A Comprehensive Review of New Technologies
December 2025
ABSTRACT
The period from 2020 to 2025 has witnessed unprecedented advances in radar technology, driven by convergence with artificial intelligence, quantum sensing, digital architectures, and advanced signal processing techniques. This comprehensive review examines major technical breakthroughs across multiple domains including quantum radar development, AI-enhanced signal processing, 4D imaging radar for autonomous vehicles, synthetic aperture radar (SAR) innovations, phased array radar systems, and biomedical applications. Key developments include quantum radar prototypes demonstrating 20% performance advantages, fully digital polarimetric phased arrays achieving 25% calibration improvement, MIMO architectures with up to 1728 virtual channels, and automotive radar markets projected to reach 500 million annual shipments by 2041.
Index Terms—Radar technology, quantum sensing, artificial intelligence, 4D imaging radar, synthetic aperture radar, MIMO, phased array radar, signal processing.
I. INTRODUCTION
Radar technology has experienced a remarkable transformation over the past five years, evolving from predominantly analog, mechanically-scanned systems to sophisticated digital, software-defined architectures integrated with artificial intelligence and quantum sensing capabilities. The fundamental drivers stem from military requirements for enhanced detection, civilian demands for autonomous vehicle safety, and expanding applications in environmental monitoring and biomedical diagnostics.
The fundamental radar range equation governs detection performance across all radar architectures:
Rmax = [Pt G² λ² σ / (4π)³ Pmin]|1/4 (1)
where R_max is maximum detection range, P_t is transmitted power, G is antenna gain, λ is wavelength, σ is radar cross section, and P_min is minimum detectable signal power. Modern advances focus on optimizing each parameter through digital beamforming, adaptive waveforms, and AI-enhanced signal processing [1], [2].
II. QUANTUM RADAR: FROM LABORATORY TO PRODUCTION
A. Theoretical Foundations and Quantum Illumination
Quantum radar represents one of the most significant paradigm shifts in radar technology, leveraging quantum mechanical phenomena such as entanglement and quantum illumination to achieve detection capabilities beyond classical radar limitations. The theoretical foundations predict quantum advantage in target detection under conditions of high thermal noise and signal loss [6], [7].
The quantum illumination protocol generates entangled signal-idler photon pairs. The signal-to-noise ratio advantage for quantum illumination over classical radar is given by:
SNRQI / SNRclassical ≈ exp(NS) (2)
where N_S represents the mean signal photon number. This exponential advantage becomes significant in the low signal regime typical of long-range detection scenarios. The quantum correlation between signal and idler photons enables superior discrimination of target returns from thermal background noise through joint measurement strategies.
B. Recent Technical Advances and Performance
In 2023-2024, research teams achieved quantum radar performance improvements of approximately 20% over classical systems in controlled experimental conditions [10]. Chinese research institutions have reported mass production of ultra-low-noise, four-channel single-photon detectors [11]. The global quantum radar market demonstrates substantial growth potential, valued at approximately $309 million in 2024 with projections reaching $662 million by 2031, representing a CAGR of 7.4% [12].
III. FULLY DIGITAL PHASED ARRAY RADAR SYSTEMS
A. Polarimetric Weather Radar and Array Theory
Fully digital phased array radar technology represents a transformative advancement for meteorological applications. The Horus radar, developed by the Advanced Radar Research Center at the University of Oklahoma, is a truck-based S-band fully digital polarimetric phased array radar designed to assess replacement potential for the WSR-88D operational weather radar network [14].
The array factor for an N-element phased array with element spacing d is expressed as:
AF(θ) = Σn=0|N-1 a|n exp[j(kd sin θ + φn)] (3)
where a_n represents element amplitude weights, k = 2π/λ is the wavenumber, θ is the scan angle, and φ_n denotes the phase excitation for beam steering. Digital beamforming enables independent control of each element's amplitude and phase, facilitating simultaneous multi-beam operation and adaptive null steering.
