Radar is used to detect and/or track target objects and their attributes, such as range, speed, and other information obtained through signals at RF and microwave frequencies. The broad classes of radar systems are active and passive (Figure 1). Passive radar systems use non-cooperative source(s) of illumination, such as a target’s emitted signals, broadcast signals, or cellular communication signals, to obtain information about the target. Since radar performance relies on the sensing capabilities of the receiver, significant innovations have been made in areas such as phased array antennas, digital beam-forming, detection algorithms, and source separation algorithms. Active radar uses cooperative sources of illumination by generating its own signal(s) to illuminate the target. Within the class of active radar, there is monostatic radar, where the signal source is collocated with the receiver, and multistatic radar, where there are two or more receiver locations.

Figure 1. Passive radar (left) and active radar (right).

Among active radar systems, there are several common signal types. The most basic is continuous wave (CW) radar, where a constant frequency sinusoidal signal is transmitted. The CW signal allows the receiver to detect phase/frequency variations (Doppler shift) from the target reflection. Unless a special provision for absolute time marker is used, however, range detection is not possible. A modified CW signal using a stepped frequency modulated (SFM) signal obtains a better range estimate by hopping over multiple discrete frequencies. A further modification of the CW signal to linearly ramp up and down a range of frequencies is called linear frequency modulation (LFM) or frequency modulated CW (Figure 2). An LFM radar allows detection of Doppler as well as range by observing the frequency difference of the time-delayed received signal from the transmitted signal. If a stationary object is detected, a constant beat frequency (transmit to receive frequency difference) is observed.

Without loss of generality, today’s active radar system design can be broken into two major components: the baseband signal processing and the RF/microwave front end (including the antenna). Figure 3 shows a high-level block diagram of an active radar system.

Digital Signal Processing

Figure 2. Example LFM waveform where a pulse ramps from high frequency to low, and back again.
Thanks to widely available commercial processors, embedded processors, field-programmable gate arrays (FPGA), digital signal processors (DSP), and, more recently, graphics processing units (GPU), radar signal processing engineers now have a breadth of platforms from which to choose. The choice largely depends on the type of signal processing that is needed and the cost of implementation. General-purpose, personal computer-type hardware may be sufficient for radar systems with relatively low throughput and simple signal processing requirements. An FPGA or GPUbased processor might be needed if large parallel processing is needed. In this case, however, the cost of the hardware increases substantially. In most platforms, pulse generation and receiver algorithms can be implemented with appropriate software, bringing benefits such as programmability and reuse of intellectual property. At the same time, radar signal processing engineers are faced with the challenge of incorporating more and more sophisticated algorithms, consuming longer simulation times within the system and exhausting the available computational resources.

Figure 3. General radar block diagram.
While each of the processing blocks has relatively simple functions, it becomes a complex task to integrate these algorithms, partition them onto appropriate platforms, coordinate and communicate with the RF/microwave front end, and compensate for its non-idealities. Does it make sense to implement all of the processing in the FPGA at the cost of hardware, development time, and less flexibility? Does it make sense to implement all of the processing on the CPU, perhaps at the cost of performance? Or does it make sense to partition the algorithms for the CPU and FPGA (and maybe DSP) such that each algorithm is run in some optimal way? If so, what are the throughput, latency, and synchronization requirements between these processing units? These are some of the challenges faced by radar signal processing engineers in developing the next generation of radar systems.

RF/Microwave Front End

The transmitter and receiver unit (Figure 4) plays a key role in acquiring the information for processing. Many radar systems today operate at S-Band (2 to 4 GHz), X-Band (8 to 12 GHz), and higher. Design choices for the transmitter upconverter and receiver downconverter depend on many factors, such as the target frequency range, available local oscillators, interfaces to the DAC and ADC, phase/amplitude control, and cost. Perhaps the most heavily researched areas are high power amplifier (HPA) and low noise amplifier (LNA) design.

