DETECTION OF INTERFERENCE-CAUSED DISTURBANCES IN FMCW RADAR SYSTEMS

Abstract

A method that exhibits the following: Providing a digital radar signal (y[n]) by means of a radar receiver, wherein the digital radar signal (y[n]) comprises a plurality of segments; Calculating an envelope signal (x env [n]), which represents the envelope of a segment of the digital radar signal (y[n]); Determining a time (n1) of the start of an interference signal contained in the segment of the digital radar signal (y[n]), wherein the determination comprises the following: Determining a statistical parameter (µ[n], σ[n]) of the envelope signal (x env [n]) for a plurality of successive time indices of the envelope signal (x env [n]); Calculate at least one threshold value (µ[n] + λ · σ[n], ​​λ · σ) based on at least one statistical parameter (µ[n], σ[n]), Detecting a time index (n1) representing the time of onset of an interference signal, based on a comparison of the envelope signal (x env [n]) with at least one threshold value (µ[n] + λ · σ[n], ​​λ · σ).

Description

TECHNICAL AREA

[0001] The present description concerns the field of radar sensors, in particular signal processing methods used in radar sensors which enable the detection of interfering interferences. BACKGROUND

[0002] Radar sensors are used in a wide variety of applications for object detection, typically involving the measurement of distances and speeds of the detected objects. There is a growing demand for radar sensors, particularly in the automotive sector, where they can be used in advanced driver assistance systems (ADAS), such as adaptive cruise control (ACC, or radar cruise control). These systems can automatically adjust a vehicle's speed to maintain a safe distance from other vehicles ahead (as well as other objects and pedestrians). Other automotive applications include blind spot detection, lane change assist, and similar systems.In the field of autonomous driving, radar sensors and multi-sensor systems will play an important role in controlling autonomous vehicles.

[0003] Various types of radar systems and radar measurement methods are known and published. Radar systems for vehicles are described, for example, in publications US 20170343646A1, EP 3173812A1, and US 20070018886A1. The latter two address, among other things, the problem of interference in frequency-modulated continuous-wave radar systems. Publication US 7375676B1 concerns a pulse radar, such as that used for airspace surveillance, and also deals with the topic of interference. Publication EP 3037840A1 describes a passive radar for airspace surveillance and specifically addresses the problem of interference caused by rotating wind turbines. Publication DE 2155074 describes a circuit arrangement for interference suppression in a radar receiver. The publication WO 2018 / 220 784 A1 deals with signal processing and interference detection in ultrasonic sensors.Publication AU 2015101677 A4 describes a method for suppressing high-frequency interference. Publication US 2016 / 0308627 A1 deals with the detection of interference signals.

[0004] As automobiles are increasingly equipped with radar sensors, the probability of interference rises. This means that a radar signal emitted by a first radar sensor (installed in a first vehicle) can be interfering with the receiving antenna of a second radar sensor (installed in a second vehicle). In the second radar sensor, the first radar signal can interfere with an echo of the second radar signal, thereby impairing the operation of the second radar sensor. SUMMARY

[0005] The following describes a method for a radar system that can be used to detect interference in the received radar signal. According to one embodiment, the method comprises providing a digital radar signal by means of a radar receiver, wherein the digital radar signal comprises a plurality of segments; calculating an envelope signal representing the envelope of a segment of the digital radar signal; and determining the onset time of an interference signal contained in the considered segment of the digital radar signal using at least one statistical parameter of the envelope signal.

[0006] Furthermore, a radar system is described. According to one embodiment, the system comprises a radar receiver configured to provide a digital radar signal comprising a plurality of segments. The system further comprises a processing unit configured to calculate an envelope signal representing the envelope of a segment of the digital radar signal. The processing unit is further configured to determine the onset time of an interference signal contained in the segment of the digital radar signal using at least one statistical parameter of the envelope signal. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] The following illustrations explain exemplary embodiments in more detail. The illustrations are not necessarily to scale, and the exemplary embodiments are not limited to the aspects shown. Rather, the emphasis is placed on illustrating the principles underlying the exemplary embodiments. The illustrations show: Fig. Figure 1 is a sketch illustrating the operating principle of an FMCW radar system for distance and / or speed measurement. Fig. 2 includes two timing diagrams to illustrate the frequency modulation (FM) of the RF signal generated by the FMCW system. Fig. Figure 3 is a block diagram illustrating the basic structure of an FMCW radar system. Fig. Figure 4 is a sketch illustrating an example of how interference signals can be introduced into the receiving antenna of a radar sensor. Fig. Figure 5 is a circuit diagram illustrating a simplified example of a radar transceiver and another radar transceiver causing interference. Fig. Figure 6 shows in a time diagram (frequency versus time) an example of an emitted radar signal with several sequences of chips, where each sequence has a specific number of chirps that are used for a measurement. Fig. Figure 7 shows a time diagram of a transmitted signal from a radar sensor and a transmitting signal (interference signal) from another radar sensor (interferer) causing the interference, with the signal profiles (frequency over time) of these signals partially overlapping. Fig. Figure 8 shows a time diagram of an exemplary signal waveform of a radar signal (after mixing into the baseband), which includes a radar echo from a radar target and an interference signal. Fig. Figures 9a-c illustrate in an exemplary time diagram the result of determining the envelope of a baseband signal with an interference-induced disturbance using different calculation methods. Fig. Figure 10 illustrates the identification of a signal segment that essentially contains an interference-induced disturbance, based on the previously determined envelope of the baseband signal. Fig. 11 and Fig. 12 illustrate an alternative approach to Fig. 10. Fig. Figure 13 is a flowchart illustrating an embodiment of the method described herein. DETAILED DESCRIPTION