The holographic back-projection method addresses calibration challenges by deriving copolar magnitude and phase of horizontally and vertically polarized fields. For electronically steered beams, back-projection calibration reduces H/V beam mismatch biases by approximately 25%, from 0.34 dB to 0.08 dB [15].
B. Doppler Processing Techniques
The Doppler frequency shift for a target with radial velocity v_r is given by:
fD = 2vr / λ (4)
Recent developments in Doppler velocity estimation for polarimetric weather radars demonstrate performance improvements through integration of dual-polarization data. The coherent time-series stitching method provides the most accurate velocity estimates, demonstrating significant improvements in estimate variance across diverse weather signal conditions [16].
IV. ARTIFICIAL INTELLIGENCE INTEGRATION IN RADAR SYSTEMS
A. Machine Learning for Signal Processing
Artificial intelligence, particularly deep learning techniques, has fundamentally transformed radar signal processing capabilities. Convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs) enable radar systems to achieve performance levels previously unattainable through classical signal processing [19], [20].
The matched filter output for radar signal detection is expressed as:
y(t) = ∫ x(τ) h(t - τ) dτ (5)
where x(t) is the received signal and h(t) is the matched filter impulse response. Deep neural networks learn optimal non-linear transformations that exceed traditional matched filtering performance under complex clutter conditions, achieving classification accuracies exceeding 90% for multi-class target discrimination.
B. Adaptive Electronic Counter-Countermeasures
Machine learning enables sophisticated ECCM capabilities through real-time jamming pattern detection. The signal-to-jamming-plus-noise ratio (SJNR) for adaptive systems is:
SJNR = PS / (PJ + PN) (6)
where P_S is signal power, P_J is jammer power, and P_N is noise power. AI-based adaptive ECCM systems dynamically adjust frequency, power, and beam characteristics on a pulse-to-pulse basis, maintaining operational effectiveness against sophisticated electronic warfare threats [23], [24].
V. 4D IMAGING RADAR FOR AUTONOMOUS VEHICLES
A. MIMO Radar Principles and Virtual Apertures
4D imaging radar represents a transformative advance in automotive perception technology, adding elevation resolution and velocity measurement to conventional capabilities. Multiple-input multiple-output (MIMO) radar creates a virtual array by exploiting waveform diversity across transmit elements [29], [30].
For a MIMO radar with M transmitters and N receivers, the number of virtual channels is:
Nvirtual = M × N (7)
The angular resolution improvement is proportional to the virtual aperture size. For a virtual aperture length L_virtual, the azimuth resolution Δθ is approximated by:
Δθ ≈ λ / (2Lvirtual) (8)
Advanced implementations include 79 GHz MIMO radars with 1728 channels providing high-resolution 4D snapshots containing range, azimuth, elevation, and velocity information simultaneously [31].
The range resolution for FMCW radar with bandwidth B is:
ΔR = c / (2B) (9)
where c is the speed of light. Modern systems achieve sub-centimeter range resolution through multi-GHz bandwidth operation at millimeter-wave frequencies, with range capabilities exceeding 380 meters [33].
B. Market Growth and Applications
The automotive 4D imaging radar market demonstrates explosive growth, transitioning from $87-393M in 2025 to projected $1.2-195B by 2030-2034, representing CAGRs between 25% and 93% [36]-[38]. By 2025, 4D radar is expected to achieve 11.4% penetration of the automotive radar market. Robotaxi platforms demonstrate heavy reliance: Cruise deploys 21 radars per vehicle while Waymo utilizes six high-performance 4D imaging radars [41].
VI. SYNTHETIC APERTURE RADAR ADVANCES
A. SAR Resolution and HRWS Systems
Synthetic aperture radar technology has overcome traditional limitations through High Resolution Wide Swath (HRWS) SAR architectures [44], [45]. The azimuth resolution for SAR is determined by:
δaz = Lant / 2 (10)
where L_ant is the physical antenna length. This remarkable result shows SAR azimuth resolution is independent of range and wavelength, determined solely by antenna aperture.
