Overcoming the Challenges of Designing and Testing Phased-Array Radar Systems
By Dingqing Lu, Agilent Technologies
Phased array is widely used in modern radar systems for rapid multi-target search and track operations, as well as to achieve higher resolution and better detection performance. Despite these enviable benefits, developing phased-array radar is no easy proposition. For today’s engineers, it translates into a myriad of test challenges, not the least of which is finding a way to improve performance while also reducing the high costs associated with transmit/receive (T/R) modules with Direct Digital Synthesizers (DDSs), digital-to-analog converters (DACs), and analog-to-digital converters (ADCs). Also of concern to the engineer is finding a way to work effectively with the entire development team—the system architect, the RF team and the signal processing team. Additionally, calibration of the transmit/receive (T/R) module can be difficult, not to mention time consuming and expensive. Addressing these challenges demands an appropriate method of designing and testing phased-array radar systems; one that streamlines the R&D lifecycle so that faster, cheaper, better phased-array radar systems can be achieved.
Phased-Array Radar Design: The Basics
There are two types of phased-array radar systems: passive and active (Figure 1). In a passive system, a baseband source is connected to a single large transmitter (Tx) with a high power amplifier (HPA). The Tx, in turn, is connected to a beamformer followed by antenna units, the return signals of which are connected to a single receiver (Rx) and subsequently, to the baseband receiver. In passive systems, the signal loss between radiating elements and the transmitter/receiver can be quite large. However, because passive antenna systems have a central radio frequency source, developing a radar system based on a passive electronically scanned array (PESA) is a fairly straightforward process.
The same cannot be said of radar based on an active electronically scanned array (AESA). In contrast to a PESA radar, AESA devices have T/R modules containing small Tx and Rx designs located behind each radiating element and the baseband source is connected to the beamformer. Transmitter power is distributed through many small PAs to the antennas, while the baseband receiver receives signals through antennas in many small, low noise amplifiers (LNAs). In an active system, the signal loss between the PA/LNA and the radiating element is much smaller than in a passive system. Electronic scanning is therefore used, which enables faster, more flexible searching. However, because each module contains its own radio frequency source, development of AESA radars is substantially more complex.
The Platform Solution
Dealing with the complexity of AESA radar development, while also addressing the traditional problems and challenges associated with developing a phased-array radar, requires a platform solution that enables effective design and test at all stages of the development process (Figure 2). The ideal platform solution relies on simulation as its foundation and features a number of key characteristics, including cross-domain simulation with RF, as well as EM, and the ability to measure both 3D and 2D antenna patterns. Measured antenna patterns, coupled with Tx measurements (e.g., waveform, spectrum and time-side-lobes) and Rx measurements (e.g., detection rate and false alarm rate) can be used for performance validation.
The platform solution also offers trade-off analysis, T/R module and antenna units’ failure analysis, and adaptive algorithm creation support. Additionally, it features links to test equipment (e.g., a signal generator, arbitrary waveform generator (AWG) and signal analyzer) for hardware testing, along with support for integrated test. The links allow data to be downloaded to an AWG for testing RF signals, and for hardware signals to be acquired and sent back to simulation for post analysis.
For any radar system test solution a software core is needed to integrate all test software and hardware together, as well as to automate the test. In Figure 3, a radar system test platform is proposed based on this idea. The software integrates all test instruments together as a test system that provides complex radar test signals with environment scenarios to the device-under-test (DUT), to capture DUT outputs and then synchronize signals, post process it to extract more information, and obtain advanced measurements like detection rate, false alarm rate and imaging analysis. Without the integration and synchronization, each instrument would function on its own, making it impossible to perform such complex tests.
The simulation-based platform solution must also support the key models used in phased-array radar systems:
• DDS Model: The DDS model is a key model for any digital radar and is frequently used in AESA radar for T/R module design. It uses a digital radar source to generate digital waveforms, such as Continuous Wave (CW), pulse, LFM pulse, stepped pulse, and stepped LFM from the downloaded I, Q waveforms, LFM, CW, pulse and LFM pulse.
