution with reduced complexity.

Performance analysis of subspace‐based spatio‐temporal spectral estimator

Amirsoleimani

Olfat

2022

In this article, the performance analysis of subspace‐based spatial‐temporal spectral estimation is investigated. Subspace methods adopt an eigenstructure approach to estimate the array observation power in the frequency‐direction (fprefix−θ$$ f-\theta $$) plane. This approach is used in many wideband array processing techniques using spatial‐temporal covariance matrix (STCM). In this article, the performance of subspace‐based algorithms is addressed in case of two plane waves, separated in direction of arrival (DOA) and frequency, impinging on a linear array. The analysis on one‐dimensional (f$$ f $$ or θ$$ \theta $$) subspace‐based methods are extended to the two‐dimensional case (fprefix−θ$$ f-\theta $$). Then, an analytic formulation for the expected value of the null spectrum of the observations' spatial‐temporal spectral is presented. Accordingly, two resolution criteria for resolving two plane waves are discussed, and corresponding analytic relations are derived and verified through simulations. In this analysis, the effect of temporal lag order, number of snapshots, and signal‐to‐noise ratio (SNR) in DOA and frequency resolution are well shown. Finally, two conceptual examples of practical design scenarios are presented.