[0008] Fig. Figure 1 illustrates, in a schematic diagram, the application of a frequency-modulated continuous-wave radar system (FMCW) as a sensor for measuring the distances and velocities of objects commonly referred to as radar targets. In this example, the radar device 1 has separate transmit (TX) and receive (RX) antennas 5 and 6, respectively, which is referred to as a bistatic or pseudo-monostatic radar configuration. It should be noted, however, that a single antenna can also be used, serving simultaneously as the transmit and receive antenna (monostatic radar configuration). The transmit antenna 5 radiates a continuous RF signal s RF (t) ab, which is frequency-modulated, for example, with a type of sawtooth signal (periodic, linear frequency ramp). The radiated signal s RF(t) is backscattered at radar target T and the backscattered / reflected signal y RF (t) (echo signal) is received by the receiving antenna 6. Fig. Figure 1 shows a simplified example; in practice, radar sensors are systems with multiple transmit (TX) and receive (RX) channels to also determine the direction of arrival (DoA) of the backscattered / reflected signal y. RF (t) determine and thus be able to locate the radar target T more accurately.

[0009] Fig. Figure 2 illustrates, by way of example, the aforementioned frequency modulation of the signal s RF (t). As in Fig. The radiated RF signal is shown in diagram 2 (upper diagram). RF (t) composed of a set of “chirps”, i.e. the signal s RF (t) comprises a sequence of sinusoidal waveforms with increasing frequency (up chirp) or decreasing frequency (down chirp). In the present example, the instantaneous frequency f increases.LO (t) of a chirp starting at a starting frequency f START within a time period T CHIRP linearly to a stopping frequency f STOP an (see lower diagram in Fig. 2) Such chirps are also called linear frequency ramps. In Fig. Figure 2 shows three identical linear frequency ramps. It should be noted, however, that the parameters f START , f STOP , T CHIRP The pause between individual frequency ramps can also vary. Furthermore, the frequency change does not necessarily have to be linear (linear chirp). Depending on the implementation, for example, transmitted signals with exponential or hyperbolic frequency variation (exponential or hyperbolic chirps) can also be used. For a measurement, a sequence of frequency ramps is always transmitted, and the resulting echo signal is evaluated in the baseband to detect one or more radar targets.

[0010] Fig. Figure 3 is a block diagram illustrating a possible structure of a radar device 1 (radar sensor). According to this diagram, at least one transmitting antenna 5 (TX antenna) and at least one receiving antenna 6 (RX antenna) are connected to an RF front end 10 integrated into a chip. This front end can include all the circuit components required for RF signal processing. These circuit components include, for example, a local oscillator (LO), RF power amplifiers, low-noise amplifiers (LNAs), directional couplers (e.g., rat-race couplers, circulators, etc.), and mixers for downconverting the RF signals to the baseband or an intermediate frequency (IF) band. The RF front end 10 can be integrated—possibly together with other circuit components—into a single chip, which is typically referred to as a monolithic microwave integrated circuit (MMIC).The baseband is sometimes also referred to as the IF band (depending on the implementation). In the following, no further distinction will be made between baseband and IF band; only the term baseband will be used. Baseband signals are those signals on which the detection of radar targets is based.

[0011] The example shown depicts a bistatic (or pseudo-monostatic) radar system with separate RX and TX antennas. In a monostatic radar system, the same antenna would be used for both transmitting and receiving the electromagnetic (radar) signals. In this case, a directional coupler (e.g., a circulator) can be used to separate the transmitted RF signals from the received RF signals (radar echo signals). As mentioned, radar systems in practice usually have multiple transmit and receive channels with multiple transmit and receive antennas (antenna arrays), which, among other things, enables the measurement of the direction (DoA) from which the radar echoes are received. In such MIMO systems (MIMO = Multiple-Input Multiple-Output), the individual TX and RX channels are typically identical or similar in design and can be distributed across multiple integrated circuits (MMICs).

[0012] In the case of an FMCW radar system, the RF signals radiated via the TX antenna 5 can be in the range of approximately 20 GHz to 100 GHz (e.g., in the range of approximately 76–81 GHz in some applications). As mentioned, the RF signal received by the RX antenna 6 includes the radar echoes (chirp echo signals), i.e., those signal components that are backscattered from one or more radar targets. The received RF signal y RF (t) is downmixed to the baseband and further processed in the baseband using analog signal processing (see Fig. 3. Analog baseband signal processing chain 20). The aforementioned analog signal processing essentially comprises filtering and, if necessary, amplification of the baseband signal. The baseband signal is then digitized (see Fig. 3, Analog-to-Digital Converter 30) and further processed in the digital domain. The digital signal processing chain can be implemented, at least partially, as software running on a processor, for example a microcontroller or a digital signal processor (see Fig. 3, processing unit 40). The overall system is typically controlled by a system controller 50, which can also be implemented, at least partially, as software running on a processor such as a microcontroller. The RF front end 10, the analog baseband signal processing chain 20, the analog-to-digital converter 30, and optionally the processing unit 40 (or parts thereof) can be integrated together in a single MMIC (i.e., an RF semiconductor chip). Alternatively, the individual components can be distributed across multiple MMICs. The processing unit 40, or parts thereof, can be contained within the system controller 50.

