Review of radar performance improvement using beyond linear signal processing

doi:10.23919/JSEE.2026.000150

Abstract

Abstract:

Radar is a core sensing tool in the fields of modern defense, aerospace, and civil monitoring, and its performance improvement has always been a research hotspot in the field of signal processing. However, the traditional radar signal processing system mainly relies on the assumption of linear time-invariant, and its performance is seriously degraded in complex scenes such as low observable target detection, strong mainlobe interference, and non-Gaussian clutter. Therefore, beyond linear processing (BLiP), a new applied research project at the algorithm level, has been paid more and more attention. BLiP focuses on improving the radar performance with the usage of signal processing techniques that go beyond current linear signal processing algorithms. This study addresses the potential for radar performance improvement of BLiP in areas such as waveform adaptive scheduling and design, clutter suppression, adaptive interference cancellation, direction of arrival (DOA) estimation, target detection, high-resolution imaging, and target tracking, and provides an outlook on future trends of BLiP in radar signal processing.

Key words: radar signal processing, beyond linear processing (BLiP), track-before-detect (TBD), waveform design, direction of arrival (DOA), adaptive interference cancellation, iterative processing

Hongmeng Chen, Fei Meng, Zeyu Wang, Zhirui Wang, Xiang Li, Lei Huang. Review of radar performance improvement using beyond linear signal processing[J]. Journal of Systems Engineering and Electronics, 2026, 37(4): 1081-1100.

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References 119

1	Skolnik M I. Radar handbook[M]. New York: McGraw-Hill, 2008.
2	Metron Inc. Awarded DARPA beyond linear processing phase one contract [EB/OL]. [2026-08-16]. https://www.metsci.com/news/ met-ron-inc-awarded- darpa-beyond-linear- processing- phase-one- contract/.
3	Strategic Technology Office, DARPA. Broad agency announcement beyond linear processing (BLiP) HR001123S0008[R/OL]. [2026-08-16]. https://www.highergov.com/document/hr001123s0008-amendment-01-pdf-b36944/.
4	Cox P, Klein K, Coutiño M, et al. Improved detection of hypersonic threats with radar using irregular waveforms and advanced processing[R]. EDA Research, Technology, and Innovation Papers Award, 2023.
5	Yeary M. Fully digital Horus radar experiments in support of DARPA’s beyond linear processing program[C]//IEEE International Symposium on Phased Array Systems and Technology, 2024.
6	Cilliers J E, Smit J C Pulse compression sidelobe reduction by minimization of Lp-norms. IEEE Trans. on Aerospace and Electronic Systems, 2007, 43 (3): 1238. doi: 10.1109/TAES.2007.4383616
7	De Maio A, Huang Y, Piezzo M, et al Design of radar receive filters optimized according to Lp-norm based criteria. IEEE Trans. on Signal Processing, 2011, 59 (8): 4023. doi: 10.1109/TSP.2011.2153199
8	Kajenski P J Mismatch filter design via convex optimization. IEEE Trans. on Aerospace and Electronic Systems, 2016, 52 (4): 1587. doi: 10.1109/TAES.2016.140556
9	Rabaste O, Savy L Mismatched filter optimization for radar applications using quadratically constrained quadratic programs. IEEE Trans. on Aerospace and Electronic Systems, 2015, 51 (4): 3107. doi: 10.1109/TAES.2015.130769
10	Rabaste O, Bosse J Robust mismatched filter for off-grid target. IEEE Signal Processing Letters, 2019, 26 (8): 1147. doi: 10.1109/LSP.2019.2923054
11	Chen H, Lu Y, Liu J, et al. Space group targets detecting and resolving algorithm via ultra-low sidelobe filtering[C]//IEEE Radar Conference, 2019.
