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Advances in Multichannel Radar Systems

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Radar Sensors".

Deadline for manuscript submissions: 25 August 2026 | Viewed by 4118

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Dr. Wenchao Li

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Guest Editor

School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: forward-looking radar; new system radar signal processing; inverse problem
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Multichannel radar refers to a radar with multiple transmitting or receiving channels, which can exist in various forms such as single-input multi-output (SIMO), multi-input single-output (SIMO) and multi-input multi-output (MIMO). Due to its higher degree of freedom, multichannel radar has significant advantages in fields such as object detection and imaging, and it has received widespread attention in recent years.

For example, multichannel radar can perform instantaneous single snapshot processing with high processing efficiency, but often faces the problems of poor angular resolution performance caused by small aperture size and grating lobe suppression introduced by sparse channel arrangement. At the same time, multichannel radar can also be installed on motion platforms for coherent processing, such as coherent accumulation detection, synthetic aperture radar (SAR) imaging, etc., to obtain higher processing gains. Moreover, it inevitably introduces multichannel error estimation and compensation problems. In addition, multichannel radar often faces coupling problems between channels.

This Special Issue aims to gather the latest research results and highlight the advances of multichannel radar, with topics including the following: imaging algorithms, parameter estimation, motion compensation, super-resolution, anti-jamming, direction of arrival (DOA) estimation, array calibration and other signal processing technologies of multichannel radar or multichannel SAR.

Dr. Wenchao Li
Guest Editor

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Keywords

multichannel radar
direction of arrival (DOA) estimation
radar signal processing
multiple-input multiple-output (MIMO) radar
synthetic aperture radar (SAR)

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Published Papers (6 papers)

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Research

23 pages, 9338 KB

Open AccessArticle

Geometry-Driven Phase Error Estimation for Azimuth Multi-Channel SAR via Global Radar Landmark Control Point Library

by Tingting Jin, Zheng Li, Feng Wang and Hui Long

Sensors 2026, 26(5), 1622; https://doi.org/10.3390/s26051622 - 5 Mar 2026

Viewed by 325

Abstract

Azimuth multi-channel synthetic aperture radar (SAR) is a core technology for achieving high-resolution wide-swath (HRWS) imaging. However, inter-channel phase inconsistency causes image amplitude distortion and phase accuracy degradation, which severely affects subsequent applications. Existing phase error estimation methods face specific limitations: the performance of subspace-based approaches degrades in complex scenes due to unreliable covariance matrix estimation, while conventional frequency-domain correlation methods rely on manual selection of strong scatterers, introducing inefficiency and subjectivity that precludes autonomous deployment. To address these issues, this paper proposes a geometry-driven inter-channel phase error estimation framework based on Global Radar Landmark Control Point Library (GRL-CP). The proposed framework replaces scene-dependent target selection with geometric-prior-driven control point activation. The GRL-CP library stores only the geodetic coordinates and scattering stability attributes of globally persistent radar landmarks, rather than image patches. For a new SAR acquisition, the echo position of these landmarks are predicted using a range–Doppler geometric model, enabling fully automatic and reliable control point activation. Based on the activated radar landmarks, inter-channel phase error is estimated using a frequency-domain correlation scheme. Experimental results on multi-channel spaceborne SAR datasets demonstrate that the proposed method achieves improved stability and accuracy under complex terrain scenarios. Full article

(This article belongs to the Special Issue Advances in Multichannel Radar Systems)