[0013] In the examples described here, "processing unit" refers to any structure or group of functional entities designed to perform the necessary functions (calculations). A processing unit can comprise one or more processors designed to execute software / firmware instructions. However, the processing unit can also (additionally or alternatively) include hard-wired hardware units specifically designed to perform certain calculations quickly (e.g., a CFAR algorithm or a fast Fourier transform, etc.). The processing unit is not necessarily integrated into a single chip but can also be distributed across multiple chips.

[0014] The System Controller 50 can be integrated into a separate chip and configured to communicate with the MMIC 100 (or multiple MMICs) via one or more communication links. Suitable communication links include, for example, a Serial Peripheral Interface (SPI) bus or Low-Voltage Differential Signaling (LVDS) according to the TIA / EIA-644 standard. Parts of the aforementioned processing unit can be integrated into the System Controller 50. The processing unit, or parts thereof, can also be integrated into the radar MMIC.

[0015] In addition to echo signals, the received radar signal can also be... RF (t) (cf. Fig. 1) also contain interference signals caused by other RF sources, in particular other radar sensors. These interference signals interfere with the echo signals from real radar targets. Fig. Figure 4 illustrates a simple example of how an interfering device can disrupt the received echo signals. Fig. Figure 4 depicts a road with three lanes and four vehicles V1, V2, V3, and V4. At least vehicles V1 and V4 are equipped with radar sensors. The radar sensor of vehicle V1 emits an RF radar signal s RF (t) from ( Fig. 4, arrows with solid lines) and the received RF radar signal y RF (t) includes the radar echoes from the preceding vehicles V2 and V3 as well as from the oncoming vehicle V4 ( Fig. 4, arrows with dashed lines). Furthermore, the RF radar signal y received by the radar sensor of vehicle V1 includes RF (t) a radar signal (interference signal) generated by the radar sensor of the oncoming vehicle V4 ( Fig. 4, arrow with dashed line). For the radar sensor of vehicle V1, the radar sensor of vehicle V4 is a jammer (interferer).

[0016] The signal y received by the radar sensor of vehicle V1 RF(t) can be written as follows in the case of U radar targets and V jammers: yRF(t)=yRF,T(t)+yRF,I(t), where yRF,T(t)=∑i=0U−1AT,i⋅sRF(t−ΔtT,i) and yRF,I(t)=∑k=0V−1AI,k⋅sRF,k'(t−ΔtI,k).

[0017] In the equations (1) to (3) above, the signal components y correspond to RF,T (t) and y RF,I (t) of the received signal y RF (t) the radar echoes from real radar targets T i or the interfering signals. In practice, multiple radar echoes and multiple interfering signals can be present. Equation (2) therefore represents the sum of the radar echoes from U to different radar targets T. i are caused, whereby A T,i the attenuation of the emitted radar signal s RF (t) and Δt T,i the round trip delay time (RTDT) for a specific radar target T idenote. Similarly, equation (3) represents the sum of the interference signals caused by V interference sources. Here, A denotes I,k the attenuation of the interference signal radiated by a jammer sRF,k'(t) and Δt I,k the associated signal propagation time (for each interfering signal k = 0, 1, ... , V - 1). It should be noted that the radar signal emitted by vehicle V1 s RF (t) and the interference signal emitted by vehicle V4 s RF,0 '(t) (index k = 0 for vehicle V4) will typically exhibit different chirp sequences with varying chirp parameters (start / stop frequency, chirp duration, repetition rate, etc.). Furthermore, the amplitude of the received interference signal component y can RF,I (t) be significantly higher than the amplitude of the echo signal component y RF,T (t). As a rule, the interference signal components will have significantly higher amplitudes than the signal components of the radar echoes.

[0018] Fig. Figure 5 illustrates an exemplary implementation of a radar transceiver 1 according to the example from Fig. 3 in more detail. In the present example, the RF frontend 10 of the “own” radar transceiver 1 as well as the RF frontend 10' of another (interfering) radar sensor 1' are shown. It should be noted that Fig. Figure 5 shows a simplified circuit diagram to illustrate the basic structure of the RF front end 10 with one transmit channel (TX channel TX1) and one receive channel (RX channel RX1). As mentioned, actual implementations, which can vary significantly depending on the specific application, are usually more complex and feature multiple TX and / or RX channels, which may also be integrated into different MMICs.

[0019] The RF frontend 10 includes a local oscillator 101 (LO) which generates an RF oscillator signal s LO (t) is generated. The RF oscillator signal s LO(t) is in operation - as above with reference to Fig. 2 described - frequency-modulated and also referred to as the LO signal. In radar applications, the LO signal is usually in the SHF (Super High Frequency, centimeter wave) or EHF (Extremely High Frequency, millimeter wave) band, e.g., in the interval from 76 GHz to 81 GHz in some automotive applications. Some radar systems operate in the 24 GHz ISM (Industrial, Scientific and Medical) band. The LO signal s LO (t) is processed in both the transmit signal path TX1 (in the TX channel) and the receive signal path RX1 (in the RX channel).