12	Wang T, Chen Z H, Cheng Z W, et al Joint design of mismatched filter and unimodular transmit waveform. Journal of Northwestern Polytechnical University, 2021, 39 (6): 1349. doi: 10.1051/jnwpu/20213961349
13	Karbasi S M, Radmard M, Nayebi M M, et al Design of multiple-input multiple-output transmit waveform and receive filter for extended target detection. IET Radar, Sonar & Navigation, 2015, 9 (9): 1345. doi: 10.1049/iet-rsn.2015.0063
14	Cui G L, Yu X X, Yang J, et al An overview of waveform optimization methods for cognitive radar. Journal of Radars, 2019, 8 (5): 537.
15	Cui G L, Yu X X, Carotenuto V, et al Space-time transmit code and receive filter design for colocated MIMO radar. IEEE Trans. on Signal Processing, 2017, 65 (5): 1116. doi: 10.1109/TSP.2016.2633242
16	Tang B, Li J Spectrally constrained MIMO radar waveform design based on mutual information. IEEE Trans. on Signal Processing, 2019, 67 (3): 821. doi: 10.1109/TSP.2018.2887186
17	Wei Y S, Xu Z Y Review of signal design for discontinuous spectrum radar. Journal of Radars, 2022, 11 (2): 183.
18	Wang X Y, Tang B, Wu W J, et al Relative entropy-based waveform optimization for Rician target detection with dual-function radar communication systems. IEEE Sensors Journal, 2023, 23 (10): 10718. doi: 10.1109/JSEN.2023.3264458
19	Li D, Tang B, Xue L Waveform design for MIMO DFRC systems: Finer sensing and safer communications. IEEE Trans. on Signal Processing, 2024, 72, 4509. doi: 10.1109/TSP.2024.3470219
20	Shi L F, Quan Y, Fan J T, et al Communicational radar detection technology. Journal of Radars, 2020, 9 (6): 1056.
21	Ma D, Shlezinger N, Huang T, et al Joint radar-communication strategies for autonomous vehicles: combining two key automotive technologies. IEEE Signal Processing Magazine, 2020, 37 (4): 85. doi: 10.1109/MSP.2020.2983832
22	Xu Q H, Lan L, Liao G S, et al Transceiver design for an FDA-MIMO radar and MIMO communication spectral coexistence system. Journal of Radars, 2025, 14 (4): 915.
23	Tang A M, Liu F, Yuan W J, et al Signal design for communication centric ISAC: state of art and future aspects. Journal of Radars, 2025, 14 (4): 1019.
24	Zhang Y C, Zhang Y, Li W C, et al Superresolution surface mapping for scanning radar: Inverse filtering based on the fast iterative adaptive approach. IEEE Trans. on Geoscience and Remote Sensing, 2018, 56 (1): 127. doi: 10.1109/TGRS.2017.2743263
25	Mao D Q, Zhang Y C, Zhang Y, et al Target fast reconstruction of real aperture radar using data extrapolation-based parallel iterative adaptive approach. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14, 2258. doi: 10.1109/JSTARS.2021.3054046
26	Chen H M, Li Y C, Gao W Q, et al Bayesian forward-looking superresolution imaging using Doppler deconvolution in expanded beam space for high-speed platform. IEEE Trans. on Geoscience and Remote Sensing, 2022, 60, 5105113.
27	Chen H M, Yu J Z, Zhang W J, et al Probability model-driven airborne Bayesian forward-looking super-resolution imaging for multitarget scenario. Journal of Radars, 2023, 12 (6): 1125.
28	Li W X, Li M, Zuo L, et al Real aperture radar forward-looking imaging based on variational Bayesian in presence of outliers. IEEE Trans. on Geoscience and Remote Sensing, 2022, 60, 5117113.
29	Mao D Q, Yang J Y, Yang M J, et al IAA-Net: an iterative adaptive approach for angular super-resolution imaging of real aperture scanning radar. Journal of Radars, 2024, 13 (5): 1073.
30	Zhou Z J, Li Y L, Liu K, et al Multichannel radar forward-looking imaging method based on dual-network co-operation. Journal of Radars, 2026, 15 (1): 196.
