Patent Description:
As uses of RF transceiving technologies proliferate, so does the likelihood of potentially performance degrading interference being received by a RF transceiver, such as an automotive radar system. Possible effects include reduced ability of the RF receiver to detect objects of interest and/or to estimate relevant parameter values of those detected objects, such as object range, object bearing, and/or object Doppler. The reliable detection of degrading interference becomes especially important for safety-critical applications. For example, a radar mounted in the front bumper of a vehicle may be relied upon to detect objects in the path of the host vehicle. If the radar cannot perform its function within specifications due to the presence of interference, then it must notify the controlling system of this situation so that it can recover to a safe state.

Interference may be categorized in several ways, such as continuous versus intermittent, narrowband versus wideband, etc. Narrowband interferers may include AM radio broadcasts and amateur radio signals. Wideband noise that extends across a large portion of the RF spectrum may also interfere. Wideband noise can be caused, for example, by electrical machinery, internal combustion engines (e.g., lawn mowers), fluorescent lights, and other sensors. Wideband interferers can be somewhat random, and may be more difficult to avoid. The bandwidth of a RF receiver can be approximated by the inverse of its receiver gate. For example, the bandwidth of a radar receiver having receiver gate of 400ns is <NUM>. An example of a wideband interference source is the RF transmission of a narrow pulse, such as having a duration of 10ns, which would have a bandwidth of <NUM>. Another example of a wideband interference source is a fast frequency chirp, such as continuous wave (CW) transmission that changes frequency in steps or continuously over a span of <NUM> over a duration of <NUM>µsec.

All RF receivers experience electrical fluctuations produced by internal components, which is known as thermal noise. The thermal noise and a desired signal of interest (SOI), such as a reflection from a target object of a RF signal transmitted by the sensor, undergo subsequent amplification. The thermal noise may change with temperature, component aging and/or be inherently distinct for different radar systems.

A significant challenge is to distinguish between the presence of degrading interference and ordinary changes in thermal noise contributions. The latter may be caused by changes in receiver-chain amplification, which affects the signal and noise equally, thereby not presenting a significant change in receiver functionality. The former, however, causes a significant increase in receiver noise levels without the corresponding improvement in signal quality, therefore degrading the ability of the receiver to perform its intended function.

Current technologies might attempt to solve this interference differentiation problem by estimating the thermal noise level of the receiver. The presence of interference would be declared when the receiver noise level exceeds some threshold above the estimated thermal noise level. For example, interference might be declared when the receiver noise consistently exceeds 1OdB above the estimated thermal noise level. This technique, however, does not always provide good results, due to the challenge of accurately estimating the thermal noise level of the receiver. In practical receivers, there can be significant variability in receiver- chain gain due to changes in temperature, component aging and/or between different instances of manufactured radars. It is often beyond state-of-the-art and/or available resources to adequately characterize and model the gain in order to adequately predict thermal noise levels.

The direct measurement of thermal noise level by the receiver is another possible technique. The implementation of this technique, however, may require the presence of additional and costly receiver circuitry. Itmay also require the interruption of normal radar functionality in order to perform this measurement, which might be undesirable.

Poor estimation of thermal noise levels can cause excessive false detections of interference. This might be remedied by raising the detection threshold, for example, from l0dB to 25dB. A higher threshold, however, degrades the ability to detect the presence of lower, yet still significantly degrading, levels of interference in the radar receiver. To achieve very low false-detection rates, the threshold might even need to be increased to such a high level that degrading interference can usually not be discriminated.

Therefore, there is a need for a RF interference detector and method with improved identification of system performance degrading interference.

<CIT>, which is considered closest prior art, discloses a method for interference detection in a communication system by evaluating (i.e. by comparing with a threshold) the difference between the thermal noise detected between two tones of sub-carriers of guard (frequency) intervals and the noise in a pilot signal.

D1 does not disclose time intervals of different lengths (short/extended) in which the thermal noise level and the intermittent interference-plus-noise level are measured, respectively.

<CIT> discloses an automotive radar with radio-frequency interference avoidance.

Wikipedia discloses order statistics (https://en. org/w/index. php? title=Order statistic&oldid= <NUM>) and Rayleigh distributions
(https://en. org/w/index. php? title=Rayleigh_distribution&oldid= <NUM>).

<CIT> discloses a vehicle sensor system and process.