[0020] The transmission signal s RF (t) (cf. Fig. 2), which is radiated from the TX antenna 5, is amplified by amplifying the LO signal s LO (t), for example by means of the RF power amplifier 102, is generated and is therefore merely an amplified and possibly phase-shifted version of the LO signal s LO(t). The output of amplifier 102 can be coupled to the TX antenna 5 (in the case of a bisstatic / pseudo-monostat radar configuration). The received signal y RF (t), which is received by the RX antenna 6, is fed to the receiver circuit in the RX channel and thus directly or indirectly to the RF port of the mixer 104. In the present example, the RF receive signal y RF (t) (antenna signal) is pre-amplified by amplifier 103 (gain g). Mixer 104 thus receives the amplified RF receive signal g · y. RF (t). Amplifier 103 can, for example, be an LNA (low noise amplifier). The LO signal s is connected to the reference port of mixer 104. LO (t) supplied, so that the mixer 104 receives the (pre-amplified) RF receive signal y RF (t) downmixes to the baseband. The downmixed baseband signal (mixer output signal) is then used with y BB (t) denotes this baseband signal y. BB(t) is first processed further in an analog manner, whereby the analog baseband signal processing chain 20 essentially performs amplification and filtering (e.g., bandpass or lowpass), for example to suppress unwanted sidebands and image frequencies. The resulting analog output signal, which is fed to an analog-to-digital converter (see Fig. The signal supplied to the ADC 30 (3) is denoted by y(t). Methods for the digital processing of the digitized output signal (digital radar signal y[n]) are known per se (for example, range Doppler analysis) and are therefore not discussed in detail here.

[0021] In the present example, mixer 104 mixes the pre-amplified RF receive signal g · y RF(t) (i.e., the amplified antenna signal) down to the baseband. The mixing can be done in one stage (i.e., directly from the RF band to the baseband) or via one or more intermediate stages (i.e., from the RF band to an intermediate frequency band and then to the baseband). In this case, the receive mixer 104 effectively comprises several individual mixer stages connected in series. Furthermore, the mixer stage can include an IQ mixer, which generates two baseband signals (in-phase and quadrature signal) that can be interpreted as the real and imaginary parts of a complex baseband signal.

[0022] Fig. Figure 5 further shows a part (the TX channel of the RF front end 10') of another radar sensor 1', which acts as a jammer for radar sensor 1. The RF front end 10' of radar sensor 1' includes another local oscillator 101', which generates a LO signal s LO'(t) is generated, which is amplified by amplifier 102'. The amplified LO signal is used as an RF radar signal s RF,0 '(t) is emitted via the antenna 5' of the radar sensor 1' (see equation (3)). This RF radar signal s RF,0 '(t) contributes to the interference signal component y received by the antenna 6 of the other radar sensor 1 RF,I (t) at and can cause the aforementioned interferences.

[0023] Fig. Figure 6 schematically illustrates an example of an FM scheme as is commonly used in FMCW radar sensors for frequency modulation (FM) of the LO signal. LO (T) is used. In the example shown, a sequence of chirps is generated for each measurement. Fig. The first sequence in example 6 contains only 16 chirps. In practice, however, a sequence can contain significantly more chirps, for example, 128 or 256 chirps. A number corresponding to a power of two allows the use of efficient FFT (Fast Fourier Transform) algorithms in subsequent digital signal processing (e.g., range / Doppler analysis). There can be a pause between the individual sequences.

[0024] Fig. 7 and Fig. Figure 8 illustrates, using an example, how a jammer can interfere with the radar echoes contained in the RF signal y received by radar sensor 1. RF (t) are contained, which can be disruptive. Fig. Figure 7 shows in a diagram (frequency over time) a chirp emitted by radar sensor 1 with a chirp duration of 60µs ( Fig. 7, solid line). The starting frequency of the transmitted signal s RF(t) is approximately 76250 MHz and the stopping frequency is approximately 76600 MHz. A jamming signal y generated by another radar sensor RF,I (t) includes an up chirp with a start frequency of approximately 76100 MHz, a stop frequency of approximately 76580 MHz and a chirp duration of 30 µs, and a subsequent down chirp that starts at the stop frequency of the preceding chirp and ends at the start frequency of the preceding chirp and has a chirp duration of 10 µs ( Fig. 7, dashed line). The bandwidth B of the radar sensor's baseband signal is essentially determined by the baseband signal processing chain 20 and is shown in Fig. 7 indicated by the dashed lines. Fig. Figure 8 shows an exemplary signal waveform of the (preprocessed) baseband signal y(t) of radar sensor 1. It can be seen that, due to interference, the signal components exhibit a significant amplitude in those time intervals where the frequencies of the transmitted signal and the interfering signal have a frequency difference that is less than or equal to the bandwidth B of the radar sensor (see Figure 8). Fig. 7 and Fig. 8) In the present example, the interference occurs three times during the chirp duration of 60 µs, namely at approximately 7 µs, 28 µs, and 42 µs. As mentioned, the power of the interfering signal is typically higher than the power of the radar echoes from real targets. Furthermore, the interfering signals and the transmitted signal of the radar sensor 1 under consideration are uncorrelated (apart from exceptions not considered here), which is why the interference can be regarded as noise (in the sense of a broadband disturbance) and thus increases the noise floor.