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23 pages, 2961 KB

Open AccessArticle

Eigenvalue Adjustment-Based STAP in Airborne MIMO Radar Under Limited Snapshots

by Chao Xu, Qizhen Feng, Zhao Wang, Dingding Li and Di Song

Sensors 2026, 26(5), 1508; https://doi.org/10.3390/s26051508 - 27 Feb 2026

Viewed by 288

Abstract

The covariance matrix performs a vital role for space-time adaptive processing (STAP) in airborne multiple-input multiple-output (MIMO) radar. As is known, the clutter-plus-noise covariance matrix (CPNCM), reflecting the statistical characteristics of radar echo, is a key component for MIMO-STAP. Commonly, an ideal CPNCM is impossible to obtain, and it must be estimated with sufficient snapshots. According to the RMB rule, MIMO-STAP requires many snapshots since MIMO radar has a high degree-of-freedom (DoF) due to its orthogonal transmit waveform. However, this is hard to satisfy in practice. This paper develops a novel covariance matrix estimation method under limited snapshots in airborne MIMO-STAP radar. Motivated by the random matrix theory, the proposed method enhances the CPNCM estimation by noise and clutter sample eigenvalues adjustment (EA). Concretely, the sample eigenvalues of noise are adjusted as noise power, and the ones of clutter are adjusted through minimizing the radar output power. Then, with the sample eigenvectors and adjusted sample eigenvalues, an effective CPNCM is formulated, and EA-MIMO-STAP is implemented reliably. Multiple experiments demonstrate that EA-MIMO-STAP has superior performance and robustness. Full article

(This article belongs to the Special Issue Advances in Multichannel Radar Systems)

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18 pages, 4009 KB

Open AccessArticle

The Effect of the Equivalent Permittivity Model in Contactless MIMO-GPR Imaging

by Gianluca Gennarelli, Ilaria Catapano and Francesco Soldovieri

Sensors 2026, 26(5), 1463; https://doi.org/10.3390/s26051463 - 26 Feb 2026

Viewed by 356

Abstract

Multiple-Input–Multiple-Output Ground-Penetrating Radar (MIMO-GPR), collecting multiview–multistatic data, is now becoming an assessed diagnostic tool, enabling enhanced reconstruction accuracy and subsurface target detection due to the exploitation of multiple Tx/Rx channels. In this context, the present work deals with a 2D radar imaging approach for contactless MIMO GPR based on the equivalent permittivity concept. The imaging problem is formulated as a linearized inverse scattering problem under Born approximation, and a ray propagation model, based on equivalent permittivity spatially varying along depth, is adopted to account for the wave propagation through the air–soil interface. The resulting linear inverse problem is solved by means of an adjoint inversion, enabling reliable target reconstruction. Despite the approximation introduced by the present formulation, numerical simulations show that the proposed imaging strategy is sufficiently accurate from an engineering viewpoint and is computationally efficient. Full article

(This article belongs to the Special Issue Advances in Multichannel Radar Systems)

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23 pages, 28280 KB

Open AccessArticle

Complementary Design of Two Types of Signals for Avionic Phased-MIMO Weather Radar

by Zhe Geng, Ling Wang, Fanwang Meng, Di Wu and Daiyin Zhu

Sensors 2026, 26(2), 423; https://doi.org/10.3390/s26020423 - 9 Jan 2026

Viewed by 679

Abstract

An avionic weather radar antenna should be able to operate in multiple modes to cope with the change in resolution and elevation coverage as an aircraft approaches a storm cell that could expand 10 km in elevation. To solve this problem, we propose the addition of four auxiliary antenna (AuxAnt) arrays based on the phased-MIMO antenna structure to the existing avionic weather radar for future field data collection missions. Two types of signals are employed: the Type I signal transmitted by AuxAnt 1 and 2 is designed based on a non-overlapping subarray configuration, with Subarray 1 and 2 dedicated to the transmission of long and short pulses, respectively, so that the near-range blind zone is mitigated. Leveraging the waveform design and beamforming flexibility provided by the phased-MIMO antenna, pulse compressions based on frequency modulation and phase-coding are employed for wide and narrow main beams, respectively. To suppress the range sidelobes, adaptive pulse compression is used at the receiver end in substitute of the conventional matched filter. In contrast, the Type II signal transmitted by AuxAnt 3 and 4 is designed based on the contextual information so that the transmitted beampatterns have specific sidelobe levels at certain directions for interference suppression. The advantages of the proposed signaling strategy are verified with a series of ingeniously devised experiments based on real weather data. Full article