The present disclosure describes embodiments of improved apparatus (e.g., a detector, receiver, etc.) and methods to detect narrowband and/or wideband interference present with a SOI in RF signal received by a RF receiver. The embodiments utilize automated real-time signal analysis characterizing changing densities and distributions of signal features with order statistical filtering.

Accurate detection of interference is important to ensure proper operation of the RF receiver. For example, it can permit the generation of notification signals such as alerts or control signals for deployment of counter measures to mitigate the harmful effects of the interference. One example of a counter measure would be to adjust the RF transceiver's transmission frequency plan in order to avoid or to reduce the interference. Another example is to activate a special operating mode that might degrade performance during normal operation, but provide a net improvement in functionality during the presence of degrading interference.

In one embodiment, the detected objects from an automotive radar may be used to implement an application. For the example of an automotive radar installed in the corner of the rear bumper, an application might be blindspot detection. Such applications provide an audio, visual, and/or haptic warning to alert the driver to the presence of another vehicle in the blindspot region of the driver's vehicle. Depending on details of the radar design and the interference, the presence of interference could cause false alerts to the driver and/or missed alerts for actual vehicles in the blindspot region. In such cases, interference detection can alternatively be used to determine when to disable the blindspot application and to inform the driver that the application is unavailable. Alternatively, interference detection could also be used to improve performance by, for example, activating alternative algorithms to improve application performance in the presence of interference. In certain embodiments, an interference alert is issued, or an interference suppression or avoidance application invoked, if an interference metric determined by the methods or apparatus described herein exceeds an alert threshold for a predetermined number of sampling cycles. The alert or invoked application may then be ceased if the interference metric falls below the alert threshold (e.g., immediately, or after a predetermined time, number of cycles, etc.).

Any of the aspects and/or embodiments described herein can include one or more of the following embodiments. In one embodiment, a method is provided wherein an RF signal is detected at a receiver. The received RF signal may include an RF SOI and potentially an intermittent interference signal occupying an interference bandwidth. Thermal noise of the receiver may be estimated by statistically analyzing a plurality of time intervals of spectral magnitude data of the received RF signal, including at least one spectral magnitude data interval not including the intermittent interference signal. Order statistics may be applied to the spectral magnitude data to estimate the thermal noise, and to estimate an intermittent-interference-plus-noise level by statistically analyzing an extended time interval of the spectral magnitude data so that the degrading interference, if present, is included in the analyzed spectral magnitude data. An interference metric may then be determined based on a ratio of the estimated intermittent-interference-plus-noise level to the estimated thermal noise. The interference metric may then be evaluated against one or more thresholds to detect the presence or absence of degrading RF interference.

In one embodiment, estimating the thermal noise comprises obtaining a frequency domain representation of the plurality of time intervals, wherein the frequency domain representation includes a magnitude level for each of a plurality of frequencies sorted in an order statistic (e.g., Rayleigh or other) distribution. A value may be determined associated with a thermal-noise reference percentile relative to the distribution as a raw thermal noise estimate, and the raw thermal noise estimate may be conditioned to compensate for estimation bias in order to obtain the thermal noise estimate. Estimating the intermittent-interference-plus-noise level may include obtaining a frequency domain representation of the extended time interval, wherein the frequency domain representation includes a magnitude level for each of a plurality of frequencies sorted in an order statistic Rayleigh distribution. Then a value may be determined associated with the reference percentile relative to the Rayleigh distribution as a raw intermittent-interference-plus-noise level estimate, and the raw intermittent-interference-plus-noise level estimate may be conditioned to compensate for estimation bias to obtain the intermittent-interference-plus-noise level estimate.

A selected number of samples of the extended time interval may be discarded prior to obtaining the frequency domain representations. The thermal-noise reference percentile may comprise a standard deviation percentile along the Rayleigh distribution selected to be lower than representations of RF object reflections and degrading interference in the Rayleigh distribution.

In one aspect, estimate conditioning may comprise eliminating outlier and averaging remaining thermal noise or intermittent-interference-plus-noise level estimates, respectively, over several sampling cycles. Obtaining the frequency domain representations may further comprise respectively reducing sidelobe energies of the frequency domain representations through application of a window approximation (e.g., a Kaiser window, etc.).

In another embodiment, normalization techniques may be applied in order to compensate for time domain attenuation underestimations of the thermal noise and intermittent-interference-plus-noise level estimates resulting from the window approximation.