[0025] Several concepts have been proposed for suppressing interference-induced disturbances. Some concepts assume that individual signal segments of the digital radar signal y[n] affected by interference (corresponding to a chirp in the RF transmit signal s) are isolated. RF (t) can be assigned) are identified as "impaired". The impaired signal segments are usually discarded and not considered in further signal processing. Other concepts require that individual samples of the digital radar signal y[n] affected by an interference are identified as "impaired". In this case, the affected signal segment does not have to be discarded as a whole, but the affected samples can be selectively corrected (e.g., by interpolation / approximation) to suppress the interference.

[0026] The concepts described below aim to reliably and robustly (i.e., independently of the specific situation) identify samples or groups of consecutive samples that are potentially affected by interference and therefore cannot (and should not) be considered for object detection. As in Fig. As shown in Figure 8, interference manifests itself as relatively short pulses, which are referred to below as interference bursts. Within such a burst, the frequency initially decreases and then increases again. In order to "cut out" an interference burst from the digital radar signal as precisely as possible, it is necessary to detect the onset and the end of the respective burst as accurately and reliably as possible. The concepts described here use the envelope of the digital radar signal y[n] for this purpose, which is processed, for example, segment by segment.

[0027] Fig. Figures 9a-c are timing diagrams that exemplify a 1024-sample segment of a digital radar signal y[n] containing an interference signal (in this example, an interference burst). As a first step, an envelope of the signal segment under consideration is determined, which can be done in different ways. The results of the different approaches to calculating the envelope are shown in diagrams (a) to (c). Fig. 9 shown. One possibility is to use a signal x representing the envelope. env [n] to calculate is the calculation of the absolute value of the analytical representation of the digital radar signal y[n]. Diagram (a) of the Fig. Figure 9 shows an exemplary segment of the digital radar signal and the absolute value of the corresponding analytical signal. The absolute value represents the envelope x. env[n]. An analytical signal is a complex-valued signal whose imaginary part is the Hilbert transform of the real part. Provided in the RF front end (see Fig. Since an IQ mixer is used to generate the baseband signal in the RF frontend (10) of the radar sensor (5), the digital radar signal y[n] is already an analytical signal (real and imaginary parts corresponding to the in-phase and quadrature components). If a real baseband signal is generated, the corresponding imaginary part can be generated using a Hilbert transformer. In this case, the signal x representing the envelope is... env [n]: xenv[n]=y[n]2+H{y[n]}2, where y[n] denotes the real baseband signal and ℌ{y[n]} denotes the corresponding Hilbert-transformed signal (i.e., the imaginary part belonging to y[n]).

[0028] A Hilbert transformer can be implemented, for example, as an all-pass filter exhibiting a constant phase response of π / 2. This all-pass filter can be implemented as a finite impulse response filter. The generation of an analytical signal using a Hilbert transformer and the subsequent calculation of the envelope by determining the absolute value of the complex-valued analytical signal is well-known and therefore will not be discussed further here.

[0029] Another way to calculate a signal representing the envelope is to approximate an RMS (root mean square) value using a moving average of the instantaneous signaling. The result of this approach is shown in diagram (b) of the Fig. Figure 9 shows the signal representing the envelope. In this case, the signal x is the envelope signal. env [n]: xenv[n]=1L∑i=n−L / 2+1n+L / 2y[i]2, where L represents the window length used to calculate the moving average. In one example, L = 30 is used. The square of the RMS value would also be suitable as a signal representing the envelope.

[0030] Another way to calculate a signal representing the envelope is to use a cell-averaging CFAR algorithm, which can also be used for other purposes in radar systems. The parameters for the CFAR algorithm can be chosen so that the envelope is provided with the desired accuracy. The result is similar to the approximation according to Equation 5, but depending on the specific implementation in the computing units (see Fig. 4, paragraph 40) of the radar sensors already provides hardware acceleration for the execution of the CFAR algorithm, which allows for very fast calculation of the signal x representing the envelope. env[n] allowed. The result of this approach is shown in diagram (c) of the Fig. 9 shown.

[0031] It should be noted that in the examples described here, a segment of the digital radar signal y[n] is always considered. The determined envelope signal x env [n] is therefore a finite signal belonging to the considered segment of the digital radar signal y[n]. In the examples described here, the length (number of samples) of the segment of the digital radar signal y[n], and thus the length of the associated envelope signal x, is env [n] N Samples, where N = 1024. Depending on the calculation method used, the envelope signal x env [n] may also include fewer than N samples. The segment of the digital radar signal y[n] under consideration can usually be assigned to a specific chirp of the emitted chirp sequence (i.e., of the emitted RF signal s). RF(t)) are assigned. For a chirp sequence with M chirps (see Fig. 6, exemplary sequence with 16 chirps) thus M corresponding segments of the digital radar signal y[n] can be determined (and thus also M corresponding envelope signals x) env [n]). The concepts described here can be applied to each of the envelope signals x env [n] are applied separately. When reference is made below to the digital radar signal y[n], this usually refers to the currently considered segment of the digital radar signal y[n].

[0032] One can see in Fig. 9, that all three of the above-mentioned possibilities for calculating the signal x env [n] (hereinafter referred to as the envelope signal) lead to a similar result. After the envelope signal x envAfter calculating the envelope of the considered segment of the digital radar signal y[n], the beginning and end of the interference burst are detected. The implementations described here use a statistical approach to make the detection more robust. A first example is given below using the diagrams from Fig. 10 explained.

