Radar Amplifiers: Classical to Quantum

• Radar amplifiers are specialized devices or modules that boost radar signal power, sensitivity, and feature quality using optimized RF, quantum, and AI techniques.
• Classical implementations use vacuum tubes and semiconductor LNAs to extend detection range and reduce noise, achieving outputs like 10–200 kW and gains of 9 dB for radar-on-chip systems.
• Quantum and feature-level amplifiers utilize JPAs, JTWPA, and attention-driven neural modules to deliver near-quantum-limited performance and improved occupancy prediction in multimodal fusion.

A radar amplifier is a specialized electronic device or algorithmic module that boosts the power, sensitivity, or representational quality of radar signals for transmission, reception, or downstream processing. In classical radar, amplifiers—whether high-power RF devices such as klystrons, magnetrons, or semiconductor LNAs—serve to increase signal amplitude, extend detection range, and maintain low system noise figures. In modern computational pipelines (e.g., camera-radar fusion for occupancy prediction), “radar amplifier” refers to modules that enhance data features by selective weighting, attention, or enrichment. The following sections detail fundamental physical implementations, advanced quantum designs, feature-level amplifiers for multimodal radar-AI systems, and comparative empirical results across representative research.

1. Physical Principles and Classical Radar Amplifiers

Radar amplifiers in classical systems primarily convert low-level RF signals to higher power for transmission or boost received echoes prior to digitization.

• Vacuum tube amplifiers (tetrodes, klystrons, IOTs, magnetrons) are renowned for high continuous-wave (CW) or pulsed output. Example device parameters:
  • Tetrode: 10–200 kW (CW), 60–75% efficiency, VHF–UHF bands (Carter, 2013).
  • Klystron: 50 kW–2 MW (CW), 40–70% efficiency, S/X/Ku bands.
  • Design equations for tetrode output power:

  $$I_a = C\,\bigl(V_{g1} + \tfrac{V_{g2}}{\mu_2} + \tfrac{V_a}{\mu_a}\bigr)^n$$

  Practical efficiency correction yields $\eta_{\text{practical}}\approx70\%-75\%$.

• Solid-state amplifiers (LNA, HEMT, CMOS) dominate at lower powers and in integrated circuits.

  • Example: A 2–6 GHz UWB CMOS LNA with 9 dB gain, 2.5 dB noise figure, and 11 mW power consumption for radar-on-chip front-ends (Tharakan et al., 8 Mar 2025).

Amplifiers require carefully engineered impedance matching, thermal management, and protection circuits to maintain linearity and prevent system damage. For instance, a TARA VHF cosmic-ray radar transmitter combines analog broadcast PAs, achieving 40 kW CW output and 30% efficiency, with excellent harmonic rejection (>60 dB) (Abbasi et al., 2014).

2. Quantum Radar Amplifiers: JPAs and JTWPA

Quantum-enhanced radar systems employ Josephson Parametric Amplifiers (JPA), Engineered JPAs (EJPA), and Josephson Traveling Wave Parametric Amplifiers (JTWPA) to achieve near-quantum-limited added noise, entanglement between signal and idler modes, and dramatically higher sensitivity compared to classical amplifiers.

• JPA (standard and engineered) architecture:
  • Superconducting microwave resonator, modulated by a SQUID, creates two-mode squeezed vacuum (TMSV) states (Luong et al., 2019, Hosseiny et al., 2022).
  • Gain: $G_{\text{JPA}}\approx20$ dB, bandwidth (standard) $\approx1$ MHz, (engineered) $\approx300$ MHz.
  • Added noise: $n_{\text{add}}\approx\frac12$ photon at input, approaching the quantum minimum (Caves’ theorem).
• JTWPA:
  • Constructed from hundreds of rf-SQUID cells in coplanar-waveguide metamaterial; operates in three-wave mixing regime via DC flux biasing (Livreri et al., 2021).
  • Empirical metrics: $G(\omega)$ = 20–25 dB, $BW_{3\text{dB}}$ = 10 GHz, noise figure $NF=0.5$ dB (X-band).
  • Amplifies both outgoing entangled radar modes and weak returns across wide bandwidth, directly enabling quantum illumination with $M=BT$ mode pairs ($B\sim10$ GHz).

A plausible implication is that broadband quantum amplifiers (JTWPA) scale detection advantage exponentially with mode number, outperforming both classical HEMTs and narrowband JPAs for stealth target detection, provided cryogenic infrastructure is available.

3. Feature-Level Amplifiers in Radar-AI Fusion Pipelines

In multimodal deep learning-based radar systems, “amplifier” denotes a feature processing block that channels attention to high-quality signal features and suppresses noisy dimensions. The REOcc network introduces the Radar Amplifier for 3D occupancy prediction in camera-radar fusion (Song et al., 10 Nov 2025).