In another aspect, obtaining the frequency domain representations may comprise applying a FFT of respective lengths. Whereas an FFT length associated with the thermal noise estimation may comprise a fraction of the number of time intervals in the plurality of time intervals, the FFT length associated with the intermittent-interference-plus-noise level estimation may be equal to a number of time domain samples of the extended time interval. The use of distinct FFT lengths may result in scaling changes in the thermal noise and intermittent-interference-plus-noise level estimates. Normalization may be utilized to compensate for such operations.

One or more parameters may be received by the RF receiver (or a detector or controller of the receiver) specifying at least one of the number of time intervals in the plurality, a number of time intervals to be discarded prior to estimating the thermal noise, an FFT length to be used in estimating the thermal noise, an FFT length to be used in estimating the interference plus noise level, and a percentile for identifying a reference percentile relative to an order statistic Rayleigh distribution of the spectral magnitude data.

In another aspect, present disclosure provides an interference detector for use with an RF receiver configured to receive an RF signal including a SOI and potentially an intermittent interference signal occupying an interference bandwidth. The detector may include a controller and/or processor configured to operate in accordance with any of the above method embodiments. In one embodiment, the RF receiver comprises an automotive radar receiver.

In another embodiment, the disclosure provides an RF receiver configured to detect RF interference that includes a front end configured to receive an RF signal including a SOI and potentially an intermittent interference signal occupying an interference bandwidth, and an interference detection controller and/or processor configured to operate in accordance with any of the above method embodiments.

The foregoing and other features and advantages of the embodiments will be apparent from the following more particular description, as illustrated in the accompanying drawing.

The present disclosure is further described in the detailed description which follows, in reference to the noted plurality of drawings by way of non-limiting examples of embodiments of the present disclosure, in which like reference numerals represent similar parts throughout the several views of the drawings.

The details described and illustrated herein are by way of example and for purposes of illustrative description of the exemplary embodiments only and are presented in the case of providing what is believed to be the most useful and readily understood description of the principles and conceptual aspects of the disclosure. In this regard, no attempt is made to show structural details of the subject matter in more detail than is necessary for the fundamental understanding of the disclosure, the description taken with the drawings making apparent to those skilled in that how the several forms of the present disclosure may be embodied in practice. Further, like reference numbers and designations in the various drawings indicate like elements.

The embodiments described herein are directed to interference detection methods and detectors, and RF receivers employing them. <FIG> illustrates an exemplary radar sensor (radar system) <NUM>, the sensor having two receiver channels and one transmitter channel. Radar sensor <NUM> may be configured with conventional RF components and assemblies, such as an antenna array <NUM>, analog front end electronics <NUM>, a digital front end module <NUM>, and a signal processing module <NUM>. Signal processing module <NUM> may be configured with a processor or controller <NUM> and an interference detector <NUM> that operates on a received RF signal <NUM> in accordance with the disclosed embodiments.

<FIG> illustrates multiple steps of an interference detection method <NUM> in accordance with an embodiment. Once the distribution of the thermal noise and interference is known, the statistical model can be used to set thresholds for satisfying interference alert and/or countermeasure initiation or cessation. Method <NUM> will be described in detail with example functional block diagrams shown in additional figures. In general, however, exemplary method <NUM> begins by receiving (step <NUM>) the received RF signal <NUM> detected by radar sensor <NUM>. Received signal <NUM> includes an RF signal of interest (SOI) <NUM>, such as a reflected wave of a transmitted signal emitted by a transmitter of radar sensor <NUM>, and potentially an intermittent interference signal <NUM> occupying an interference bandwidth. In step <NUM>, thermal noise <NUM> is estimated by the interference detector <NUM> by statistically analyzing a plurality of short time intervals of spectral magnitude data of the received RF signal <NUM>, including at least one spectral magnitude data interval not including the intermittent interference signal <NUM>. The interference source cannot be continuously present during operation of the sensor <NUM>. In an exemplary embodiment, "present" may mean detectable for at least a determined time, such as <NUM>. So, not continuously present would mean that there must be some time gaps in the presence and/or effects of the interfering source. For example, an interfering source might have a repeating transmission cycle, where each cycle has example duration of <NUM>. A first segment of the interferer cycle might comprise <NUM> of fast frequency sweeping that causes interference to the sensor <NUM>. A second segment, however, might comprise a pulsed segment at a single carrier frequency lasting <NUM>, which does not cause degrading interference to sensor <NUM>. Finally, a last example segment might consist of a <NUM> period of no RF transmissions.