(This article belongs to the Special Issue Advances in Multichannel Radar Systems)

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21 pages, 16524 KB

Open AccessArticle

MUSIC-Based Multi-Channel Forward-Scatter Radar Using OFDM Signals

by Yihua Qin, Abdollah Ajorloo and Fabiola Colone

Sensors 2025, 25(24), 7621; https://doi.org/10.3390/s25247621 - 16 Dec 2025

Viewed by 674

Abstract

This paper presents an advanced signal processing framework for multi-channel forward-scatter radar (MC-FSR) systems based on the Multiple Signal Classification (MUSIC) algorithm. The proposed framework addresses the inherent limitations of FFT-based space-domain processing, such as limited angular resolution and the poor detectability of weak or closely spaced targets, which become particularly severe in low-cost FSR systems, which are typically operated with small antenna arrays. The MUSIC algorithm is adapted to operate on real-valued data obtained from the non-coherent, amplitude-based MC-FSR approach by reformulating the steering vectors and adjusting the degrees of freedom (DoFs). While compatible with arbitrary transmitting waveforms, particular emphasis is placed on Orthogonal Frequency Division Multiplexing (OFDM) signals, which are widely used in modern communication systems such as Wi-Fi and cellular networks. An analysis of the OFDM waveform’s autocorrelation properties is provided to assess their impact on target detection, including strategies to mitigate rapid target signature decay using a sub-band approach and to manage signal correlation through spatial smoothing. Simulation results, including multi-target scenarios under constrained array configurations, demonstrate that the proposed MUSIC-based approach significantly enhances angular resolution and enables reliable discrimination of closely spaced targets even with a limited number of receiving channels. Experimental validation using an S-band MC-FSR prototype implemented with software-defined radios (SDRs) and commercial Wi-Fi antennas, involving cooperative targets like people and drones, further confirms the effectiveness and practicality of the proposed method for real-world applications. Overall, the proposed MUSIC-based MC-FSR framework exhibits strong potential for implementation in low-cost, hardware-constrained environments and is particularly suited for emerging Integrated Sensing and Communication (ISAC) systems. Full article

(This article belongs to the Special Issue Advances in Multichannel Radar Systems)

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22 pages, 4598 KB

Open AccessArticle

A ST-ConvLSTM Network for 3D Human Keypoint Localization Using MmWave Radar

by Siyuan Wei, Huadong Wang, Yi Mo and Dongping Du

Sensors 2025, 25(18), 5857; https://doi.org/10.3390/s25185857 - 19 Sep 2025

Cited by 2 | Viewed by 1170

Abstract

Accurate human keypoint localization in complex environments demands robust sensing and advanced modeling. In this article, we construct a ST-ConvLSTM network for 3D human keypoint estimation via millimeter-wave radar point clouds. The ST-ConvLSTM network processes multi-channel radar image inputs, generated from multi-frame fused point clouds through parallel pathways. These pathways are engineered to extract rich spatiotemporal features from the sequential radar data. The extracted features are then fused and fed into fully connected layers for direct regression of 3D human keypoint coordinates. In order to achieve better network performance, a mmWave radar 3D human keypoint dataset (MRHKD) is built with a hybrid human motion annotation system (HMAS), in which a binocular camera is used to measure the human keypoint coordinates and a 60 GHz 4T4R radar is used to generate radar point clouds. Experimental results demonstrate that the proposed ST-ConvLSTM, leveraging its unique ability to model temporal dependencies and spatial patterns in radar imagery, achieves MAEs of 0.1075 m, 0.0633 m, and 0.1180 m in the horizontal, vertical, and depth directions. This significant improvement underscores the model’s enhanced posture recognition accuracy and keypoint localization capability in challenging conditions. Full article

(This article belongs to the Special Issue Advances in Multichannel Radar Systems)

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