Two-dimensional noisy autoregressive estimation with application to joint frequency and direction of arrival estimation

Amanat, Alireza; Mahmoudi, Alimorad; Hatam, Majid

doi:10.1007/s11045-017-0539-z

Cited by 4 publications

(3 citation statements)

References 12 publications

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“…Consequently, we can construct a (p+q−1)×(p+q) matrix D whose rows are d T i . By premultiplying (12) by D, and taking advantage of (14), we obtain…”

Section: Data Modelmentioning

confidence: 99%

Non-iterative Subspace-based Method for Estimating AR Model Parameters in the Presence of White Noise with Unknown Variance

Esfandiari

Vorobyov

Karimi

2019

2019 53rd Asilomar Conference on Signals, Systems, and Computers

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We consider the problem of estimating the parameters of autoregressive (AR) processes in the presence of white observation noise with unknown variance, which appears in many signal processing applications. A new non-iterative subspace-based method named extended subspace (ESS) method is developed. The basic idea of the ESS is to estimate the variance of the observation noise via solving a generalized eigenvalue problem, and then estimate the AR parameters using the estimated variance. The major advantages of the ESS method include excellent reliability and robustness against high-level noise, and also estimating the AR parameters in a non-iterative manner. Simulation results help to evaluate the performance of the ESS method, and demonstrate its robustness.

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“…Consequently, we can construct a (p+q−1)×(p+q) matrix D whose rows are d T i . By premultiplying (12) by D, and taking advantage of (14), we obtain…”

Section: Data Modelmentioning

confidence: 99%

Non-iterative Subspace-based Method for Estimating AR Model Parameters in the Presence of White Noise with Unknown Variance

Esfandiari

Vorobyov

Karimi

2019

2019 53rd Asilomar Conference on Signals, Systems, and Computers

View full text Add to dashboard Cite

show abstract

“…The paper by Amanat et al (2017) addressed the problem of two-dimensional autoregressive estimation in the presence of additive white noise. The estimation method is developed by combining the low-order and high-order Yule-Walker (YW) equations.…”

Section: Parameter Estimation Using Sensor Arraysmentioning

confidence: 99%

Array signal processing and systems

Liao

Madanayake

Agathoklis

2018

Multidim Syst Sign Process

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We are delighted to present this special issue of Multidimensional Systems and Signal Processing on Array Signal Processing and Systems. Sensor arrays play an important role in spatio-temporal signal processing with applications spanning across multiple fields such as electromagnetic, acoustics, ultrasonic and seismic processing systems. This plethora of possible applications has sparked a large number of new theoretical developments and array processing systems in the last few years.In the field of electromagnetic sensor (antenna) arrays, there is much interest in the electronically-steerable radio-frequency (RF) beams that can be obtained from phased-array antennas. In the microwave bands, phased-array antennas take both analog and digital signal processing approaches, and find applications in wireless base-stations, radar sensors, space communications, and radio telescopes. In the emerging mm-wave regime, modern applications of array processing are of paramount importance for wireless communications. In particular, emerging 5G systems based on massive-MIMO basestations depend on array processing to mitigate the effects of high path loss and blockages in an urban environment. Acoustic sensor (microphone) arrays are extremely useful for audio and multimedia applications, including high-fidelity sound recording, immersive multimedia and augmented/virtual reality. Ultrasound sensor arrays find extensive applications in biomedical engineering where they are used for imaging the human body and in structural health monitoring of airframes B Bin Liao

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“…Aiming at the problems existing in the joint estimation algorithm of frequency and azimuth parameters, many scholars have carried out in-depth research. The authors of [13] constructed a two-dimensional autoregressive model based on observational data and estimated the signal frequency and azimuth according to a two-dimensional linear prediction. Reference [14] presented the joint estimation of the frequency and azimuth via establishing the matrix of direction of the azimuth, which requires less computation.…”

Section: Introductionmentioning

confidence: 99%

A Frequency–Azimuth Spectrum Estimation Method for Uniform Linear Array Based on Deconvolution

Lu,

Cai,

Guo

et al. 2024

Remote Sensing

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The frequency–azimuth (FRAZ) spectrum is a critical characteristic in passive target detection and tracking, as it encapsulates information regarding the signal’s frequency and azimuth. However, due to the inherent limitations in the sonar array’s physical aperture and the analysis time of the system, the signal often suffers from undersampling in both spatial and temporal dimensions. This undersampling leads to energy leakage across the azimuth and frequency domains, adversely affecting the resolution of the FRAZ spectrum. Such a reduction in resolution hampers multitarget resolution and feature extraction. To address these challenges, this study introduces a deconvolution-based FRAZ spectrum estimation method tailored for uniform linear arrays. The proposed method initiates by decoupling the azimuth and frequency in the FRAZ spectrum, forming a two-dimensional point scattering function that possesses shift-invariance. Subsequent to this, the power spectrum and the two-dimensional point scattering function undergo deconvolution using the Richardson–Lucy (R–L) iterative algorithm. The final stage involves calculating the signal azimuths and frequencies based on the deconvolution results from the preceding step. Comparative analyses involving simulations and sea test results reveal that the proposed method achieves a narrower main lobe width and diminished background noise in contrast to traditional FRAZ spectrum estimation techniques. This improvement is instrumental in minimizing the target’s energy leakage in both the azimuth and frequency domains.

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Two-dimensional noisy autoregressive estimation with application to joint frequency and direction of arrival estimation

Cited by 4 publications

References 12 publications

Non-iterative Subspace-based Method for Estimating AR Model Parameters in the Presence of White Noise with Unknown Variance

Non-iterative Subspace-based Method for Estimating AR Model Parameters in the Presence of White Noise with Unknown Variance

Array signal processing and systems

A Frequency–Azimuth Spectrum Estimation Method for Uniform Linear Array Based on Deconvolution

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