An interference-plus-noise level <NUM> may be estimated (step <NUM>) by detector <NUM> by statistically analyzing an extended time interval of the data of received RF signal <NUM>, so that the degrading interference, if present, will be included in the extended time interval. In step <NUM>, an interference metric <NUM> may be determined by detector <NUM> based on a ratio of the estimated interference-plus-noise level <NUM> to the estimated thermal noise <NUM>. In step <NUM>, the interference metric <NUM> may be evaluated against one or more thresholds <NUM> to detect the presence or absence of degrading RF interference in received RF signal <NUM>. If the interference metric <NUM> exceeds the one or more thresholds <NUM> under conditions defined by logic of the detector <NUM>, an alert or other countermeasure control signal <NUM> may be issued by the detector <NUM>.

<FIG> is a block diagram representation of an embodiment of an interference detector <NUM> (which may be very similar or identical to detector <NUM>). As shown, interference detector <NUM> may be configured with two data flow channels, one of which may receive a first input data stream <NUM>, for example a stream of <NUM> time-domain samples of the received RF signal <NUM> received at a normal angle to an antenna array of the sensor <NUM>. A second time-domain data stream <NUM> corresponding to the received RF signal <NUM> received at a squint angle with respect to the sensor <NUM> antenna array may be received as input to the second data flow channel. The time-domain data streams <NUM>, <NUM> (which in other embodiments are not limited to normal and squint angle data streams) are each fed into respective beam interference detectors 302A, 302B, which in turn generate, respectively, normal-beam interference alert <NUM> and squint-beam interference alert <NUM> signals. Alerts signals <NUM>, <NUM> may be received by logic module <NUM>, which uses indication(s) of the presence of a degrading interference signal reflected in the values of the alert signals <NUM>, <NUM> to determine whether to issue an interference alert <NUM> (or other counter measure control signal).

<FIG> is a block diagram showing in more detail functional components of an embodiment of an interference detector <NUM>, i.e., implementing 302A and/or 302B. The time-domain data samples <NUM>, <NUM> are received at DC removal module <NUM> where average values of any DC signals present in the samples are removed. The output from DC removal module <NUM> is split into separate noise and interference-plus-noise signal processing data flows. As will be described in detail below, the signal processing channels employ order statistics (OS) in OS filters <NUM>, <NUM> to respectively generate raw noise estimate σrn <NUM> and raw interference plus noise estimate σri+n <NUM>, and conditioning (at conditioners <NUM>, <NUM>) to generate conditioned noise estimate σcn <NUM> and conditioned interference plus noise estimates σci+n <NUM> that are used by detection metric calculator <NUM> to determine an interference-plus-noise to noise ratio <NUM>. OS are advantageous because they are directly related to the underlying distribution and are robust in the presence of outliers. Detection logic <NUM> then uses the detection metric <NUM> to determine whether a degrading interfering signal has been detected; i.e., a raw detection signal <NUM> is output from detection logic <NUM>. Additional alert logic <NUM> then determines whether alert conditions have been met, and if so, an interference alert <NUM> (or countermeasure control signal) is output.

<FIG> illustrates in more detail the operation of an OS filter <NUM>, such as OS filter <NUM> which is used to estimate thermal noise <NUM> and OS filter <NUM> used to estimate interference plus noise in received RF signal <NUM>. OS filter <NUM> utilizes Rayleigh probability distribution <NUM> and a number of process control parameters whose values depend upon whether thermal noise <NUM> or interference plus noise level <NUM> is being estimated. The significance of the example parameter values shown in Table One will be described below.