31	Zhang Y, Zhang P, Tuo X Y, et al Sparse targets angular super-resolution reconstruction method under unknown antenna pattern errors for scanning radar. Journal of Radars, 2024, 13 (3): 646.
32	Zhang L, Qiao Z J, Xing M D, et al High-resolution ISAR imaging by exploiting sparse apertures. IEEE Trans. on Antennas and Propagation, 2012, 60 (2): 997. doi: 10.1109/TAP.2011.2173130
33	Xu G, Xing M D, Zhang L, et al Sparse apertures ISAR imaging and scaling for maneuvering targets. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7 (7): 2942. doi: 10.1109/JSTARS.2014.2315630
34	Zhang S, Liu Y, Li X Computationally efficient sparse aperture ISAR autofocusing and imaging based on fast ADMM. IEEE Trans. on Geoscience and Remote Sensing, 2020, 58 (12): 8751. doi: 10.1109/TGRS.2020.2990445
35	Huang Q, Zhang L, Wei S, et al CV-Res-ADMM-Net for sparse aperture ISAR imaging. IEEE Trans. on Aerospace and Electronic Systems, 2025, 61 (4): 8291. doi: 10.1109/TAES.2025.3544618
36	Yuan Z A, Chen X, Deng B, et al Terahertz ISAR imaging with nonrigid vibration compensation and sidelobe suppression of space targets. IEEE Trans. on Geoscience and Remote Sensing, 2024, 62, 5110516.
37	Gong Y, Jiang Y, Liu Z, et al Fast ISAR imaging algorithm for azimuth gapped data based on structured Toeplitz matrix. IEEE Trans. on Computational Imaging, 2025, 11, 1548. doi: 10.1109/TCI.2025.3625054
38	Kang H, Li J, Guo Q, et al Pattern coupled sparse bayesian learning based on UTAMP for robust high resolution ISAR imaging. IEEE Sensors Journal, 2020, 20 (22): 13734. doi: 10.1109/JSEN.2020.3004037
39	Xiong S, Li K, Wang H, et al Sparse aperture high-resolution RID ISAR imaging of maneuvering target based on parametric efficient sparse bayesian learning. IEEE Geoscience and Remote Sensing Letters, 2024, 21, 4005905. doi: 10.1109/lgrs.2024.3371674
40	Zhang Y, Bai X, Liu S, et al Joint translational motion compensation and high-resolution ISAR imaging based on sparse Bayesian learning. IEEE Trans. on Computational Imaging, 2025, 11, 1115. doi: 10.1109/TCI.2024.3468014
41	Zhang Y, Bai X, Zhou F High-resolution ISAR imaging of maneuvering targets based on 2-D complex fast sparse Bayesian learning. IEEE Trans. on Radar Systems, 2025, 3, 995. doi: 10.1109/TRS.2025.3586927
42	Roy R, Kailath T ESPRIT-estimation of signal parameters via rotational invariance techniques. IEEE Trans. on Acoustics, Speech, and Signal Processing, 1989, 37 (7): 984. doi: 10.1109/29.32276
43	Porat B, Friedlander B Analysis of the asymptotic relative efficiency of the MUSIC algorithm. IEEE Trans. on Acoustics, Speech, and Signal Processing, 1988, 36 (4): 532. doi: 10.1109/29.1557
44	So H C. Source localization: algorithms and analysis. Maddio S, Cidronali A, Manes G. eds. Handbook of Position Location: Theory, Practice, and Advances[M]. Hoboken: Wiley, 2011: 25.
