Efficient, safe radar signal capturing, processing

On the way to autonomous driving advanced driver assistance systems (ADAS) based on vision, LIDAR and radar have to gradually supersede the driver's visual sense. To achieve this challenging goal ADAS sensors have to further evolve to become more reliable, more accurate, safer and more efficient. This article focuses on automotive radar and specifically discusses signal processing steps of a modern fast chirp radar system. An example shows how radar signal capturing and processing can be realised in an efficient and safe way. Additional automotive radar aspects like low power, small form factor and scalability are also touched.

Automotive radar evolution
Radar has a substantial history in Automotive of more than two decades. In the early days radar was mainly used for applications like adaptive cruise control (ACC), which was an optional comfort feature in luxury cars. Radar systems were widely used in the military and avionics industry due to its capability to directly measure relative position and relative speed of objects in the free space. However, in the high-volume automotive market radar technology was not very attractive because of high cost.

Over the last decade advancements in micro-wave chip sets and digital signal processing devices enabled a significant cost reduction of radar systems. Meanwhile car makers start to offer car models with radar sensors as standard equipment. Besides ACC, radar sensors are used for a manifold of use cases like autonomous emergency brake, blind spot detection and rear or front collision warning. Beyond cost reduction, new radar sensors have to fulfil ever increasing technical requirements, e.g. larger detection range, higher range resolution, wider angle of view and improved object discrimination with multi-target detection capability. On top of these system requirements also other automotive-specific aspects like functional safety, lower power and reduction of form factor have to be taken into account.

In former automotive radar sensors FMCW (frequency modulated continuous wave) technique with slow ramps (aka chirps) has been used, because of restrictions in the RF front-end chip set and limited availability of base band processing chips for the harsh automotive environment. This technique was rather slow (e.g. 30ms chirp time) and provided limited resolution and limited multi-target capability. Thanks to advancements in the development of semiconductors, modern automotive radar sensors are based on the fast chirp technique. As the name indicates the frequency ramps occur at a higher rate and have a reduced ramp time in the range of 10-100µs.

At this ramp speed objects in the surrounding can be assumed to be quasi-stationary, i.e. relative movement of the objects can be ignored within a ramp period.

In figure 1(a) an exemplary fast transmit signal s_Tx(t) and the received echo s_Rx(t) is depicted. The frequency difference signal of transmit minus receive signal (figure 1(b)) is generated by the down mixer unit. The delta frequency is directly proportional to the distance (range) to the objects that cause a reflection of the electro-magnetic wave. With former slow ramps the delta frequency was ambiguous, because it was comprised of the range component and the Doppler shift component, which was caused by the relative speed of the object. To resolve this ambiguity ramps with different directions and slew rates were used. Fast chirps do not have to cope with this ambiguity due to the quasi-stationary situation. However, a single chirp won't allow the measurement of the relative speed. By analysing multiple successive chirps the Doppler shift can be retrieved from the phase information (figure 1(c)). By doubling or even quadrupling the number of receive channels the azimuth (horizontal) angle of the surrounding objects can be determined, e.g. with a digital beam forming approach.

Radar capture and processing hardware
A high performance analogue-to-digital converter (ADC) and a digital signal processing (DSP) device is required for the analysis of the delta frequency. Before the ADC samples the input and converts the down mixed analogue base band signal into the digital domain, the signal has to be filtered with an anti-aliasing filter and boosted with a low noise amplifier. An equaliser that compensates range-dependent signal losses would be also beneficial. The digitized signal is captured and analysed by the signal processor, which extracts range, velocity and angle information from the data by applying following steps as summarised in figure 2.

Radar signal processing steps
The radar signal processing steps consist of two orthogonal FFTs, digital beam forming and a target detector. The first FFT determines the range of the objects that cause a reflection of the signal. It is called range FFT (figure 2(b)). As mentioned earlier, frequency shift is directly proportional to the propagation delay, respectively the distance to the object. When the range FFTs of all chirps and all receive channels have been calculated, the relative velocity Δv of the objects can be determined by executing the orthogonal Doppler FFT of the range FFT results. This is done by collecting the same range bins of all range FFTs from a single receive channel as input data for the Doppler FFT (see horizontal blue bars in diagrams of figure 2(b) and 2(c)). Due to the fast chirp approach the same range bins are apart from each other by one chirp period. The relative velocity of objects translates into a phase shift from range bin of chirp (n) to range bin of chirp (n+1), which represents the Doppler frequency. This is repeated for all range bins and all receive channels. The result after range FFT and Doppler FFT is a two-dimensional range-Doppler array per receive channel. The array elements are complex values with a real and imaginary part.