Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2609.25547

Centroid Angle Estimation of Multiple Scatterers Using Monopulse Radar with Frequency Diversity

Minyoung Hwang·Hyuncheol Park·Joohwan Chun
View original source ↗Read source document ↗Export metadata ↓

Abstract

The monopulse technique determines the angle of a target by comparing signals from two narrow beams, yielding a precise angular estimate with low complexity. However, it struggles to resolve multiple closely spaced scatterers within the same resolution cell. Existing methods for estimating multiple scatterer angles involve complex signal processing and system modifications. We propose an effective method to estimate the angular centroid of scatterers using the mode of monopulse angle estimates. A semi-analytic expression for the angle estimate distribution is derived, confirming that its mode aligns with the centroid. To enhance estimation accuracy, we employ frequency diversity to reduce sample correlation. Numerical results validate the advantages of the proposed method, demonstrating superior performance over conventional techniques with low complexity.

Explore related subjects

eess.SP

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Minyoung Hwang, Hyuncheol Park, Joohwan Chun. 2026-09-22. Centroid Angle Estimation of Multiple Scatterers Using Monopulse Radar with Frequency Diversity. https://arxiv.org/abs/2609.25547

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Circulant ADMM-Net for Fast High-resolution DoA Estimation

This paper introduces CADMM-Net and CHADMM-Net, two deep neural networks for direction of arrival estimation within the least-absolute shrinkage and selection operator (LASSO) framework. These two networks are based on a structured deep unfolding of the alternating direction method of multipliers (ADMM) algorithm through the use of circulant as well as Hermitian-circulant matrices. Along with a computational complexity of $\mathcal{O}(N\log(N))$ per layer for the inference, where $N$ is the length of the dictionary $\mathbf{A}$, they additionally exhibit a memory footprint of $N$ and approximately half of $N$ for CADMMNet and CHADMM-Net, respectively, compared with $N^{2}$ for ADMM-Net. Furthermore, these structured networks exhibit a competitive performance against ADMM-Net, LISTA, TLISTA, and THLISTA with respect to the detection rate, the angular root-mean square error, and the normalized mean squared error.

eess.SP↗
arXiv2026

BASIIS: Bistatic Angular Sampling and Interpolation for ISAC Setups

Integrated Sensing and Communications (ISAC) is a defining feature of 6G, extending cellular networks with radar-like sensing at limited additional overhead. In bistatic deployments, sensing requires coordinating the transmitter (TX) and receiver (RX) arrays to scan the Cartesian product of angle of departure and arrival, resulting in a four-dimensional sampling problem in the angular domain. This work establishes a complete angular sampling framework for bistatic ISAC, extending the DFT-based optimal-sampling methodology to the full azimuth and elevation domains of both arrays. We show that the bistatic geometry couples the TX and RX elevation angles, and represent this coupling through the ortho-baseline coarray, a virtual array that captures the joint elevation aperture of the array pair. From the coarray we derive a minimal sampling and interpolation scheme, near-lossless and realizable with any beamforming architecture. Monte Carlo simulations confirm the proposed minimal acquisition essentially equalizes the detection accuracy of dense oversampled imaging while acquiring 3 to 5 times fewer TX-RX direction pairs. This allows having bistatic operations with drastically reduced overhead on the radio resource usage of ISAC systems.

eess.SP↗
arXiv2026

Prediction-Aided V2X Safety Message Recovery via Uncertainty-Aware LDPC Decoding

Periodic basic safety messages (BSMs) exhibit temporal correlation as vehicle motion evolves continuously over time. This temporal structure provides predictive information about the current BSM and motivates prediction-aided recovery after a decoding failure. For low-density parity-check (LDPC)-coded transmission, a straightforward approach maps a predicted vehicle state to a candidate bit sequence and uses it as decoder prior information. However, a point prediction does not capture the uncertainty of the prediction. The resulting hard point prior assigns fixed confidence to each predicted bit and may introduce strong erroneous evidence when the prediction is incorrect. This paper proposes an uncertainty-aware prediction-aided LDPC recovery framework for vehicle-to-everything (V2X) safety messages. Instead of using a deterministic point prediction, the proposed method represents the current vehicle state by a predictive distribution. The distribution is propagated through BSM field quantization and serialization to obtain bit-level probabilities, which are converted into prior log-likelihood ratios (LLRs) whose magnitudes reflect prediction reliability. After an initial cyclic redundancy check (CRC) failure, these probabilistic priors are combined with the original channel observations and used in a second LDPC decoding pass. The proposed method preserves the standardized message representation and channel-coding procedure. Simulation results show that the proposed probabilistic priors outperform the hard point prior across different motion predictors. At $E_b/N_0=0.75$~dB, the proposed method recovers 87.27% of the messages that fail the initial decoding attempt.

eess.SP↗