45	Qian C, Huang L, So H C Improved unitary root-MUSIC for DOA estimation based on pseudo-noise resampling. IEEE Signal Processing Letters, 2014, 21 (2): 140. doi: 10.1109/LSP.2013.2294676
46	Liao B, Chan S C, Huang L, et al Iterative methods for subspace and DOA estimation in nonuniform noise. IEEE Trans. on Signal Processing, 2016, 64 (12): 3008. doi: 10.1109/TSP.2016.2537265
47	Malioutov D, Cetin M, Willsky A S A sparse signal reconstruction perspective for source localization with sensor arrays. IEEE Trans. on Signal Processing, 2005, 53 (8): 3010. doi: 10.1109/TSP.2005.850882
48	Pazos S, Hurtado M, Muravchik C DOA estimation using random linear arrays via compressive sensing. IEEE Latin America Transactions, 2014, 12 (5): 859. doi: 10.1109/tla.2014.6872896
49	Northardt E T, Bilik I, Abramovich Y I Spatial compressive sensing for direction-of-arrival estimation with bias mitigation via expected likelihood. IEEE Trans. on Signal Processing, 2013, 61 (5): 1183. doi: 10.1109/TSP.2012.2232654
50	Shen Q, Liu W, Cui W, et al Underdetermined DOA estimation under the compressive sensing framework: a review. IEEE Access, 2016, 4, 8865. doi: 10.1109/ACCESS.2016.2628869
51	Yardibi T, Li J, Stoica P, et al Source localization and sensing: a nonparametric iterative adaptive approach based on weighted least squares. IEEE Trans. on Aerospace and Electronic Systems, 2010, 46 (1): 425. doi: 10.1109/TAES.2010.5417172
52	Rowe W, Li J, Stoica P. Sparse iterative adaptive approach with application to source localization[C]//5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2013: 196.
53	Glentis G O, Jakobsson A Efficient implementation of iterative adaptive approach spectral estimation techniques. IEEE Trans. on Signal Processing, 2011, 59 (9): 4154. doi: 10.1109/TSP.2011.2145376
54	Barcelo M, Vicario J L, Seco-granados G A reduced complexity approach to IAA beamforming for efficient DOA estimation of coherent sources. EURASIP Journal on Advances in Signal Processing, 2011, 2011 (1): 521265. doi: 10.1155/2011/521265
55	Agarwal A, Kumar A, Agrawal M. Iterative adaptive approach to DOA estimation with acoustic vector sensors[C]//OCEANS-Genova, 2015.
56	Wang W, Tan W J Alternating iterative adaptive approach for DOA estimation via acoustic vector sensor array under directivity bias. IEEE Communications Letters, 2020, 24 (9): 1944. doi: 10.1109/LCOMM.2020.2997245
57	Wipf D P, Rao B D Sparse Bayesian learning for basis selection. IEEE Trans. on Signal Processing, 2004, 52 (8): 2153. doi: 10.1109/TSP.2004.831016
58	Ji S, Xue Y, Carin L Bayesian compressive sensing. IEEE Trans. on Signal Processing, 2008, 56 (6): 2346. doi: 10.1109/TSP.2007.914345
59	Liu Z M, Zhou Y Y A unified framework and sparse Bayesian perspective for direction-of-arrival estimation in the presence of array imperfections. IEEE Trans. on Signal Processing, 2013, 61 (15): 3786. doi: 10.1109/TSP.2013.2262682
60	Yang Z, Xie L H, Zhang C S Off-grid direction of arrival estimation using sparse Bayesian inference. IEEE Trans. on Signal Processing, 2013, 61 (1): 38. doi: 10.1109/TSP.2012.2222378
61	Hu N, Sun B, Wang J, et al Source localization for sparse array using nonnegative sparse Bayesian learning. Signal Processing, 2016, 127, 37. doi: 10.1016/j.sigpro.2016.02.025
62	Huang Q, Zhang G, Fang Y Real-valued DOA estimation for spherical arrays using sparse Bayesian learning. Signal Processing, 2016, 125, 79. doi: 10.1016/j.sigpro.2016.01.009
63	Gerstoft P, Mecklenbräuker C F, Xenaki A, et al Multisnapshot sparse Bayesian learning for DOA. IEEE Signal Processing Letters, 2016, 23 (10): 1469. doi: 10.1109/LSP.2016.2598550
64	Dai J, So H C Sparse Bayesian learning approach for outlier-resistant direction-of-arrival estimation. IEEE Trans. on Signal Processing, 2018, 66 (3): 744. doi: 10.1109/TSP.2017.2773420