Figure 4. High-level block diagram of a radar front-end transmit and receive unit.
The HPA performs the critical function of amplifying the illumination signal to the highest power permitted without adding distortion, while having high enough efficiency to maintain power consumption within specified limits. Depending on the application, transmit power levels can range from milliwatts to kilowatts. Linearity of the power amplifier is of great importance since nonlinearity can cause pulse degradation and introduce spurs that violate spectral mask requirements or corrupt the receive signal. In recent years, field plate technology has allowed GaAs HPAs to be operated at higher voltages. Field plate technology and air-bridges increase the breakdown voltages of high electron mobility transistors (HEMT). Increased power density, however, introduces heat dissipation issues. Field plate technology has been used with devices that already support higher voltages and power densities, such as gallium nitride (GaN) on silicon carbide (SiC) substrates. PA designers are faced with a multitude of problems in trading off devices, component count, thermal management, and miniaturization, all while satisfying the amplification and spurious emission requirements.

Figure 5. Link budget analysis using the National Instruments AWR Visual System Simulator.
While LNA design is relatively mature, its performance is crucial to achieving front-end sensitivity and overall radar performance goals. Link budget and noise figure analyses have historically been performed with simple hand calculations or spreadsheets; however, use of a graphical tool like the NI AWR Visual System Simulator (VSS) or similar, greatly enhances the designer’s ability to close in on the specification and spot problem areas (Figure 5).

Integration: Putting it All Together

Figure 6. An example of an integrated tool chain by the National Instruments AWR Visual System Simulator.
The radar system architect has the enormous task of understanding various tradeoffs in the digital domain as well as in the RF/microwave domain, and putting it all together. Many years ago, radar systems might have been designed and integrated by a small team of hardware engineers, but today’s radar systems are becoming increasingly complex with domain experts from several areas contributing to the development of one system. How can an algorithmic tradeoff be adequately balanced with microwave circuit requirements and cost? There is clearly a greater need for mixed digital and RF/microwave design, simulation, and a prototype framework so that the corresponding domain experts can communicate with each other to address this complex design problem. One approach is to consider a well-integrated tool chain that supports microwave design, digital signal processing, hardware-in-the-loop (either hardware-based processing such as on FPGAs or measurements), and the corresponding hardware capability to support rapid prototyping of designs (Figure 6). Various systems software packages allow multiple designers to easily create and evaluate subsystem architectures, bringing their designs from concept to simulation and, ultimately, to physical implementation in a single system within a single framework.

Conclusion

Today’s radar systems are as complex as they are diverse. What is common, however, is that they each contain a digital signal processing section and RF/microwave front end. In this article, we looked at a few key elements in both of these areas with examples for pulse compression radar and discussed several technology challenges as well. While radar systems previously were developed by a few hardware engineers, today’s systems often rely on the design contributions of multiple domain experts. Various software tools simplify the complexity of the design process and allow engineers to think across the traditional boundaries.

This article was written by Dr. Takao Inoue, Senior RF Platform Engineer, and Phyllis Cosentino, Senior Product Marketing Manager, at National Instruments in Austin, TX. For more information, Click Here.

References

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  2. J. Hasch, E. Topak, R. Schnabel, T. Zwick, R. Weigel and C. Waldschmidt, “Milli meter-Wave Technology for Auto motive Radar Sensors in the 77 GHz Frequency Band,” IEEE Transactions on Microwave Theory and Techniques, Vol. 60, No. 3, March 2012, pp. 845, 860.
  3. C. Li, J. Cummings, J. Lam, E. Graves and W. Wu, “Radar Remote Monitoring of Vital Signs,” IEEE Microwave Magazine, Vol. 10, No. 1, February 2009, pp. 47-56. C. Gentner, E. Munoz, M. Khider, E. Staudinger, S. Sand and A. Dammann, “Particle Filter Based Positioning With 3GPP-LTE in Indoor Environments,” IEEE/ION Position Location and Navigation Symposium Proceedings, April 2012, pp. 301, 308

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This article first appeared in the June, 2014 issue of Aerospace & Defense Technology Magazine.

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