The slant range resolution is given by:
δr = c / (2Br cos θ) (11)
where B_r is the transmitted signal bandwidth and θ is the incidence angle. Modern SAR systems employ chirp bandwidths exceeding 1 GHz to achieve sub-meter resolution.
B. Interferometric SAR for Deformation Monitoring
Interferometric SAR (InSAR) measures surface deformation by comparing phase differences between two SAR acquisitions. The line-of-sight displacement d is related to the interferometric phase φ by:
d = (λ / 4π) φ (12)
For C-band SAR (λ ≈ 5.6 cm), each 2π phase cycle corresponds to approximately 2.8 cm displacement. PIESAT-1 achieves elevation accuracy of 3-7 meters and deformation accuracy of 3-5 mm/year [52]. Modern InSAR systems achieve millimeter-level deformation accuracy through advanced processing techniques.
C. Market Growth and Applications
The SAR market demonstrates robust growth, with revenues increasing from $2.93 billion in 2023 to projected $7.33 billion by 2033 [55], [56]. X-band radar holds approximately one-third of market share due to high-resolution capabilities. New satellite constellations dramatically increase data availability: Sentinel-1C launched December 5, 2024; PIESAT-1 launched March 30, 2023; and AIRSAT-08 launched December 2024 as China's first ultra-low-orbit SAR satellite [50]-[53].
VII. BIOMEDICAL AND SPECIALIZED RADAR APPLICATIONS
A. Clinical Monitoring Systems
Biomedical radar has emerged as a transformative non-contact monitoring technology for clinical applications. Systems operating at 120 GHz enable cuffless blood pressure measurement achieving 93.71% accuracy for systolic blood pressure and 99.39% accuracy for diastolic measurements through pulse transit time analysis [63]. Radar-based vital sign monitoring provides continuous assessment of heart rate, respiration, and sleep patterns without physical contact with patients.
For vital sign monitoring, the radar detects chest wall displacement Δx(t) caused by respiration and heartbeat. The phase shift Δφ in the reflected signal is proportional to displacement:
Δφ = (4π / λ) Δx(t) (13)
This phase modulation enables extraction of respiratory and cardiac waveforms for non-invasive monitoring. Machine learning integration enables automated detection of respiratory disorders including obstructive sleep apnea, central sleep apnea, and hypopnea [64], [65].
VIII. CONCLUSION
The 2020-2025 period represents a transformative era in radar technology characterized by convergence of quantum physics, artificial intelligence, digital signal processing, and advanced semiconductor technology. The fundamental radar range equation (Eq. 1) continues to govern detection performance, but modern implementations optimize each parameter through revolutionary approaches:
Quantum radar: Exponential SNR advantage through quantum illumination (Eq. 2)
Digital phased arrays: Element-level control enabling arbitrary beam patterns (Eq. 3)
MIMO virtual apertures: Linear multiplication of resolution (Eq. 7-8)
FMCW ranging: Sub-centimeter resolution through GHz bandwidths (Eq. 9)
SAR processing: Range-independent azimuth resolution (Eq. 10)
InSAR deformation: Millimeter-precision through phase coherence (Eq. 12)
AI optimization: Exceeding matched filter performance through deep learning (Eq. 5-6)
Market projections indicate sustained double-digit growth: quantum radar ($309M to $662M by 2031, 7.4% CAGR), 4D imaging radar ($87-393M to $1.2-195B by 2030-2034, 25-93% CAGR), automotive radar (140M to 500M units by 2041), and SAR ($2.93B to $7.33B by 2033). The mathematical foundations established over decades remain valid, but implementation through quantum sensing, AI processing, and digital beamforming enables capabilities exceeding previous systems by orders of magnitude.
APPENDIX: EQUATION SUMMARY
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