In the development of modern electronic warfare, the UAV drone plays a critical role in radar reconnaissance missions. Our radar reconnaissance mission payload for a specific UAV drone integrates digital array direction-finding (DF) technology and digital interferometer technology to achieve high-gain detection and precise DF positioning of various radar signals. This payload is primarily designed to acquire intelligence about enemy radar targets, enabling identification, analysis, and localization with high sensitivity, high DF accuracy, wideband coverage, rapid response, and strong anti-interference capabilities. However, during installation, operation, and maintenance, numerous error sources affect DF accuracy. Among them, the amplitude and phase inconsistencies among receiving channels—from antennas to signal processing—are the most significant. These inconsistencies introduce phase errors that degrade DF performance. Therefore, it is essential to calibrate the mission payload to eliminate these errors and ensure system precision. Based on the DF principle and accuracy analysis, we researched the calibration technology and methods for the mission payload of a certain UAV drone. Through engineering practice, this approach effectively improves calibration efficiency and guarantees the system’s DF accuracy, offering significant engineering guidance and application value.
1. Direction-Finding Principle
The phase-based DF method determines the direction of arrival (DOA) by measuring the phase differences of signals received by multiple antenna elements. For a single baseline interferometer as shown in the fundamental configuration, the phase difference between two antennas is derived under the far-field assumption. The signal arrival angle θ relative to the antenna normal satisfies the following relationship:
$$ \phi = \frac{2\pi \Delta R}{\lambda} = 2\pi f \frac{L}{c} \sin\theta $$
where \( c \) is the speed of light, \( \lambda = c/f \) is the wavelength at frequency \( f \), and \( L \) is the baseline length (distance between phase centers of the two antennas). In practice, due to manufacturing tolerances, the phase center does not exactly coincide with the geometric center. Once the antenna array layout and signal frequency are fixed, the DOA can be derived from the measured phase difference. For multi-baseline linear arrays or two-dimensional planar/circular arrays, one channel is selected as the reference. When the array receives an incoming wave, the multi-channel receiver down-converts the RF signals to IF, then the digital acquisition and processing unit performs digitization, signal detection, parameter measurement, and subsequent processing to produce pulse descriptor words (PDW), DF results, and localization information.
For a UAV drone, the antenna array is often conformal with the fuselage skin, and the antenna elements are embedded inside the fuselage. The wide-beam antennas are significantly affected by the surrounding structure, which alters the amplitude and phase characteristics. Therefore, calibration is necessary to estimate and compensate for these initial phase errors.
2. Analysis of Direction-Finding Accuracy
Direction-finding accuracy is defined as the root-mean-square (RMS) statistical error of the measured DOA relative to the true direction of the incoming electromagnetic wave. To identify the error sources, we differentiate the phase equation:
$$ d\phi = \frac{\partial \phi}{\partial f} df + \frac{\partial \phi}{\partial L} dL + \frac{\partial \phi}{\partial \theta} d\theta = \frac{2\pi L \sin\theta}{c} df + \frac{2\pi f \sin\theta}{c} dL + \frac{2\pi f L \cos\theta}{c} d\theta $$
Converting differentials to incremental form:
$$ \Delta\phi = \frac{2\pi L \sin\theta}{c} \Delta f + \frac{2\pi f \sin\theta}{c} \Delta L + \frac{2\pi f L \cos\theta}{c} \Delta\theta $$
It is evident that DF errors originate from phase measurement error, frequency measurement error, and baseline measurement error. Once the antenna array is installed and fixed, the baseline length is essentially determined, and any deviation between the phase center and geometric center can be compensated through calibration. The frequency measurement accuracy of the payload can generally eliminate the contribution of frequency error. Therefore, the dominant factor is the phase measurement error, which is primarily caused by the amplitude and phase imbalance among receiving channels.
The amplitude and phase inconsistency in each channel arises from variations in analog components within the RF front-end, down-converters, and intermediate frequency modules. Figure 2 (conceptual diagram) illustrates the typical block diagram of a digital receiver channel. The external channel includes the radome, antenna, and RF cable up to the receiver front-end; the internal channel covers from the front-end to the digital processing unit. Even though all channels use identical circuit designs and component types, analog circuits inevitably exhibit slight differences in frequency-dependent amplitude and phase characteristics. Moreover, these imbalances can drift with operating frequency, voltage, temperature, humidity, device aging, and mechanical installation.
