Cesar Iovescu / Radar Application Manager

Sandeep Rao / Radar Systems Designer

Texas Instruments (www.ti.com/mmwave)

Velocity measurement using multiple objects in the same range

2 The chirp rate measurement method does not work with multiple objects moving at different speeds. It is useless if the objects are at the same distance from the radar when measuring. Since these objects are at the same distance, they produce a reflected chirp with the same IF frequency. Therefore, the range FFT produces a single peak, which represents the combined signal of all objects at the same range. Simple phase comparison techniques are useless.

In this case, to measure velocity, the radar system must transmit three or more chirps. It transmits a set of N chirps with the same velocity. This set of chirps is called a chirp frame. Figure 7 shows frequency as a function of time in chirp frames.

Figure 7 Chirp Frame

An example of this processing technique is illustrated below with two objects at the same distance from the radar but with different velocities, v ₁ and v ₂ .

The range FFT processes the set of reflected chirps to produce a set of N peaks with the same location but different phases, integrating the phase contributions of these two objects (the individual phase contributions of each of these objects are represented in Figure 8 as red and blue phasors).

Figure 8 The range FFT of the reflected chirp frame produces N phasers.

A second FFT, called the Doppler FFT, is performed to identify the two objects in the N-phase as shown.

Figure 9 Doppler FFT distinguishes between two objects.

ω ₁ and ω ₂ correspond to the phase difference between successive chirps of the object (Equation 13).

(13)

Speed Resolution

According to the theory of discrete Fourier transforms, if Δ ω = ω ₂ ? ω ₁ >2π/N radians/sample, we can determine two discrete frequencies ω ₁ and ω ₂ .

Since Δ ω is also defined in the equation,

<br /> (Equation 10), the velocity resolution (V _res ) is exactly

If the frame period is T _f = NT _c (Equation 14):

(14)

The velocity resolution of a radar is inversely proportional to the frame time (T _f ).

Angle detection

Angle estimation

FMCW radar systems can estimate the angle of reflected signals using the horizontal plane, as shown in Figure 10. This angle is also called the angle of arrival (AoA).

Figure 10 Arrival angle

Angle estimation is based on the observation that small changes in the object distance result in a phase shift in the range FFT or Doppler FFT peak. This result is used to perform angle estimation using two or more RX antennas as shown in Figure 11. The differential distance between each antenna and the object results in a phase shift in the FFT peak. AoA can be estimated using phase shift.

Figure 11 Two antennas are required to estimate AoA.

In this configuration, the phase change is obtained as in equation 15.

(15)

Assuming a plane wave front, the basic topography shows Δd = lsin(θ), where l is the distance between the antennas. Therefore, the angle of arrival (θ) can be calculated from the measured Δ Φ in equation 16.

(16)

Note that Δ Φ depends on sin(θ). This is called a nonlinear dependence. sin(θ) is approximated using a linear function only when θ is small:

sin(θ) ~ θ.

Therefore, the estimation accuracy varies with AoA, as shown in Figure 12, and is more accurate when the θ value is smaller.

Figure 12 The smaller the value, the higher the AoA estimation accuracy.

Maximum viewing angle

The maximum field of view of a radar is defined as the maximum AoA that the radar can estimate. See Figure 13.

Figure 13 Maximum field of view

To measure an angle unambiguously, | Δω | must be < 180°. Using equation 16, this gives

It corresponds to .

Equation 17 shows that the maximum field of view that two antennas spaced l apart can provide is:

(17)

The spacing between the two antennas l = λ/2 results in a maximum field of view of ± 90°.

Texas Instruments mmWave Sensor Solutions

As you know, FMCW sensors use a combination of RF, analog, and digital electronics to determine the range, velocity, and angle of nearby objects.

Figure 14 is a block diagram of several components.

TI has revolutionized the sensing range of FMCW by integrating DSP, MCU, TX RF, RX RF, analog and digital components into a single RFCMOS chip.

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Figure 14 RF, analog, and digital components of an FMCW sensor

TI’s RFCMOS mmWave sensors differentiate themselves from existing SiGe-based solutions by implementing flexibility and programmability into their mmWave RF front-end and MCU/HWA/DSP processing back-end. While SiGe-based solutions can store only a limited number of chirps and require real-time intervention to update chirps and chirp profiles during the actual frame, TI’s mmWave sensor solution can store 512 chirps using four profiles before the start of the frame. This capability allows TI’s mmWave sensors to be easily configured in multiple configurations to maximize the extraction of useful data from the field. Individual chirps and processing back-ends can be tailored “on the fly” to meet real-time application requirements such as extended range, increased speed, improved resolution or specific processing algorithms.

TI’s mmWave sensor portfolio ranges from the high-performance front-end radar AWR1243 sensor to the single-chip radar AWR1443 sensor and AWR1642 sensor. Designers can meet ADAS (Advanced Driver Assistance Systems) and autonomous driving safety regulations, including ISO 26262 implementing Automotive Safety Integrity Level (ASIL) B, with products from the AWR mmWave portfolio.

The industrial TI mmWave sensor portfolio consists of two single-chip devices. The IWR1443 mmWave sensor includes a hardware accelerator for radar signal processing, while the IWR1642 mmWave sensor uses a DSP to perform the necessary processing. The DSP provides greater flexibility and enables other higher-level algorithms, such as tracking and classification, to be integrated into the software. These single-chip devices provide easy access to highly accurate data about an object’s range, velocity, and angle, enabling advanced sensing in promising applications that demand performance and efficiency, such as Industry 4.0 for smart infrastructure, factory and building automation products, and autonomous drones.

Texas Instruments provides a complete development environment for engineers building mmWave sensor products for industrial and automotive applications, including:

- Hardware evaluation modules for AWR1x and IWR1x mmWave sensors

- mmWave SDK (Software Development Kit) including RTOS, drivers, signal processing libraries, mmWave API, mmWaveLink, and security features (available separately).

- mmWave Studio, an offline tool for algorithm development and analysis, including data capture, visualizer, and system predictor.

To learn more about mmWave products, tools and software, visit www.ti.com/mmwave.