[0033] According to Fig. 10, Diagram (a), are used for the signal x env [n] continuously calculates statistical parameters, namely a running mean µ[n] and a running standard deviation σ[n], ​​i.e. μ[n]=1n−n0+1∑i=n0nxenv[i], and σ[n]=1n−n0+1∑i=n0n(xenv[i]−μ[i])2, where n=n0,n0+1, n0+2,.. where in the present example from Fig. 10 holds for the starting index n0 = 0. In each time step, i.e., for each time index n, the inequality xenv[n]>μ[n]+λ⋅σ[n] evaluated, where λ is a constant, predetermined parameter. In other words, the envelope signal x env [n] is compared to a threshold µ[n] + λ · σ[n] which depends on statistical parameters that define the envelope signal x env [n] itself characterize and depend on it. Inequality 8 is evaluated for increasing time indices n = 0, 1, 2, ... If inequality 8 is satisfied for a certain number of consecutive samples (e.g., for n = n1, n1 + 1, n1 + 2), then the time index of the first of these consecutive samples (i.e., n = n1) is defined as the onset time of an interference burst. The corresponding sample x env [n1] is shown in diagram (a) of the Fig. 10 is marked with a circle. It should be noted here that the envelope signal x env[n] is processed segment by segment, where in the present example the segment length N is equal to 1024 (i.e., n = 0, 1, ..., 1023). Each processed segment can be assigned to a specific chirp in the transmitted RF signal s RF (t) are assigned, which is not necessarily the case with the concepts described here.

[0034] Fig. Figure 10, Diagram (b), illustrates the detection of the end time of the interference burst. This can be done in the same way as the start of the interference burst, for example, by applying the method described above to the “reversed” signal. xenv'[n]=xenv[N−1−n] is applied (for signal segments with N samples, i.e., n = 0, ..., N - 1). Inverting the signal x env [n] is not absolutely necessary; alternatively, equations (6) and (7) can be adjusted accordingly.

[0035] Another approach to detecting the onset of an interference burst using the previously calculated envelope signal x env [n] will in the following begin the Fig. 11 and Fig. 12 explained. According to the example from Fig. 11 are used for the signal x env [n] continuously calculates statistical parameters, namely a “local” standard deviation σ[n] (i.e., the standard deviation of the samples in a moving window) and a scaled mean of the local standard deviation σ[n]. The signal x env [n] considered segment-wise as in the previous example, i.e. the segments for n = 0 + s i , ..., N - 1 + s i , where N is the length of the segment (e.g. N = 1024) and s i The starting index of the i-th segment (e.g., s1 = 0, s2 = 1024, etc.) is denoted. The following uses the signal segment x as an illustrative example. env[n], for n = 0, ..., 1023, considered. The individual segments of the envelope signal x env [n] can each correspond to a specific chirp in the emitted RF signal s RF (t) may be assigned, but this is not necessarily the case.

[0036] The aforementioned standard deviation σ[n] of a sliding window with K samples can be calculated as follows: σ[n]=1K∑i=n−K / 2+1n+K / 2(xenv[i]−μ[i])2, with μ[n]=1K∑i=n−K / 2+1n+K / 2xenv[i], for=0,1,...,N−1.

[0037] The window length in this example is an even number. To calculate the standard deviation σ[n] for time indices n < K / 2 - 1 and n ≥ N - K / 2, the considered segment of the signal x can be env [i] can be padded with zeros at the edges (zero-padding). Alternatively, the domain of σ[n] can be reduced accordingly. The lower waveform in Fig. 11 represents the "local standard deviation σ[n] according to equation 9.

[0038] This standard deviation σ[n], ​​calculated according to Equation 9, is continuously compared with a threshold value λ · σ, which is also a statistical parameter, namely, in this example, a scaled mean of the standard deviation of the segment under consideration. The threshold value can therefore be calculated as follows: λ⋅σ¯=λ⋅1N∑n=0N−1σ[n], where λ is a predetermined constant scaling factor. At the beginning and end of an interference burst, the local standard deviation σ[n] calculated for the moving window will increase. For each time index n, it is checked whether the corresponding value σ[n] lies above the threshold λ · σ, i.e., the following inequality is evaluated: σ[n]>λ⋅σ¯.

[0039] Inequality 12 is evaluated for increasing time indices n = 0, 1, 2, ..., . If inequality 12 is satisfied for a certain number of consecutive samples (e.g., for n = n1, n1 + 1, n1 + 2), then the time index of the first of these consecutive samples (i.e., n = n1) is defined as the onset time of an interference burst (analogous to the previous example from Fig. 10) Similarly, the time of the end of the interference burst can be determined. For this purpose, inequality 12 can be evaluated for decreasing time indices n = N - 1, N - 2, N - 3, ..., and if inequality 12 is satisfied for a certain number of consecutive samples (e.g., for n = n2, n2 - 1, n2 - 2), then the time index of the first of these consecutive samples (i.e., n = n2) is defined as the time of the end of the interference burst.

[0040] All time indices n lying between the detected time indices n1 and n2 (i.e. n1 < n < n2) can be defined as belonging to the interference burst and the digital radar signal y[n] is considered to be affected by interference in the section n1 ≤ n ≤ n2 (see Fig. 12) In the subsequent digital signal processing, the sections affected by interference can be specifically taken into account, for example, to restore the undisturbed signal at least approximately in these sections by means of suitable interpolation / approximation methods.

