Patent Description:
The radars have a loop filter in their local oscillator (LO) subsystems that is optimized for continuous wave phase noise, but may not be optimal for a dynamic phase noise. Dynamic phase noise is the term used to refer to the phase noise that is associated with a chirp signal that is being transmitted over a certain bandwidth. The measurements of frequency linearity and dynamic phase noise are important in FMCW radars because they determine the purity of the waveform of the chirp signal transmitted by the radars.

The current solution involves the use of test equipment to measure the frequency linearity and phase noise. However, most test equipment is expensive and time consuming to use. The more affordable test equipment typically does not have the bandwidth to measure the frequency linearity and/or they do not have the capability to operate on a wider chirp signal bandwidth to measure dynamic phase noise. <CIT> discloses a FMCW radar apparatus. <CIT> relates to a measurement device and method for determining a deviation of a broadband measurement signal from a reference signal. XP055603626 Steffen Heuel:
"<NPL>" relates to an application note concerning automated measurements of <NUM> FMCW radar signals. <NPL> and <CIT> represent further state of the art.

Circuits and methods that validate frequency linearity and dynamic phase noise of frequency modulated continuous wave (FMCW) radars are disclosed herein. Frequency linearity is the linearity of a chirp signal transmitted by a radar. Dynamic phase noise is the term used to refer to the phase noise that is measured when a chirp signal is being transmitted over a certain bandwidth. The circuits and methods are described below based on an example chirp signal having a waveform with the following parameters:.

<FIG> is a graph showing an example of a chirp signal <NUM> as a function of time that has the above described parameters. The chirp signal <NUM> repeats continuously, starts at a start frequency of <NUM>, and ends at a stop frequency of <NUM>. The frequency rise between the start frequency and the stop frequency corresponds to the portion of the chirp signal <NUM> where frequency linearity is measured. More specifically, a slope rate that is constant between the start frequency and the stop frequency along a line <NUM> corresponds to a chirp signal that has high frequency linearity. If the line <NUM> is curved or has discontinuities, the chirp signal has low frequency linearity. Thus, the straightness of the line <NUM> plotted between the start frequency and the stop frequency is related to the frequency linearity.

Additional reference is made to <FIG>, which is a flowchart <NUM> describing a not claimed embodiment of determining chirp signal linearity of a transmitted chirp signal. In step <NUM>, the transmitted chirp signal is captured. In some examples the chirp signal is captured using a generic signal analyzer. For example, commonly available spectrum analyzers may be used to capture the transmitted chirp signals. The spectrum analyzers further function as signal analyzers even with high bandwidth chirp signals because they down-convert the chirp signal using their own internal or locally produced local oscillator (LO). The chirp signal is downconverted to its equivalent base-band intermediate frequency (IF) samples in step <NUM> and is digitized to yield a digitized IF signal, which is referred to as the IF signal in step <NUM>.

The IF signal is quadrature in nature (typically digital I and Q samples). The bandwidth of the spectrum analyzer may or may not accommodate the bandwidth of the transmitted chirp signal, so the analysis bandwidth of the spectrum analyzer may be set to its maximum value in order to analyze the greatest portion of the chirp signal as possible. In some examples, the analysis bandwidth of the spectrum analyzer is approximately <NUM>.

The amplitude of the digitized quadrature samples that are captured by the spectrum analyzer are chosen in a way that it does not clip or saturate the dynamic range of the digitizer and so that the digitized quadrature samples are not buried under the noise floor of the digitizer. The amplitude selected should be optimal and should be in the linear range of operation of the digitizer. In some examples the IF signal is processed to compute the phase of the IF signal. The derivative of the phase of the IF signal with respect to time is calculated to yield the instantaneous frequencies of the chirp signal. In step <NUM>, the instantaneous frequencies of the chirp signal are determined, which are sometimes referred to as the computed frequency samples.

The computed frequency samples can have frequency offsets caused by the frequency difference between the transmitter's LO frequency, which is typically the chirp signal's LO, and the receiving equipment's LO. The difference in the starting frequency of the chirp signal, as captured by the equipment, and the actual start frequency used for the transmission of the chirp signal <NUM> can also cause a frequency offset. The frequency offset may be removed at step <NUM>, by conventional techniques.

Ideal frequency samples of the computed frequency samples are determined from the computed frequency samples. In some examples this is determined by applying a linear fit in the form of a first order polynomial fit, which may include simple terms such as slope and intercept, to the computed frequency samples. In some examples, the polynomial for the polynomial fit is f(t) = at + b, where 'f' is the ideal frequency samples, 't' is the time base; 'a' is slope, and 'b' is the intercept. All the variables are the polynomial coefficients that give the best fit for the computed frequency samples.

Predetermined frequency samples are frequency samples based on a mathematical model of the chirp signal. The difference between the ideal and predetermined frequency samples are determined in step <NUM> to yield the frequency error samples. Frequency linearity is calculated in step <NUM> from the frequency error samples. In some examples, statistical operations such as mean standard deviation on the frequency error samples yield a measure of the frequency linearity.

