An efficient and safe approach of radar signal capturing and processing : Page 3 of 7

June 04, 2015 //By Peter Aberl, Texas Instruments
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 realized in an efficient and safe way. Additional automotive radar aspects like low power, small form factor and scalability are also touched.
Radar Capture and Processing Hardware

A high performance analog-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 analog baseband signal into the digital domain, the signal has to be filtered with an anti-aliasing filter and boosted with a low noise amplifier. An equalizer that compensates range-dependent signal losses would be also beneficial. The digitized signal is captured and analyzed by the signal processor, which extracts range, velocity and angle information from the data by applying following steps as summarized 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 (see 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.

Figure 2: Radar Signal Processing Steps (4 Rx channels). Click image for full resolution. (a) Signal Capture; (b) Range FFT; (c) Doppler FFT; (d) Digital Beam Forming, (e) Target Detector; (f) Object Tracker

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