Therefore, the main contributions to the DF error include: amplitude and phase imbalances among channels, antenna element position errors, electromagnetic coupling between elements, individual element response variations, and near-field signal source incidence. During installation, the position of antenna elements and routing of RF cables must be carefully controlled to minimize these effects. In long-term operation, periodic calibration is required to maintain channel consistency.
The following table summarizes the major error sources and their impact on DF accuracy for the UAV drone payload:
| Error Source | Description | Impact on DF Accuracy |
|---|---|---|
| Amplitude inconsistency | Different channel gains lead to amplitude imbalance. | Introduces bias in phase measurement; degrades DF accuracy. |
| Phase inconsistency | Different channel phase shifts cause additional phase differences. | Directly contributes to phase measurement error; major source of DF error. |
| Antenna element position error | Misalignment of antenna phase centers relative to design baseline. | Alters the true baseline length, causing systematic DF offset. |
| Inter-element coupling | Mutual coupling between adjacent antenna elements. | Distorts the amplitude and phase response; introduces correlated errors. |
| Radome and fuselage effects | Conformal radome and embedded installation alter wavefront. | Causes frequency-dependent phase and amplitude distortion; requires external calibration. |
| Temperature and aging drift | Analog component parameters change with temperature and time. | Slowly varying errors that necessitate periodic recalibration. |
3. Calibration Principle and Method
Before installing the antenna array and RF cables on the UAV drone, the installation environment must be analyzed. Cables must maintain good contact, minimize bending, and respect minimum bend radius. Connector mating cycles should be limited, and after a certain number of disconnections, cable performance must be rechecked. Antenna elements for the same frequency band should be aligned along a straight line. The gap between the antenna mounting plate and the inner surface of the radome should be filled with absorbing material to suppress cavity resonance. Conductive adhesive tape is used to smooth edges and reduce scattering. The installation angle deviation relative to the aircraft body coordinate system must be measured and recorded; the correction values are stored in the system configuration software to automatically adjust the DF results.
The primary calibration approach uses an external radiating source to measure and compensate for channel imbalances. The calibration setup is conceptually illustrated (the figure is inserted at the appropriate location within the text). The idea is to select the frequency band of interest, configure the signal source with the same frequency and modulation as the target radar signals, and radiate via a standard horn antenna toward the UAV drone’s antenna array. One channel with the flattest amplitude and phase response across the band is chosen as the reference. The other channels are compared to this reference to obtain the amplitude and phase imbalance, including the phase error introduced by the entire external and internal paths. This yields a frequency response function, which is used to form an initial calibration table. The frequency step can be adjusted to refine the calibration data. After acquiring data for all frequencies, the calibration table is downloaded to the system software. During operation, the system automatically applies the correction factors to steer the DF result to true bearing.
After updating the calibration values, a field verification is performed using a moving cart with the radiating source at known angles (e.g., 20°, 30°, 45°). The measured amplitude differences between channels should remain stable and smooth, with no deep nulls; the phase differences should be continuous and consistent, without abrupt jumps. This confirms the validity of the calibration.
The calibration procedure requires an open area with flat terrain, free of reflections and obstructions. The distance between the calibration source and the UAV drone is typically at least 50 meters. To mitigate ground multipath reflections, absorbing material is laid between the source and the drone. The placement of the absorber significantly influences the calibration results; thus, the optimal configuration is determined by trial to maximize channel amplitude consistency.
Figure above shows a typical UAV drone used in our calibration test. The antenna array is embedded conformally with the fuselage, and the calibration source is positioned at a known distance and angle.
To illustrate the calibration process mathematically, let \( H_i(f) = A_i(f)e^{j\varphi_i(f)} \) be the transfer function of channel \( i \) (including antenna, cable, and receiver). The reference channel is denoted as \( H_{\text{ref}}(f) \). The complex calibration coefficient for channel \( i \) is:
$$ C_i(f) = \frac{H_{\text{ref}}(f)}{H_i(f)} = \frac{A_{\text{ref}}(f)}{A_i(f)} e^{j(\varphi_{\text{ref}}(f) – \varphi_i(f))} $$
The calibrated output of channel \( i \) is obtained by multiplying the raw measurement by \( C_i(f) \). In practice, the calibration coefficients are stored in a lookup table indexed by frequency bins. The step size \( \Delta f \) is typically chosen to be small enough (e.g., 10 MHz) to capture frequency variations.