[0041] It is understood that if an interference burst occurs at the beginning of the considered signal segment y[n] (and thus also in x) envIf the interference burst is located at the beginning of the signal segment y[n], the start of the interference burst may not be readily detectable using the approaches described above. This also applies to the detection of the end of the interference burst if it is located at the end of the considered signal segment y[n]. In these cases, only the end (index n2) of the interference burst (if it is at the beginning of the signal segment) or the beginning (index n1) of the interference burst (if it is at the end of the signal segment) is detected. In this case, the affected section is 0 ≤ n ≤ n2 or n1 ≤ n ≤ N - 1. The cases "interference burst is at the beginning of the segment" and "interference burst is at the beginning of the segment" can be distinguished using various criteria (e.g., by comparing the envelope signal x). env[n] with a fixed, predefined threshold. Furthermore, it is also possible that a segment is affected by multiple interference bursts. In this case, the segment can, for example, be divided into subsegments, each with one interference burst, and these subsegments are processed according to the concepts described here. The presence of multiple interference bursts can also be detected, for example, by means of a fixed, predefined threshold.

[0042] Several aspects of the procedure described here will be illustrated below using the flowchart. Fig. 13 summarized, although this is not a complete but merely an exemplary list of technical features. According to Fig. 13 provides a radar receiver (see e.g. Fig. 5, RX channel RX1, baseband signal processing 20 and ADC 30) a digital radar signal y[n] which comprises a multitude of segments (see Fig. 13, step S1). The processing of the radar signal y[n] can be performed segment by segment. In the examples described here, each of the segments can be assigned a specific chirp (see Fig. 6) in the RF transmission signal s RF (t) are assigned (one-to-one assignment), but this is not necessarily the case.

[0043] According to Fig. 13 will be an envelope signal x env [n] is calculated, which represents the envelope of a segment of the digital radar signal y[n]. (see Fig. 13, step S2). That is, for each segment of the digital radar signal y[n], an envelope signal x can be generated. env [n] are determined (segment by segment). However, the calculation does not necessarily have to be performed for every segment of the digital radar signal y[n]. In the examples described here, however, an envelope signal x is determined for each segment that will later be processed for a radar measurement. env[n] is calculated. Subsequently, using at least one statistical parameter of the envelope signal x, env [n] - a (start) time is calculated at which an interference signal contained in the considered segment of the digital radar signal y[n] begins (see Fig. 13, step S3). To determine at least one statistical parameter of the envelope signal x env To determine [n], a statistical analysis of the envelope signal x can be used. env [n] can be performed. In particular, means and standard deviations can be calculated.

[0044] According to one example, determining the aforementioned (start) time (denoted by the time index n1 in the examples described above) involves determining at least one statistical parameter of the envelope signal x. env [n] for a plurality of successive time indices of the envelope signal x env[n], the calculation of a threshold value (see Equation 8, threshold µ[n] + λ · σ[n]) for each of the successive time indices based on the associated at least one statistical parameter, and the detection of the time index n1, which represents the time of the start of the interference signal (burst). The detection is based on a comparison of the envelope signal x env [n] with the associated threshold values ​​(see equation 8 and Fig. 10) for the multitude of successive time indices. The at least one statistical parameter can be a mean and a standard deviation of the envelope signal x. env [n], each calculated within a variable time window. Variable time window means that the (temporal) length of the time window varies. It starts, for example, at a fixed starting index (e.g., n=0) and ends at the currently considered time index n of the envelope signal x. env[n]. That is, each time index n of the envelope signal x env [n] can be assigned a mean µ[n] and a standard deviation σ[n]. Similarly, for each time index n of the envelope signal x env [n] a threshold value µ[n] + λ · σ[n] can be calculated.

[0045] According to another embodiment, determining the aforementioned (start) time of the interference signal (burst) involves determining a statistical parameter (e.g., standard deviation σ[n]) of the envelope signal x env [n] for a multitude of consecutive time indices. Subsequently, a threshold value (e.g., λ · σ, see Equation 11) is calculated based on the determined statistical parameters. That is, in this example, for each envelope signal x env[n] a (single) threshold is calculated. Subsequently, the aforementioned time index, representing the time of onset of the interference signal, is detected. This detection is based on a comparison of the statistical parameters determined for a multitude of successive time indices with the single threshold (see Equation 12). The at least one statistical parameter can be a standard deviation σ[n] of the envelope signal x. env [n], which is calculated in a moving time window (with, for example, constant length), and the threshold can be calculated based on a mean σ of the determining standard deviations σ[n]. Unlike in the previously described example, in this case the samples of the envelope signal x are not used. env instead of comparing [n] with a (variable) threshold, the calculated standard deviations σ[n] are compared - for each time index - with a constant threshold determined for the respective segment.