On each radar cycle, a number (e.g., <NUM>) of time-domain data samples <NUM> of received RF signal <NUM> are received by OS filter <NUM> (after DC components have been removed by DC removal module <NUM>). To achieve reasonable accuracy in determining the noise estimates and detection metric, a large enough sample set must be used to reduce the expected uncertainty to a reasonably small value. Blanker <NUM> discards all but N samples of the data <NUM>, wherein parameter N <NUM> is selected based on whether thermal noise <NUM> or interference <NUM> is being estimated. In the exemplary embodiment, if thermal noise <NUM> is to be estimated, all <NUM> data samples are utilized. If interference level <NUM> is to be estimated, half (e.g., <NUM>) of the data samples may be discarded. A goal of using a larger N time interval sample set for estimating thermal noise <NUM> is to include for analysis at least one time interval in which the intermittent interference signal is not present, so that the interfering signal and the RF SOI do not mask one another at identical or overlapping frequencies. Blanker <NUM> then output N time-domain samples <NUM> to STFTM module <NUM>. STFTM module <NUM> performs a non-overlapping short-time Fourier transform magnitude operation on the N time-domain samples <NUM>, resulting in a frequency domain representation of the time interval data including a magnitude level for each of a plurality of frequencies at which signals are present in the received RF signal <NUM>. STFTM module <NUM> utilizes two additional inputs, a FFT length parameter <NUM> and a window function <NUM>. FFT length parameter <NUM> may be selected to be short for thermal noise estimation, to increase the number of transform operations and the probability that no interfering signal is present in the analyzed time interval data. In contrast, in estimating interference, FFT length parameter <NUM> may be selected to be an extended time interval, e.g., equal to the number N of the time domain samples <NUM>. Window function <NUM> is generated by window module <NUM>, which accepts as an input the FFT length parameter <NUM> and window type parameter <NUM> (e.g., a Kaiser window, etc.). Window function <NUM> operates to reduce sidelobe energies of the frequency domain representations of the spectral data resulting from the STFTM window approximation operation.

STFTM spectral output data <NUM> is then sorted by magnitude in ascending order by sorting function <NUM>. Sorted spectral magnitude data <NUM> is then passed from sorting function <NUM> to Rayleigh parameter estimator <NUM>. The Rayleigh parameter estimator <NUM> operates (as described in detail below) to generate a raw sigma estimate σraw <NUM>, which could comprise raw noise σrn <NUM> or raw interference plus noise σri+n <NUM> of <FIG>, depending upon which variance is being estimated in the particular embodiment of OS filter <NUM>.

The operation of Rayleigh parameter estimator <NUM> will now be described with additional reference to <FIG> and <FIG>. In order to generate raw sigma estimate σraw <NUM>, parameter estimator <NUM> first iteratively generates an unscaled estimate σunscaled <NUM> at Rayleigh statistic converter <NUM>, and then performs FFT normalization <NUM> and window normalization <NUM> on unscaled estimate σunscaled <NUM> to obtain the raw sigma estimate σraw <NUM>. Rayleigh parameter estimator <NUM> estimates the raw sigma estimate σraw <NUM> under which the assumption that the sorted spectral data <NUM> is a random variable having a Rayleigh distribution (as shown in <FIG>). The Rayleigh distribution has one parameter, σ, which is the same as being estimated by the Rayleigh parameter estimator <NUM>, i.e., raw sigma estimate <NUM>. The sorted spectral data <NUM>, however, may include signal components that disturb the Rayleigh distribution. Non-arithmetic estimates are plotted against distribution <NUM> to illustrate different "modes" in the spectrum. Each mode corresponds to a single component in the receive RF signal, including the desired RF SOI <NUM> and the degrading interference signal <NUM>. An advantageous operating principle of the order statistics analyses utilized in the embodiments is the data populating the left-most portion of the probability distribution plot <NUM> comprises predominantly, if not exclusively, thermal noise <NUM> or interference plus noise data. Data representing the RF SOI <NUM> and degrading RF interference signal <NUM> end up plotted towards the far right portion of the distribution <NUM>, as shown for explanatory purposes only. As shown in <FIG>, Rayleigh statistic converter <NUM> receives as an initial input the reference percentile parameter <NUM> (e.g., <NUM>% of the probability distribution) and the sorted spectral magnitude data <NUM>. Rayleigh statistic converter <NUM> represents a function that generates and iteratively refines the accuracy of unscaled estimate σunscaled <NUM> based on these inputs and measures of bias present in the instant estimate. As shown in <FIG>, Rayleigh statistic converter <NUM> initializes (block <NUM>) a counter and initializes (block <NUM>) an adjusted percentile raw estimate to the input reference percentile parameter <NUM>. An iterative process (blocks <NUM> and <NUM>) is then performed to remove any corruption bias that may be present in the raw estimate σraw <NUM> due to the presence of outliers (e.g., from reflected target objects signals, interference, etc.) in the Rayleigh distribution <NUM>. For example, if half of the sorted spectral magnitude data <NUM> are corrupted by outliers, this could cause roughly a <NUM>% error in the raw estimate σraw <NUM>. In order to remove the corruption bias, the raw estimate σraw <NUM> is recalculated (in block <NUM>) iteratively with adjustments in an adjusted reference percentile <NUM>'.