65	Chen Z D, Lu X C, Huang Y F, et al DOA estimation with sparse Bayesian learning using hierarchical half-cauchy prior with spectra refinement strategy. IEEE Trans. on Aerospace and Electronic Systems, 2025, 61 (5): 12059. doi: 10.1109/TAES.2025.3571860
66	Wang H, Wang X, Lan X, et al Effective high-resolution off-grid DOA estimation with mutual coupling via CNN framework. IEEE Sensors Journal, 2025, 25 (1): 1133. doi: 10.1109/JSEN.2024.3494884
67	Hu Y, Sun S, Zhang Y D Model-based learning for DOA estimation with one-bit single-snapshot sparse arrays. IEEE Journal of Selected Topics in Signal Processing, 2025, 19 (6): 909. doi: 10.1109/JSTSP.2025.3627141
68	Yu K B, Murrow D J Adaptive digital beamforming for angle estimation in jamming. IEEE Trans. on Aerospace and Electronic Systems, 2001, 37 (2): 508. doi: 10.1109/7.937465
69	Chen X, Shu T, Yu K B, et al Joint adaptive beamforming techniques for distributed array radars in multiple mainlobe and sidelobe jammings. IEEE Antennas and Wireless Propagation Letters, 2020, 19 (2): 248. doi: 10.1109/LAWP.2019.2958687
70	Luo W, Jin H, Li H, et al Radar main-lobe jamming suppression based on adaptive opposite fireworks algorithm. IEEE Open Journal of Antennas and Propagation, 2021, 2, 138. doi: 10.1109/OJAP.2020.3036878
71	Gao H, Wang H, Wang J, et al An algorithm re-used-based interference cancellation with mainlobe maintenance method. IEEE Sensors Journal, 2026, 26 (8): 12064. doi: 10.1109/JSEN.2026.3672458
72	Zhou F, Tao M, Bai X, et al Narrow-band interference suppression for SAR based on independent component analysis. IEEE Trans. on Geoscience and Remote Sensing, 2013, 51 (10): 4952. doi: 10.1109/TGRS.2013.2244605
73	Yang X, Zhang Z, Zeng T, et al Mainlobe interference suppression based on eigen-projection processing and covariance matrix reconstruction. IEEE Antennas and Wireless Propagation Letters, 2014, 13, 1369. doi: 10.1109/LAWP.2014.2339224
74	Ge M, Cui G, Yu X, et al Main lobe jamming suppression via blind source separation sparse signal recovery with subarray configuration. IET Radar, Sonar & Navigation, 2020, 14 (3): 431. doi: 10.1049/iet-rsn.2019.0500
75	Lei Z, Qu Q, Zhang L, et al Main lobe deception jamming suppression approach via SVD and FrFT for LFM radars. IEEE Trans. on Aerospace and Electronic Systems, 2024, 60 (3): 2948. doi: 10.1109/TAES.2024.3357029
76	Wang C, Zheng Z, Zhang K, et al Adaptive main-lobe jamming suppression based on cascaded LMS filter with low-cost distributed radar. IEEE Sensors Journal, 2023, 23 (21): 26292. doi: 10.1109/JSEN.2023.3317407
77	Jia Y, He M, Chen H, et al Joint design of transmit waveform and receive filter for suppression of mainlobe deceptive jammer via deep transfer siamese network. IEEE Trans. on Aerospace and Electronic Systems, 2025, 61 (5): 12153. doi: 10.1109/TAES.2025.3572062
78	He L, Zhang P, Li Y, et al Main-lobe interference suppression using robust independent component analysis based on complex Newton optimization. IEEE Trans. on Aerospace and Electronic Systems, 2025, 61 (6): 18413. doi: 10.1109/TAES.2025.3613280
79	Greco M, Bordoni F, Gini F X-band sea-clutter nonstationarity: influence of long waves. IEEE Journal of Oceanic Engineering, 2004, 29 (2): 269. doi: 10.1109/JOE.2004.828548
80	Hu J, Tung W W, Gao J A new way to model nonstationary sea clutter. IEEE Signal Processing Letters, 2009, 16 (2): 129. doi: 10.1109/LSP.2008.2009844
81	Melvin W L A STAP overview. IEEE Aerospace and Electronic Systems Magazine, 2004, 19 (1): 19. doi: 10.1109/MAES.2004.1263229
82	Haimovich A The eigencanceler: adaptive radar by eigenanalysis methods. IEEE Trans. on Aerospace and Electronic Systems, 1996, 32 (2): 532. doi: 10.1109/7.489498
83	Raghavan R S Statistical interpretation of a data adaptive clutter subspace estimation algorithm. IEEE Trans. on Aerospace and Electronic Systems, 2012, 48 (2): 1370. doi: 10.1109/TAES.2012.6178068
84	Xu S W, Bai X H, Guo Z X, et al Status and prospects of feature-based detection methods for floating targets on the sea surface. Journal of Radars, 2020, 9 (4): 684.