Claims

[1] A method which has the following features: Providing a digital radar signal (y[n]) by means of a radar receiver, wherein the digital radar signal (y[n]) comprises a plurality of segments; Calculating an envelope signal (x env [n]), which represents the envelope of a segment of the digital radar signal (y[n]); Determining a time (n1) of the start of an interference signal contained in the segment of the digital radar signal (y[n]), wherein the determination comprises the following: Determining a statistical parameter (µ[n], σ[n]) of the envelope signal (x env [n]) for a plurality of successive time indices of the envelope signal (x env [n]); Calculate at least one threshold value (µ[n] + λ · σ[n], ​​λ · σ) based on at least one statistical parameter (µ[n], σ[n]), Detecting a time index (n1) representing the time of onset of an interference signal, based on a comparison of the envelope signal (x env [n]) with at least one threshold value (µ[n] + λ · σ[n], ​​λ · σ). [2] The method according to claim 1, wherein determining the time (n1) comprises: statistical analysis of the envelope signal (x env [n]), to determine at least one statistical parameter of the envelope signal (x env to generate [n]). [3] The method according to claim 1 or 2, wherein the calculation of the envelope signal (x env [n]) includes the following: Calculating an absolute value of an analytical signal representing the digital radar signal (y[n]), where the digital radar signal (y[n]) is already provided by the radar receiver as an analytical signal or the analytical signal is calculated based on the digital radar signal (y[n]) using a Hilbert transform. [4] The method according to claim 1 or 2, wherein the calculation of the envelope signal (x env [n]) includes the following: Calculating an RMS signal based on the digital radar signal (y[n]), and Using the RMS signal as an envelope signal (x env [n]). [5] The method according to claim 1 or 2, wherein the calculation of the envelope signal (x env [n]) includes the following: Using a CFAR algorithm to determine the envelope signal (x env [n]). [6] The method according to any one of claims 1 to 5, wherein determining the time (n1) comprises: Determining at least one statistical parameter (µ[n], σ[n]) of the envelope signal (x env [n]) for a plurality of successive time indices of the envelope signal (x env [n]); Calculating a threshold value (µ[n] + λ · σ[n]) for each of the successive time indices based on the associated at least one statistical parameter; Detecting a time index (n1) representing the time of onset of an interference signal, based on a comparison of the envelope signal (x env [n]) for the plurality of successive time indices with the associated thresholds (µ[n] + λ · σ[n]). [7] The method according to claim 6, wherein the at least one statistical parameter is a mean and a standard deviation of the envelope signal (x env [n]) within a variable time window. [8] The method according to claim 7, wherein the variable time window extends from a fixed start time index to a variable end time index. [9] The method according to any one of claims 1 to 5, wherein determining the time (n1) comprises: Determining a statistical parameter (σ[n]) of the envelope signal (x env [n]) for a plurality of successive time indices of the envelope signal (x env [n]); Calculating a threshold value (λ · σ) based on the statistical parameters (σ[n]) determined for a large number of successive time indices, Detecting a time index (n1) representing the time of the start of an interference signal, based on a comparison of the statistical parameters (σ[n]) determined for a multitude of successive time indices with the threshold (λ · σ). [10] The method according to claim 9, where at least one statistical parameter is a standard deviation of the envelope signal (x env [n]) encompasses a sliding time window, and where the threshold is calculated based on the mean of the determined standard deviations. [11] A radar system which includes the following: a radar receiver designed to provide a digital radar signal (y[n]) wherein the digital radar signal (y[n]) comprises a plurality of segments; a computing unit (40, 50) that is trained to an envelope signal (x env [n]) to calculate the envelope of a segment of the digital radar signal (y[n]); to determine a time (n1) of the start of an interference signal contained in the segment of the digital radar signal (y[n]), wherein the determination includes the following computational steps: Determining a statistical parameter (µ[n], σ[n]) of the envelope signal (x env [n]) for a plurality of successive time indices of the envelope signal (x env [n]); Calculate at least one threshold value (µ[n] + λ · σ[n], ​​λ · σ) based on at least one statistical parameter (µ[n], σ[n]), Detecting a time index (n1) representing the time of onset of an interference signal, based on a comparison of the envelope signal (x env [n]) with at least one threshold value (µ[n] + λ · σ[n], ​​λ · σ). [12] The radar system according to claim 11, wherein the computing unit (40, 50) is configured to determine the time (n1): at least one statistical parameter (µ[n], σ[n]) of the envelope signal (x env [n]) for a plurality of successive time indices of the envelope signal (x env [n]) determine; to calculate a threshold value (µ[n] + λ · σ[n]) for each of the successive time indices based on the associated at least one statistical parameter; and a time index (n1) representing the time of the start of an interference signal, based on a comparison of the envelope signal (x env [n]) for the multitude of successive time indices with the associated thresholds (µ[n] + λ · σ[n]). [13] The radar system according to claim 12, wherein the at least one statistical parameter is a mean value and a standard deviation of the envelope signal (x env [n]) within a variable time window. [14] The radar system according to claim 11, wherein the computing unit (40, 50) is configured to determine the time (n1): a statistical parameter (σ[n]) of the envelope signal (x env[n]) for a plurality of successive time indices of the envelope signal (x env [n]) to determine; to calculate a threshold value (λ · σ) based on the statistical parameters (σ[n]) determined for a large number of successive time indices; and to detect a time index (n1) representing the time of the start of an interference signal, based on a comparison of the statistical parameters (σ[n]) determined for a multitude of successive time indices with the threshold (λ · σ). [15] The radar system according to claim 14, wherein the at least one statistical parameter is a standard deviation of the envelope signal (x env [n]) encompasses a moving time window, and wherein the threshold is calculated based on a mean of the determining standard deviations.

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