Direction finding and localization for far-field sources with near-field multipath reflections

Elbir, Ahmet M.; Tuncer, T. Engin

doi:10.1109/dsp-spe.2015.7369540

Cited by 4 publications

(6 citation statements)

References 13 publications

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“…In [159,160], a two-step method for estimating the DOA of an FFS and localizing its NF multipath reflections is presented. In the first step, by using a calibration technique, the DOA of the FFS is estimated.…”

Section: A Multipath Environmentmentioning

confidence: 99%

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A Comprehensive Review of Direction-of-Arrival Estimation and Localization Approaches in Mixed-Field Sources Scenario

Molaei,

Zakeri,

Andargoli

et al. 2024

IEEE Access

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Direction-of-arrival (DOA) estimation plays a crucial role in array signal processing across various domains, including radar, sonar, wireless communications, and seismic exploration. However, traditional DOA techniques often assume either far-field (FF) or near-field (NF) propagation, limiting their applicability in scenarios involving mixed-field sources. DOA estimation and localization in scenarios involving mixed NF and FF sources is a complex and dynamic field that has garnered significant research attention in recent years. This multifaceted and evolving area holds promise for addressing challenges in radar, wireless communications, and acoustic sensing applications. This review paper provides a comprehensive overview of the methodologies, techniques, and advancements in this domain. We categorize existing methodologies, discussing their advantages and limitations. Furthermore, we delve into the mathematical modeling of mixed-field sources and essential signal processing techniques for parameter estimation. Special attention is given to technical issues such as aperture loss, computational complexity, and hardware considerations. The paper discusses the various sources of noise in the mentioned scenario and highlights the importance of modeling noise accurately for effective estimation. It also explores different scenarios and assumptions considered in the literature, ranging from non-Gaussian and nonstationary noise environments to scenarios involving multipath propagation and unknown mutual coupling effects. A detailed examination of the statistical approaches used in DOA estimation and localization reveals a diverse range of methods, including higher-order statistics and second-order statistics, each with its own advantages and applications. A comparative evaluation of various approaches highlights their performance in terms of estimation accuracy, resolution, aperture loss and computational efficiency. This provides insights into the trade-offs involved in choosing between different approaches. The review also identifies promising future research directions, such as the exploration of advanced signal processing techniques like compressive sensing and deep learning, exact NF modeling, estimation based on one-bit measurements, the integration of polarization diversity, employing metasurface antennas, tracking parameters, and the utilization of full-wave or experimental data for a more realistic representation of the challenges. By reviewing advances in methodologies and techniques, as well as outlining future research directions aimed at addressing the complexities of mixed-field scenarios, this paper paves the way for the development of more robust and reliable localization systems capable of handling real-world complexities.

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Section: A Multipath Environmentmentioning

confidence: 99%

“…It exploits a UCA for 2-D DOA estimation and a virtual uniform linear array (ULA) structure for NF multipath localization. The approach presented in [159,160] is not able to resolve multiple sources and is applicable only in the presence of one FFS.…”

Section: A Multipath Environmentmentioning

confidence: 99%

A Comprehensive Review of Direction-of-Arrival Estimation and Localization Approaches in Mixed-Field Sources Scenario

Molaei,

Zakeri,

Andargoli

et al. 2024

IEEE Access

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“…Following [15], for a set of antennas {p i = [x i , y i , z i ] }, and a point narrow-band source at azimuth and elevation ξ = [φ, θ], and range r, the geometric near-field phases are modeled by…”

Section: A Signal Model For Mutually Incoherent Array Signalsmentioning

confidence: 99%

“…To study the benefit of coupling the reconstructions at different points of the trajectory via the group-sparsity constraint for the partition (15), we compare in the next section the performance with the alternative approach of solving the sparse reconstructions (14) individually at each distance to the obstacle with the group partition (12). We call this approach step-by-step ("SbyS") range fusion, versus the "GS" approach.…”

Section: Incoherent Fusion Across Filtering Intervalmentioning

confidence: 99%

“…We call this approach step-by-step ("SbyS") range fusion, versus the "GS" approach. The height map in ( 13) is averaged over the points in the trajectory in both cases, but the group-sparsity constraint (15) that encourages the hypothesis of height to be the same across distances is only included in the "GS" approach.…”

Section: Incoherent Fusion Across Filtering Intervalmentioning

confidence: 99%

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Height estimation for automotive MIMO radar with group-sparse reconstruction

Simoni¹,

Mateos-Nunez²,

González-Huici³

et al. 2019

Preprint

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A method is developed for sequential azimuth and height estimation of small objects at far distances in front of a moving vehicle using coherent or mutually incoherent MIMO arrays. The model considers phases and amplitudes of superposition of near-field multipath signals produced by specular non-diffusive ground-reflections. The reflection phase shift and power attenuation due to the interaction with the ground is assumed unknown and is estimated jointly. Groupsparsity allows combining measurements along the trajectory of the vehicle provided that the road is flat as well as measurements from multiple incoherent sensors at different locations in the vehicle. This model can be formulated for non-uniform sparse arrays in 2D and 3D with subsets of antennas at the same height and the resulting inverse problem can be approximated with efficient methods such as Block Orthogonal Matching Pursuit. It is shown in simulations that the proposed approach significantly increases estimation accuracy and decreases false alarms, both crucial for the detection of small objects at far distances.

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Design of mutually incoherent arrays for DoA estimation via group-sparse reconstruction

Mateos-Nunez¹,

Simoni²,

González-Huici³

et al. 2019

2019 IEEE Radar Conference (RadarConf)

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Direction finding and localization for far-field sources with near-field multipath reflections

Cited by 4 publications

References 13 publications

A Comprehensive Review of Direction-of-Arrival Estimation and Localization Approaches in Mixed-Field Sources Scenario

A Comprehensive Review of Direction-of-Arrival Estimation and Localization Approaches in Mixed-Field Sources Scenario

Height estimation for automotive MIMO radar with group-sparse reconstruction

Design of mutually incoherent arrays for DoA estimation via group-sparse reconstruction

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