85	Duan K Q, Li Y F, Yang X J, et al Reduced degrees of freedom in space-time adaptive processing for space-based early warning radar. Journal of Radars, 2022, 11 (5): 871.
86	The Defence, Peace, Safety, and Security Unit of the Council for Scientific and Industrial Research. The Fynmeet radar database[EB/OL]. [2026-08-16]. http://www.csir.co.ca/ small_boat_detection.
87	Liu N B, Li J, Wang G Q, et al Sea-detecting radar experiment and target feature data acquisition for multisource observation dataset of maritime targets. Journal of Radars, 2025, 14 (3): 754.
88	Mou X, Chen X, Guan J, et al Sea clutter suppression for radar PPI images based on SCS-GAN. IEEE Geoscience and Remote Sensing Letters, 2021, 18 (11): 1886. doi: 10.1109/LGRS.2020.3012523
89	Qu Q, Liu W, Wang J, et al Enhanced CNN-based small target detection in sea clutter with controllable false alarm. IEEE Sensors Journal, 2023, 23 (9): 10193. doi: 10.1109/JSEN.2023.3259953
90	Wang Y, Zhao W, Wang X, et al Nonhomogeneous sea clutter suppression using complex-valued U-Net model. IEEE Geoscience and Remote Sensing Letters, 2022, 19, 4027705. doi: 10.1109/lgrs.2022.3214633
91	Du X, Ma D, Chen X, et al TD-AMRKNet-based radar image processing framework for sea clutter suppression. IEEE Trans. on Radar Systems, 2026, 4, 387. doi: 10.1109/TRS.2026.3656433
92	Xue J, Zhao X, Xu S, et al FPTF-YOLO: fully polarimetric time-frequency YOLO for maritime radar weak target detection and localization. IEEE Trans. on Aerospace and Electronic Systems, 2026, 62, 7651. doi: 10.1109/TAES.2026.3671743
93	Ward J. Space-time adaptive processing for airborne radar[C]//IEE Colloquium on Space-Time Adaptive Processing, 1994.
94	Sun K, Zhang H, Li G, et al. A novel STAP algorithm using sparse recovery technique[C]//IEEE International Geoscience and Remote Sensing Symposium, 2009: 336.
95	Yang Z, De Lamare R C, Li X L1-regularized STAP algorithms with a generalized sidelobe canceler architecture for airborne radar. IEEE Trans. on Signal Processing, 2012, 60 (2): 674. doi: 10.1109/TSP.2011.2172435
96	Wicks M C, Rangaswamy M, Adve R, et al Space-time adaptive processing: a knowledge-based perspective for airborne radar. IEEE Signal Processing Magazine, 2006, 23 (1): 51. doi: 10.1109/MSP.2006.1593337
97	Xiong Y, Xie W, Wang Y, et al Short-range nonstationary clutter suppression for airborne KA-STAP radar in complex terrain environment. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2025, 18, 2766. doi: 10.1109/JSTARS.2024.3522257
98	Li Z, Wang T ADMM-based low-complexity off-grid space-time adaptive processing methods. IEEE Access, 2020, 8, 206646. doi: 10.1109/ACCESS.2020.3037652
99	Yang Z C, De Lamare R C, Liu W J Sparsity-based STAP using alternating direction method with gain/phase errors. IEEE Trans. on Aerospace and Electronic Systems, 2017, 53 (6): 2756. doi: 10.1109/TAES.2017.2714938
100	Duan K Q, Li X, Xing K, et al Clutter mitigation in space-based early warning radar using a convolutional neural network. Journal of Radars, 2022, 11 (3): 386.