• Target Model: When evaluating receiver performance, the radar environment has to be considered, which makes creating a practical target model very important. Generally speaking, radar environments include terrain and sea surfaces, the atmosphere (including precipitation), and the ionosphere. These conditions may degrade the radar’s observation ability and performance by producing clutter and other spurious returns, signal attenuation, and bending the radar signal path (e.g., caused by RCS, Doppler, delay, attenuation, and propagation effects).
While free space analysis may be adequate to provide a general understanding of a radar system, it is only an approximation. To accurately predict radar performance, the free space analysis must be modified to include the effects of the earth and its atmosphere. Note that radar clutter is not considered as part of this analysis because it almost always is assumed to be a distributed target that can be dealt with separately by the radar signal processor.
• Clutter Model: A clutter model is used to model the unwanted echoes in radar systems. These echoes are typically returned from ground, sea, rain, animals/insects, chaff, and atmospheric turbulences, and can cause serious performance issues with radar systems. Clutter can be best modeled using a statistical approach that combines probability density functions (PDF) for clutter amplitude and clutter power spectrum density (PSD). The PDF is used for the time-domain statistical property description, while the PSD is used for the frequency-domain description. Both are suitable for describing the effects of the radar environment. The K-clutter model is another important statistical model and is used for sea and round clutters.
• Antenna Array Model: Array antenna models for the Tx and Rx allow the designer to specify the arbitrary geometry of the antenna pattern. It can also be calculated based on the size of the antenna and illuminating window function, including uniform, cosine, parabolic, triangle, circular, cosine square, and Taylor.
• Beamformer Model: One of the key technical problems of phased arrays is beamforming. To sum all signals from the array antenna coherently, the time delay of the signal received by the antenna element at the position has to be compensated. A beamforming model can be used to help ensure the beamforming technique is optimally implemented in a phased-array radar system.
The Platform in Action
Assuming the simulation-based solution has these features, it will provide the ideal platform for designing and testing phased-array radar systems throughout all stages of the development process.
And, it does so while addressing the challenges that come with that development process.
For example, the platform’s cross-domain simulation capability enables high accuracy simulation to be performed, which is necessary to allow system architects and the RF and signal processing teams to work together. The trade-off analysis helps reduce the cost of T/R modules and DDS with higher digit DAC/ADC components. This allows designers to find a proper DAC or ADC for T/R parts to satisfy the performance requirements with the smallest digit. Adaptive algorithm support addresses the challenge of T/R module calibration by detecting and correcting for amplitude and phase errors.
If the simulation platform features both emulation and virtual test environments to account for clutter and interference, the cost of field testing can also be significantly reduced. And, if the solution has an integration capability, it can monitor subsystems and components to ensure they work properly at all stages. Moreover, if the solution features model-based simulation, as opposed to function-based simulation, designers can use different languages and software to create models and then integrate the models together to share among themselves. This allows designers to test early in the development cycle, thereby reducing overall development time.
The Bottom Line
Developing phased-array radar systems is challenging, especially if it involves an AESA device. In this case, designers require a much more integrated solution with a wide breadth of functionality. Luckily, use of a platform solution that relies on simulation as its foundation now offers designers an effective strategy for attacking the challenges they face when designing, verifying and testing phased-array radar systems. Utilizing this approach not only reduces design cycles, but also significantly reduces cost.
About the Author
Dingqing Lu has been with Agilent Technologies/Hewlett Packard Company since 1989 and is a scientist with Agilent EEsof EDA. From 1981 to 1986 he worked at the University of Sichuan as a lecturer and assistant professor. He was also a research associate in the Department of Electrical Engineering at the University of California (UCLA) from 1986 to 1989. He is an IEEE senior member and has published 20 papers in IEEE transactions, journals and international conference proceedings. He holds a United States patent for a fast DSP search algorithm. His research interests include system modeling, simulation and measurement techniques for radar, and communication systems.
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