• Architectural position: Downstream of a Radar Densifier, which spatially enriches sparse pillar features; the Radar Amplifier acts on the densified radar tensor $F_{den}^{rad}\in\mathbb{R}^{C_R\times H\times W}$.
• Mathematical formulation:

$$F_{amp} = F_{den}^{rad}\, \oplus \bigl(\Phi_{\text{prob}}(F_{den}^{rad})\,\odot\,F_{den}^{rad}\bigr)$$

where $\oplus$ concatenates channels, $\odot$ is element-wise multiplication, and $\Phi_{\text{prob}}$ is a two-layer MLP producing softmax-normalized channel probabilities.

• Contextual enrichment: The module computes global average pooling over spatial dimensions, learns channel-wise importance scores via a small MLP, then multiplies these scores back into each feature, concatenating original and weighted features.

This design enables the network to assign focus to channels carrying salient occupancy cues, leveraging global context and suppressing noise. No parameters are shared with the Densifier; both modules act consecutively.

4. Impedance Tuning and Amplifier-System Co-Optimization

Element-wise impedance tuning in active electronically scanned array (AESA) radars allows each power amplifier to maintain optimum power delivery, compensating for array mutual coupling-induced impedance variations (Rodriguez-Garcia et al., 2020).

• Switched-stub tuner topology: Each array element includes a six-stub radial-line network, yielding $2^6=64$ possible states per element, implemented with high-power RF switches.
• Optimization: A greedy real-time algorithm toggles stubs, maximizing output power while using the same switch state across all elements to preserve array pattern integrity.
• Performance gains: Tuning yields a $+18\%$ to $+27\%$ range improvement versus untuned states across steering angles, with negligible impact on sidelobe level or main-beam shape.

This approach ensures high radar efficiency and extended operational range without pattern degradation, relevant for both classical and MIMO radar architectures.

5. Empirical Results and Technological Comparisons

The following table synthesizes key amplifier metrics across representative physical, quantum, and feature-level implementations:

Technology/Module	Gain (dB)	Bandwidth	Noise Figure
Tetrode/Klystron RF PA	12–18/40–60	MHz–GHz (narrow)	~6–20 dB
HEMT (semiconductor)	>40	>10 GHz	1–3 K (>> quantum)
JPA (quantum)	~20	1–300 MHz	~0.2–0.5 dB
JTWPA (quantum)	20–25	10 GHz	~0.5 dB
REOcc Radar Amplifier	N/A	Feature domain	N/A (improves mIoU)

• In quantum radar, JTWPA enables up to $M\sim10^3–10^6$ entangled mode pairs, directly boosting the detection advantage by reducing error probability proportionally to $M$ (Livreri et al., 2021).
• In camera-radar fusion, inclusion of the Radar Amplifier yields a $+1.27$ mIoU gain (all classes) for 3D occupancy prediction over baseline, with combined densifier–amplifier providing over $+2.3$ mIoU (Song et al., 10 Nov 2025).

6. Implementation Challenges and Future Directions

Radar amplifier evolution faces distinct challenges across physical and algorithmic domains.

• Physical quantum amplifiers: Require sub-100 mK cryogenics, tight phase matching, suppression of gain ripple, and isolation against spurious harmonics. Future work includes integration of on-chip nonreciprocal elements and extension to higher frequency bands.
• Solid-state LNAs: Must balance gain, bandwidth, power consumption (e.g., 11 mW for CMOS), and layout-induced parasitics for radar-on-chip scalability (Tharakan et al., 8 Mar 2025).
• Feature-level amplifiers in AI: Depend on channel-attention mechanisms, MLP parameterization, and seamless fusion interfaces. Architecture must support end-to-end backpropagation for selection of salient radar features, and robustness against noise and data sparsity.

A plausible implication is that as radar tasks converge quantum sources, agile impedance control, and deep fusion amplifiers, co-optimization of hardware and data-driven modules will be required for next-generation detection, imaging, and autonomy systems.

7. Conclusion

Radar amplifiers encompass a wide spectrum of devices and modules, from high-power RF tubes and semiconductor front-ends to quantum-limited superconducting parametric amplifiers and context-enriching feature-level algorithms. Each class uniquely addresses the requirements of power gain, noise suppression, bandwidth, and detection sensitivity, with quantum and AI-driven approaches enabling new operational regimes. Empirical advances include the deployment of JTWPA for microwave quantum radar (10 GHz bandwidth, $0.5$ dB noise figure) (Livreri et al., 2021), engineered JPAs for kilometer-scale QTMS detection (Hosseiny et al., 2022), CMOS UWB LNAs for compact radar transceivers (Tharakan et al., 8 Mar 2025), and channel-attention Radar Amplifiers that boost occupancy prediction accuracy in multimodal fusion (Song et al., 10 Nov 2025). The evolutionary trajectory points toward hybrid systems integrating the strengths of each domain, subject to persistent engineering, algorithmic, and system-level constraints.