Having described process for measuring frequency linearity, processes for measuring dynamic phase noise will now be described. The dynamic phase noise is continually analyzed over the bandwidth of the chirp signal and is specified at one particular offset frequency. In some examples, the dynamic phase noise is referred as being offset <NUM>, <NUM>, and <NUM> from the carrier signal. In the case of FMCW radar, the carrier signal is not a single CW frequency, but constantly changes. For example, it may be the ramp signal shown in <FIG> that is specified over a particular bandwidth. Accordingly, the dynamic phase noise is specified as the phase noise of the ramp at an offset frequency, such as <NUM>, <NUM> or <NUM>.

<FIG> is a flowchart <NUM> describing a not claimed embodiment for determining the dynamic phase noise of a transmitted chirp signal. Phase of the chirp signal is determined at step <NUM>. Phase samples of the chirp signal are determined at step <NUM>. Phase samples can either be computed from the frequency samples or from the phase computed from the digitized I and Q samples. In the former case, the frequency error samples are integrated or summed up and divided by 2π to yield phase error samples. In the latter case, a linear polynomial fit (similar to linear fit on the computed frequency samples) is applied to compute phase samples. An example of the polynomial used is as follows: Φ(t) = at + b, where Φ is the phase samples of the chirp signal, 't' is the time base; 'a' is slope, and 'b' is the intercept. These variables are the polynomial coefficients that give best fit for the phase samples, which is sometimes referred to as the measured phase samples.

Predetermined phase samples are calculated in step <NUM>. The predetermined phase samples are based on a mathematical model of an ideal chip signal. The difference between the phase samples and the predetermined phase samples is determined at step <NUM> to yield phase error samples. The power spectral density function of the phase error samples is calculated in step <NUM> to yield the dynamic phase noise of the transmitted chirp signal. The power spectral density of the phase error samples is useful to compute the noise power over a certain frequency offset.

<FIG> is a flowchart <NUM> describing a procedure for computing the metrics of a chirp signal in situations where the chirp signal bandwidth cannot be accommodated by the analyzing equipment. In summary, the entire bandwidth of the chirp signal is segmented into segments, whereby each segment is analyzed. In this example, the chirp signal has the following characteristics:.

As described above, the chirp signal bandwidth is greater than the bandwidth of the device or equipment analyzing the chirp signal. In step <NUM>, the bandwidth of the chirp signal is segmented into a plurality of segments, whereby each segment has a bandwidth that is within the bandwidth of the analyzing equipment. In the example described herein, the bandwidth of the chirp signal is segmented into halves of equal bandwidth which are within the bandwidth of the analyzing equipment. The segmented halves start and stop frequencies are entered into the analyzing equipment to extract the digitized IQ samples for each segment. In step <NUM>, each segment of the chirp signal is analyzed per <FIG> and <FIG> to determine the metrics of each segment. With regard to the metrics described above, the first segment and the second segment are analyzed individually to determine their metrics. In step <NUM>, the root-mean-square of the metrics of each segment are added together to yield the metrics of the entire chirp signal. The metrics include the frequency linearity and the phase noise.

<FIG> is a block diagram of test equipment <NUM> that measures frequency linearity and phase noise of a radar <NUM>. In some examples, the test equipment <NUM> is automated and measures the frequency linearity and phase noise of the radar <NUM> during production of the radar <NUM>. The radar <NUM> has a test mode that outputs a chirp signal. In the example of <FIG>, the chirp signal has a start frequency of <NUM> and stop frequency of <NUM>. The test equipment <NUM> includes an IQ down-converter mixer <NUM> that is coupled to the output of the radar <NUM>. The IQ down-converter mixer <NUM> is coupled to an external local oscillator <NUM> that provides a mixing signal for the IQ down-converter mixer <NUM>. In the example of <FIG>, the frequency of the local oscillator is <NUM>. The mixed signal output by the IQ down-converter mixer <NUM> is output to an intermediate frequency (IF) digitizer <NUM> that extracts the digital IQ signal from the mixed signal generated by the IQ down-converter mixer <NUM>. The combination of the IQ down-converter <NUM> and the IF digitizer <NUM> serves a similar function as a conventional signal analyzer. A processor <NUM> analyzes the digitized IQ signals generated by the IF digitizer <NUM> and determines the frequency linearity and dynamic phase noise of the chirp signal per the methods illustrated in <FIG> and <FIG>.

The IQ down-converter <NUM> receives its input in the form of a chirp signal generated by the radar <NUM>. The chirp signal in the examples provided herein is in the frequency range of <NUM> to <NUM>. The chirp signal is mixed with the external LO generated by the local oscillator <NUM> to yield a zero IF signal. The zero IF signal is passed through the IF digitizer <NUM> to produce digitized quadrature (IQ) samples. The signal processing techniques described herein can be applied on the zero IF signals by the processor <NUM> to evaluate frequency linearity and dynamic phase noise as described above.