101	Zhu H G, Feng W K, Feng C Q, et al Deep unfolding based space-time adaptive processing method for airborne radar. Journal of Radars, 2022, 11 (4): 676.
102	Wang Z, Chen H, Xu S, et al Machine learning-aided nonhomogeneity detection method for airborne radar. IEEE Trans. on Radar Systems, 2025, 3, 220. doi: 10.1109/TRS.2025.3528032
103	Wang Z, Zhang X, Feng W, et al Deep unfolded atomic norm minimization algorithm for space-time adaptive processing. IEEE Geoscience and Remote Sensing Letters, 2025, 22, 3500505.
104	Huang W, Wang T, Cui W Robust airborne radar clutter suppression algorithm via deep neural networks under small-sample conditions. IEEE Sensors Journal, 2025, 25 (7): 11587. doi: 10.1109/JSEN.2025.3541037
105	Ebenezer S P, Papandreou-suppappola A Generalized recursive track-before-detect with proposal partitioning for tracking varying number of multiple targets in low SNR. IEEE Trans. on Signal Processing, 2016, 64 (11): 2819. doi: 10.1109/TSP.2016.2523455
106	Kim D Y, Ristic B, Guan R, et al A Bernoulli track-before-detect filter for interacting targets in maritime radar. IEEE Trans. on Aerospace and Electronic Systems, 2021, 57 (3): 1981. doi: 10.1109/TAES.2021.3054715
107	Ristic B, Kim D Y, Rosenberg L, et al Exploiting Doppler in Bernoulli track-before-detect for a scanning maritime radar. IEEE Trans. on Aerospace and Electronic Systems, 2022, 58 (1): 720. doi: 10.1109/TAES.2021.3098117
108	Guo Q, Teng L, Yin T X, et al Hybrid-driven Gaussian process online learning for highly maneuvering multi-target tracking. Frontiers of Information Technology & Electronic Engineering, 2023, 24 (11): 1647. doi: 10.1631/FITEE.2300348
109	Cao C, Zhao Y Multiple-model GLMB filter based on track-before-detect for tracking multiple maneuvering targets. Journal of Systems Engineering and Electronics, 2024, 35 (5): 1109. doi: 10.23919/JSEE.2024.000040
110	Wu H, Cheng Y, Chen X, et al Information geometry-based track-before-detect algorithm for slow-moving fluctuating target detection. IEEE Trans. on Signal Processing, 2023, 71, 1833. doi: 10.1109/TSP.2023.3277205
111	Pan J M, Li C, Zheng X N, et al Target tracking for passive bistatic radar based on mutual information entropy and improved PHD. Journal of Radars, 2026, 15 (2): 637.
112	Kropfreiter T, Williams J L, Meyer F Association-based track-before-detect with object contribution probabilities. IEEE Trans. on Signal Processing, 2026, 74, 1277. doi: 10.1109/TSP.2026.3659742
113	Li W, Yi W, Teh K C, et al Radar multiframe detection in a complicated multitarget environment. IEEE Trans. on Geoscience and Remote Sensing, 2023, 61, 5107516.
114	Li W, Yi W, Kong L, et al An efficient track-before-detect for multi-PRF radars with range and Doppler ambiguities. IEEE Trans. on Aerospace and Electronic Systems, 2022, 58 (5): 4083. doi: 10.1109/TAES.2022.3158633
115	Liang M, Kropfreiter T, Meyer F A BP method for track-before-detect. IEEE Signal Processing Letters, 2023, 30, 1137. doi: 10.1109/LSP.2023.3296874
116	Mao H, Zhang B, Yang J, et al. Labeled probability hypothesis density filtering for track-before-detect strategy[C]//26th International Conference on Information Fusion, 2023.