We distinguish between internal calibration and external calibration. Internal calibration uses a known signal injected at the receiver input (after the antenna) to compensate for receiver chain imbalances. External calibration includes the antenna and radome effects. The combined calibration is performed in two steps: first, internal calibration establishes baseline coefficients; second, external calibration updates the coefficients to include the antenna/radome response.
4. Calibration Application and Verification
During the actual calibration of the radar reconnaissance payload on the UAV drone, we first determined the ground reference point. Using a laser level, we marked the antenna array baseline and the source baseline. The center points of the antenna array and the radiation source were precisely located on these baselines. For verification, we marked additional points at angles of 20°, 30°, and 45° from the normal. The distance from the source to the array center exceeded 50 m. To ensure accurate marking, we used auxiliary markers and measuring tapes.
Ground multipath effects were suppressed by laying microwave absorbing material between the source and the drone. The absorber covered an angular range of ±45° relative to the array normal. We iteratively adjusted the absorber placement while monitoring the channel amplitudes to minimize fluctuations. An example layout for the low-frequency band is illustrated in the procedure—typically, multiple layers of pyramid absorber were placed on the ground to reduce reflections.
The calibration process was carried out in two phases. First, internal calibration was performed to obtain the initial amplitude and phase differences among the receiver channels. Then, external calibration was executed to include the radome and antenna effects. After obtaining the calibration table, we conducted a verification at the normal direction (0°) for all frequency bands. If the measured DOA matched the expected 0° within the tolerance, the calibration was accepted; otherwise, the problematic frequency points were recalibrated until correct.
The following table lists a sample of calibration coefficients for one of the bands (2–18 GHz) obtained during one session. The reference channel is Channel 1.
| Frequency (GHz) | Channel 2 Amplitude Correction (dB) | Channel 2 Phase Correction (deg) | Channel 3 Amplitude Correction (dB) | Channel 3 Phase Correction (deg) |
|---|---|---|---|---|
| 2.0 | 0.12 | -1.5 | -0.08 | 2.3 |
| 4.0 | -0.25 | 0.8 | 0.15 | -1.1 |
| 6.0 | 0.05 | -0.3 | -0.10 | 0.6 |
| 8.0 | 0.30 | -2.1 | -0.22 | 1.8 |
| 10.0 | -0.10 | 1.2 | 0.08 | -0.9 |
| 12.0 | 0.18 | -0.7 | -0.05 | 0.4 |
| 14.0 | -0.22 | 1.6 | 0.12 | -1.3 |
| 16.0 | 0.08 | -0.9 | -0.15 | 1.0 |
| 18.0 | 0.25 | -1.8 | -0.20 | 2.1 |
After applying these coefficients, we performed a DF accuracy verification test using the moving cart method. The source was set to a center frequency of 2.8 GHz with a signal-to-noise ratio of 10 dB. The cart moved across a range of ±45° relative to the array normal while the payload recorded the measured DOA. The data were collected and statistically analyzed. The RMS DF error was found to be approximately 0.6° across the entire angular range, with no abrupt jumps or outliers. This result demonstrates that the calibration effectively corrected the channel imbalances and restored the system’s DF accuracy to the design specification.
In practice, the mission payload undergoes a full calibration whenever it is first installed on a UAV drone, or after replacement of any antenna or RF cable. For routine maintenance, only internal calibration is performed periodically to compensate for long-term drift. The software automatically applies the stored calibration tables to maintain consistent channel performance.
5. Conclusion
We have presented a comprehensive study on the calibration technology for radar reconnaissance mission payloads installed on UAV drones. Based on the fundamental principle of phase interferometry direction finding, we analyzed the error sources that degrade DF accuracy, highlighting the dominant role of amplitude and phase inconsistencies among receiving channels. A systematic calibration method using an external radiating source was developed and implemented. The process involves precise marking of ground baselines, mitigation of ground multipath through absorbing material placement, two-step internal/external calibration, and verification via moving cart tests.
Experimental results from an actual UAV drone calibration demonstrate that after applying the calibration coefficients, the RMS DF error is reduced to about 0.6° over a ±45° field of view, meeting the system requirements. The calibration procedure has been validated through flight tests, confirming accurate radar emitter direction finding and high localization precision. This work provides significant engineering guidance for maintaining the performance of radar reconnaissance UAV drones and is of practical value for electronic warfare systems.