On each iteration, an iteration counter is checked (block <NUM>), and if the counter has not exceeded a defined threshold (e.g., <NUM> iterations), a current bias is determined (block <NUM>) by investigating the actual rank of a three standard deviation (3σ )value in the sorted and plotted spectral magnitude plot <NUM> and adjusting (blocks <NUM> and <NUM>) the adjusted reference percentile <NUM>' such that the 3σ value represents a <NUM>th percentile in the sorted spectral magnitude plot <NUM>. The iterative loop counter is also incremented in block <NUM>. When the counter is determined (block <NUM>) to meet or exceed the iterative loop count, the bias removal process ceases and Rayleigh statistic converter <NUM> outputs the current unscaled estimate σunscaled <NUM>, whereupon it undergoes FFT normalization <NUM> and window normalization <NUM>. FFT normalization <NUM> compensates for scaling introduced by using different FFT lengths in STFTM module <NUM>. The unscaled σ estimate <NUM> is multiplied by a correction factor comprising the inverse square root of the FFT length parameter <NUM>. Window normalization <NUM> applies a noise-loss correction factor to compensate for underestimation error introduced in the time domain by the application of window function <NUM>. The output of window normalization is a compensated raw noise estimate σraw <NUM>.

With reference again to <FIG>, OS filters <NUM>, <NUM> output respectively raw noise estimate σrn <NUM> and raw interference plus noise estimate σri+n <NUM>. The raw noise estimate σrn <NUM> is output on each cycle of the RF receiver to noise conditioner <NUM>, details of which are shown in <FIG>. In the embodiment shown, noise conditioner <NUM> gathers raw noise estimates σrn <NUM> (block <NUM>) over a predefined number (e.g., <NUM>) of RF receiver cycles, and selects for output the minimum value of the collected set, i.e., the noise σ least corrupted by noise of interference. The selected minimum noise σ has corrupting bias removed (block <NUM>), and the conditioned noise estimate σcn <NUM> is then output from noise conditioner <NUM>. <FIG> shows details of the operation of an embodiment of interference conditioner <NUM>, which receives raw interference plus noise estimate σri+n <NUM> over a similar number of RF receiver cycles and performs a <NUM>% trimmed mean operation thereupon. That is, from the (exemplary) <NUM> samples received, interference conditioner <NUM> discards the largest two values and the two smallest values, and outputs as the conditioned interference plus noise estimate σci+n <NUM> the average of the remaining input samples.

With reference again to <FIG>, conditioned noise estimate σcn <NUM> and conditioned interference plus noise estimate σci+n <NUM> are passed to detection metric calculator <NUM>, exemplary functions of an embodiment of which are shown in <FIG>. DMC <NUM> computes (blocks <NUM>, <NUM>) a ratio of the two values, applies a maximum operator (block <NUM>) to reduce fluctuations in the ratio, and converts (block <NUM>) the ratio in decibel (dB) units. DMC <NUM> outputs the detection metric <NUM>. The detection metric <NUM> is then passed to detection logic <NUM> of the interference detector <NUM>. An example embodiment of detection logic <NUM> is shown in <FIG>, wherein detection logic <NUM> compares the detection metric <NUM> to respective values of an "On" threshold <NUM> and an "Off' threshold <NUM>, and outputs raw detection state signal <NUM> that indicates the current state (i.e., On, Off, or indeterminate state where no changes to current actions is to be taken. ) The detection state signal <NUM> may be passed to alert logic <NUM> of the interference detector <NUM>, as shown in <FIG>. Alternatively, the detection state signal <NUM> may immediately trigger or cease an alert or an invoked countermeasure application.

An example embodiment of Alert logic <NUM> is shown in <FIG>, wherein the raw detection signal <NUM> and a counter <NUM> may be utilized to introduce RF receiver cycle-based delays by only taking actions, such as turning on or off alerts or countermeasures, after a predefined number of cycles in which a change in the raw detection signal <NUM> has been encountered.

Interference detectors in accordance with the embodiments provide reliable detection of degrading interference in mild (e.g., receiver noise floor increases of <NUM>-<NUM> dB) to severe (e.g., <NUM> dB or greater increases) interference conditions. The incidences of false alarms given during conditions of no interference are extremely low.

