Detection, Mode Selection, and Parameter Estimation in Distributed Radar Networks: Algorithms and Implementation Challenges

Chalise, Batu K.; Amin, Moeness G.; Martone, Anthony F.; Kirk, Benjamin H.

doi:10.1109/maes.2022.3205568

Cited by 13 publications

(2 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…And, Ref. [14] demonstrates that deploying a distributed network enhances radar system capabilities for target detection, parameter estimation, and tracking through cooperation among neighboring nodes. The proposed deep learning approach with LSTM networks allows nodes to compensate for each other's observations, improving overall performance in cluttered radar environments.…”

Section: Introductionmentioning

confidence: 99%

Information Fusion for Radar Signal Sorting with the Distributed Reconnaissance Receivers

et al. 2023

View full text Add to dashboard Cite

The conventional method of centralizing information fusion is commonly employed for sorting radar signals in reconnaissance receivers. However, challenges arise when the distance between reconnaissance receivers and the fusion center is distant, or when the fusion center is compromised by hostile forces. To address these issues, this paper proposes a novel distributed information fusion method. In this method, each reconnaissance receiver is restricted to accessing adjacent nodes within an undirected graph for information transmission and local computation. The distributed Dempster’s combination rule and the cautious conjunctive rule are implemented using weight functions and consensus algorithms. Furthermore, an innovative outlier detection algorithm is incorporated into the fusion process to enhance its robustness. Experimental results demonstrate that the proposed method effectively improves the accuracy of radar signal sorting. When the sorting accuracy of a single reconnaissance receiver is equal to or higher than 60%, both fusion rules achieve a sorting accuracy of 100%. Even when the sorting accuracy of a single reconnaissance receiver is as low as 50%, the fused result still maintains a sorting accuracy of over 97%.

show abstract

Section: Introductionmentioning

confidence: 99%

Information Fusion for Radar Signal Sorting with the Distributed Reconnaissance Receivers

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Collaborative sensing offers numerous benefits for object detection and radar imaging over single automotive radar sensor [2]. For example, enhancing signal gain, increasing reliability, adaptability, and greater spatial diversity [3,4]. In automotive sensing tasks, such as direction-of-arrival (DOA) estimation, collaborative sensing involves coherently integrating measurements from subarrays of each radar to synthesize an array with a larger aperture, thereby enhancing angle resolution and increasing Signal-to-Noise Ratio (SNR) at the same time [5].…”

Section: Introductionmentioning

confidence: 99%

One-Bit Phase Retrieval: More Samples Means Less Complexity?

Eamaz¹,

Yeganegi²,

Soltanalian³

2022

Preprint

View full text Add to dashboard Cite

The classical problem of phase retrieval has found a wide array of applications in optics, imaging and signal processing. In this paper, we consider the phase retrieval problem in a one-bit setting, where the signals are sampled using one-bit analog-to-digital converters (ADCs). A significant advantage of deploying one-bit ADCs in signal processing systems is their superior sampling rates as compared to their high-resolution counterparts. This leads to an enormous amount of one-bit samples gathered at the output of the ADC in a short period of time. We demonstrate that this advantage pays extraordinary dividends when it comes to convex phase retrieval formulations—namely that the often encountered matrix semi-definiteness constraints as well as rank constraints (that are computationally prohibitive to enforce), become redundant for phase retrieval in the face of a growing sample size. Several numerical results are presented to illustrate the effectiveness of the proposed methodologies

show abstract

Effect of Communications Link Impairments on the Sparsity-based Distributed DoA Estimation

Chalise

Amin

Martone

et al. 2022

2022 56th Asilomar Conference on Signals, Systems, and Computers

View full text Add to dashboard Cite

Detection, Mode Selection, and Parameter Estimation in Distributed Radar Networks: Algorithms and Implementation Challenges

Cited by 13 publications

References 55 publications

Information Fusion for Radar Signal Sorting with the Distributed Reconnaissance Receivers

Information Fusion for Radar Signal Sorting with the Distributed Reconnaissance Receivers

One-Bit Phase Retrieval: More Samples Means Less Complexity?

Effect of Communications Link Impairments on the Sparsity-based Distributed DoA Estimation

Contact Info

Product

Resources

About