117	Fishel E, Tsarov N, Tapiro T, et al. Learn to track-before-detect via neural dynamic programming[C]//IEEE International Conference on Acoustics, Speech and Signal Processing, 2024: 9586.
118	Zhang Y, Li G, Zhang X P, et al. Transformer-based tracking network for maneuvering targets[C]//IEEE International Conference on Acoustics, Speech and Signal Processing, 2023.
119	Shen L, Su H, Li Z, et al Self-attention-based transformer for nonlinear maneuvering target tracking. IEEE Trans. on Geoscience and Remote Sensing, 2023, 61, 5109013.

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[2]	Tao HAN, Gongjian ZHOU. Pseudo-spectrum based track-before-detect for bistatic radar network [J]. Journal of Systems Engineering and Electronics, 2026, 37(1): 127-136.
[3]	Ailun XIE, Xiaobin LIU, Qihua WU, Feng ZHAO, Zhenyu QIAO, Shunping XIAO. Radar pulse waveform design method based on complementary amplitude coding [J]. Journal of Systems Engineering and Electronics, 2025, 36(3): 671-680.
[4]	Jinghu SUN, Jiahuan LIU, Wenqiang WEI, Xianxiang YU, Guolong CUI, Xiuyin ZHANG. Far-field calibration of automotive millimeter wave radar via near-field implementation [J]. Journal of Systems Engineering and Electronics, 2025, 36(3): 694-700.
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[7]	Zeqi YANG, Yiheng LIU, Hua ZHANG, Shuai MA, Kai CHANG, Ning LIU, Xiaode LYU. DOA estimation of high-dimensional signals based on Krylov subspace and weighted l₁-norm [J]. Journal of Systems Engineering and Electronics, 2024, 35(3): 532-540.
[8]	Shuang WU, Ye YUAN, Weike ZHANG, Naichang YUAN. Super-resolution DOA estimation for correlated off-grid signals via deep estimator [J]. Journal of Systems Engineering and Electronics, 2022, 33(6): 1096-1107.
[9]	Luo CHEN, Xiangrui DAI, Xiaofei ZHANG. Joint angle and frequency estimation for linear array: an extended DOA-matrix method [J]. Journal of Systems Engineering and Electronics, 2022, 33(4): 887-895.
[10]	Shenghua WANG, Yunhe CAO, Yutao LIU. A method of Robust low-angle target height and compound reflection coefficient joint estimation [J]. Journal of Systems Engineering and Electronics, 2022, 33(2): 322-329.
[11]	Ping LI, Jianfeng LI, Gaofeng ZHAO. Low complexity DOA estimation for massive UCA with single snapshot [J]. Journal of Systems Engineering and Electronics, 2022, 33(1): 22-27.
[12]	Cong XU, Zishu HE, Haicheng LIU, Yadan LI. Bayesian track-before-detect algorithm for nonstationary sea clutter [J]. Journal of Systems Engineering and Electronics, 2021, 32(6): 1338-1344.
[13]	Junpeng SHI, Fangqing WEN, Yongxiang LIU, Tianpeng LIU, Zhen LIU. High-order extended coprime array design for direction of arrival estimation [J]. Journal of Systems Engineering and Electronics, 2021, 32(4): 748-755.
[14]	Shuai SHAO, Aijun LIU, Changjun YU, Quanrui ZHAO. Polarization quaternion DOA estimation based on vector MISC array [J]. Journal of Systems Engineering and Electronics, 2021, 32(4): 764-778.
[15]	Yanan DU, Hongyuan GAO, Menghan CHEN. Direction of arrival estimation method based on quantum electromagnetic field optimization in the impulse noise [J]. Journal of Systems Engineering and Electronics, 2021, 32(3): 527-537.