<FIG> is a block diagram of an embodiment of more detailed test equipment <NUM> that measures the frequency linearity and dynamic phase noise of a chirp signal. In some examples, the measurements are achieved on a single integrated circuit chip or by a single circuit. A chirp signal having a defined configuration, such as slope, bandwidth, start frequency, and stop frequency, is transmitted from a radar <NUM>. The frequencies of the chirp signal are scaled down by a divider <NUM>. Scaling down the frequencies of the chirp signal accommodates simpler design of the devices described below. In the example of <FIG>, the scaling is accomplished by a divider <NUM> that can divide the chirp signal down by a plurality of possible values, such as <NUM>, <NUM>, <NUM>, <NUM> and <NUM>.

The output of the divider <NUM> is coupled to an input of an auxiliary mixer <NUM> that is used for the down-conversion process. In the example of <FIG>, the auxiliary mixer <NUM> is a complex mixer or a quadrature mixer. An external LO <NUM> generates a signal that has the same frequencies as the radar <NUM>. In the embodiment of <FIG>, the external LO <NUM> generates frequencies in the range of <NUM> to <NUM>. The output of the external LO <NUM> is input to a divider <NUM> that may be the same or substantially similar to the mixer <NUM>. For example, the divider <NUM> may reduce the frequency of the external LO <NUM> the same as the divider <NUM> reduces the signal output by the radar <NUM>. The output of the divider <NUM> is input to an auxiliary LO <NUM> that is used for down-conversion. The output of the auxiliary LO <NUM> is input to the mixer <NUM>.

The output of the mixer <NUM> is coupled to a receiver <NUM> that includes an IF amplifier <NUM> and an ADC <NUM>. In some examples, the IF amplifier <NUM> is a two-staged, bi-quad IF amplifier that amplifies the output of the auxiliary mixer <NUM>. The output of the amplifier <NUM> is digitized by the ADC <NUM>. The output of the ADC <NUM> is processed by a processor <NUM> per the methods described in the flowcharts <NUM> and <NUM> of <FIG> and <FIG>. In some examples, the processing of the processor <NUM> is performed on the same circuit or integrated circuit as the other portions of the radar <NUM>, so the processing can be absorbed as a part of software/firmware written to characterize the radar <NUM>. For example, the processing may be performed as a self test process in the radar.

The LO used for the radar <NUM> cannot be used to feed the auxiliary mixer <NUM> and the external LO <NUM> because the phase noise gets cancelled out during the down-conversion and will affect the dynamic phase noise measurement. This problem is overcome by feeding the external LO <NUM> from an external source. The use of other internal clocks may create noise that will adversely affect the above-described measurements.

Many laboratory testing devices do not have the capability to measure dynamic phase noise of a chirp signal as described above. The chirp signal frequency linearity measurement typically requires high-end test equipment. The methods and devices described herein overcome the problems associated with measuring the frequency linearity and dynamic phase noise together by using a combination of a generic signal analyzer and signal processing algorithms.

<FIG> is flowchart <NUM> describing a method for determining frequency linearity of a chirp signal. The method includes down-converting the chirp signal to quadrature signals at step <NUM>. At step <NUM> the quadrature signals are digitized to yield an IF signal. At step <NUM> the instantaneous frequencies of the IF signal are determined. At step <NUM>, the instantaneous frequencies of the IF signal are compared to predetermined instantaneous frequencies of the chirp signal to determine frequency linearity of the chirp signal.

<FIG> is a flowchart <NUM> describing a method for determining dynamic phase noise of a chirp signal. The method includes down-converting the chirp signal to quadrature signals at step <NUM>. The method also includes digitizing the quadrature signals to yield an IF signal at step <NUM>; measuring the dynamic phase of the IF signal at step <NUM>; and comparing the measured dynamic phase of the IF signal to a predetermined dynamic phase of the chirp signal to yield dynamic phase noise of the chirp signal at step <NUM>.

Claim 1:
A method of analyzing a chirp signal generated by a frequency modulated continuous wave FMCW radar, the method comprising:
down-converting the chirp signal to quadrature signals using an local oscillator (<NUM>) which is not located within the radar;
digitizing the quadrature signals to yield an IF signal (<NUM>);
determining the instantaneous frequencies of the IF signal (<NUM>);
comparing the instantaneous frequencies of the IF signal to predetermined instantaneous frequencies of the chirp signal to determine frequency linearity of the chirp signal (<NUM>);
wherein the local oscillator is fed from an external source and is not the local oscillator of the FMCW radar;
determining dynamic phase noise by calculating the derivative of the phase of the IF signal to yield the instantaneous frequency samples of the IF signal;
determining the difference between the instantaneous frequency samples and predetermined frequency samples to yield frequency error samples;
integrating the frequency error samples to yield the phase error samples; and
calculating the power spectral density function of the phase error samples to yield the dynamic phase noise.