| 1. |
- Ren, Jiaying, et al.
(författare)
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RFI Mitigation for UWB Radar Via Hyperparameter-Free Sparse SPICE Methods
- 2019
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Ingår i: IEEE Transactions on Geoscience and Remote Sensing. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 0196-2892 .- 1558-0644. ; 57:6, s. 3105-3118
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Tidskriftsartikel (refereegranskat)abstract
- Radio frequency interference (RFI) causes serious problems to ultrawideband (UWB) radar operations due to severely degrading radar imaging capability and target detection performance. This paper formulates proper data models and proposes novel methods for effective RFI mitigation. We first apply the single-snapshot Sparse Iterative Covariance-based Estimation (SPICE) algorithm to data from each pulse repetition interval for RFI mitigation and discuss the connection of SPICE to the l(1)-penalized least absolute deviation (l(1)-PLAD) approach. Then, we devise a modified group SPICE algorithm and we prove that it is equivalent to a special case of the l(1,2)-PLAD method. The modified group SPICE algorithm can be applied to data from a coherent processing interval for effective RFI mitigation. Both the single-snapshot SPICE and the modified group SPICE methods simultaneously exploit the sparsity properties of both RFI spectrum and UWB radar target echoes. Unlike the existing sparsity-based RFI suppression methods, such as the robust principal component analysis algorithm, the proposed methods are hyperparameter-free and therefore easier to use in practical applications. Furthermore, the fast implementation of the SPICE methods is considered by exploiting the special structures of both single-snapshot and multiple-snapshot covariance matrices. Finally, the results obtained from applying the SPICE methods to simulated data as well as measured data collected by the U.S. Army Research Laboratory synthetic aperture radar system are presented to demonstrate the effectiveness of the proposed methods.
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| 2. |
- Ren, Jiaying, et al.
(författare)
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Sinusoidal parameter estimation from signed measurements obtained via time-varying thresholds
- 2018
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Ingår i: Proc. 52nd Asilomar Conference on Signals, Systems, and Computers. - Piscataway, NJ : IEEE. - 9781538692189 ; , s. 1111-1115
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Konferensbidrag (refereegranskat)abstract
- We consider the problem of sinusoidal parameter estimation using signed observations obtained via one-bit sampling with time-varying thresholds. In a previous paper, a relaxation based algorithm, referred to as IbRELAX, has been proposed to iteratively maximize the likelihood function. However, IbRELAX can only he used in applications involving a small number of sinusoids due to the time-consuming exhaustive search procedure needed in each iteration. In this paper, we present a majorizalion-minimization (MM) based IbRELAX algorithm, referred to as IbMMRELAX, to enhance the computational efficiency of IbRELAX. Using the MM technique, IbMMRELAX maximizes the likelihood function iteratively using simple FFT operations to reduce the computational cost of IbRELAX while maintaining its excellent estimation accuracy. Numerical examples are presented to demonstrate the effectiveness of the proposed method.
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| 3. |
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| 4. |
- Zhang, Tianyi, et al.
(författare)
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Joint RFI mitigation and radar echo recovery for one-bit UWB radar
- 2022
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Ingår i: Signal Processing. - : Elsevier. - 0165-1684 .- 1872-7557. ; 193
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Tidskriftsartikel (refereegranskat)abstract
- A B S T R A C T Radio frequency interference (RFI) mitigation and radar echo recovery are critically important for the proper functioning of ultra-wideband (UWB) radar systems using one-bit sampling techniques. We re-cently introduced a technique for one-bit UWB radar, which first uses a majorization-minimization method for RFI parameter estimation followed by a sparse method for radar echo recovery. However, this technique suffers from high computational complexity due to the need to estimate the parameters of each RFI source separately and iteratively. In this paper, we present a computationally efficient joint RFI mitigation and radar echo recovery framework to greatly reduce the computational cost. Specifically, we exploit the sparsity of RFI in the fast-frequency domain and the sparsity of radar echoes in the fast -time domain to design a one-bit weighted SPICE (SParse Iterative Covariance-based Estimation) based framework for the joint RFI mitigation and radar echo recovery of one-bit UWB radar. Both simulated and experimental results are presented to show that the proposed one-bit weighted SPICE framework can not only reduce the computational cost but also outperform the existing approach for decoupled RFI mitigation and radar echo recovery of one-bit UWB radar.(c) 2021 Elsevier B.V. All rights reserved.
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| 5. |
- Zhang, Tianyi, et al.
(författare)
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RFI Mitigation for One-Bit UWB Radar Systems
- 2022
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Ingår i: IEEE Transactions on Aerospace and Electronic Systems. - : Institute of Electrical and Electronics Engineers (IEEE). - 0018-9251 .- 1557-9603. ; 58:2, s. 879-889
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Tidskriftsartikel (refereegranskat)abstract
- Radio frequency interference (RFI) mitigation is critical to the proper operation of ultrawideband (UWB) radar systems because RFI can severely degrade the radar imaging capability and target detection performance. In this article, we address the RFI mitigation problem for one-bit UWB radar systems. A one-bit UWB system obtains its signed measurements via a low-cost and high rate sampling scheme, referred to as the continuous time binary value (CTBV) technology. This sampling strategy compares the signal to a known threshold that varies with slow-time and can be used to achieve a high sampling rate and quantization resolution with simple and affordable hardware. This article establishes a proper data model for the RFI sources and proposes a novel RFI mitigation method for the one-bit UWB radar system that uses the CTBV sampling technique. Specifically, we model the RFI sources as a sum of sinusoids with frequencies fixed during the coherent processing interval (CPI) and we exploit the sparsity of the RFI spectrum. We use an extended majorization-minimization-based 1bRELAX algorithm, referred to as 1bMMRELAX, to estimate the RFI source parameters from the signed measurements obtained by using the CTBV sampling strategy. We also devise a new fast frequency initialization method for the extended 1bMMRELAX algorithm to improve its computational efficiency. Moreover, a sparse method is introduced to recover the desired radar echoes using the estimated RFI parameters. Both simulated and experimental results are presented to demonstrate that our proposed algorithm outperforms the existing digital integration method, especially for severe RFI cases.
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