Numerous Monte-Carlo simulations were performed to demonstrate the robustness of the degrading interference detection methods and detectors. Variables introduced into the simulations included RF detection scenario parameters such as numbers of target objects superposed in a region of interest being sensed by the sensor <NUM>, object radar cross section, object ranges (from the sensor <NUM>), object velocities, no interference conditions, and conditions of intermittent interference comprising an interference model including a repeating pattern of transmission followed by radio silence formed by adding a complex Gaussian random variable to each time-domain sample collected during any transmission period. Variations in receiver parameters were also employed, such as thermal noise standard deviations, IQ balance, and ADC quantization. Consistent transmission waveforms from the sensor <NUM> and interference control parameters (i.e., numbers of spectral data samples, noise and interference FFT lengths, noise percentile, window type, and detection thresholds, etc.) were utilized.

<FIG> shows an example of spectral magnitude values <NUM> from which a <NUM>% thermal noise reference percentile parameter was collected. The x-axis indicates frequency translated to Doppler in units of meters/second for a particular radar sensor. The y-axis provides spectral magnitude in decibels. <FIG> shows <NUM> separate spectra, one corresponding to a <NUM>-point FFT used for interference estimation, and the remaining corresponding to the <NUM>-point FFTs used for estimating thermal noise. These spectral values correspond to FFT normalization <NUM> of <FIG>. <FIG> shows, for a scenario of low thermal noise (stdev = <NUM>) and no interference, values over time of conditioned interference-plus-noise estimate <NUM> (such as previously described σci+n <NUM>) and conditioned noise estimate <NUM> (such as previously described σcn <NUM>). The estimated noise levels 1002a, 1004a are consistent with the scenario parameters. <FIG> shows, for the same scenario, a detection metric 1006a (such as previously described metric <NUM>) over time. The small variation of the metric 1006a from 0dB level <NUM>, and significant value below both On detection threshold <NUM> (e.g., threshold <NUM>) and Off detection threshold <NUM> (e.g., threshold <NUM>) indicates that no interference energy is present.

<FIG> show, for a scenario of higher thermal noise (i.e., std dev of <NUM>, versus <NUM> in the previous scenario) and no interference, the resulting noise levels 1002b, 1004b (e.g., corresponding to estimates <NUM> and <NUM> respectively) are correspondingly higher, but the detection metric 1006b is not disturbed by the higher noise level.

<FIG> show resulting signals for a scenario of low thermal noise (std dev <NUM>), and no interference, but in the presence of a single <NUM><NUM> target object at <NUM> range and moving at <NUM>/s. The noise levels 1002c, 1004c are centered about the stdev <NUM> axis, and the detection metric 1006c remains undisturbed by the presence of the target object.

<FIG> shown resulting signals for a scenario of low thermal noise and no interference, but in the presence of sixteen (<NUM>) <NUM><NUM> target objects. Again, the noise level 1002d, 1004d are commensurate with input noise, and the detection metric 1006d remains undisturbed by the presence of numerous target objects.

<FIG> show resulting signals for a scenario of low thermal noise and no target objects, but with the model interference signal (stdev = <NUM>; interference-to-noise ratio = 20dB) applied. A higher conditioned interference plus noise estimate 1002e is shown, separate from the conditioned noise estimate 1004e. The interference detection metric 1006e now indicates the presence of the interfering signal, rising above both the On detection threshold <NUM> and Off detection threshold <NUM>.

<FIG> show resulting signals for a scenario of low thermal noise and no interference, in the presence of <NUM> target objects, and with a severe IQ imbalance (i.e., IQ amplitude balance = <NUM>; IQ phase balance = <NUM>°). The detection metric 1006f is not disturbed by the severe IQ imbalance. False alarms are not generated, as the detection metric 1006f is well below the On threshold <NUM> and only exceeds the Off threshold <NUM> briefly.

<FIG> show resulting signals for a scenario of the thermal noise floor being below an ADC quantization bit of the simulated RF receiver, for example where the thermal noise stdev = <NUM>, is <NUM> bits below the quantization bit, in the presence of no interference and <NUM> target objects. In this scenario, the noise levels <NUM>, <NUM> are increased equally, the detection metric <NUM> is not disturbed, and no false alarms are generated.

<FIG> show resulting signals for a scenario of low thermal noise, in the presence of no interference, but in the presence of a massively large (<NUM><NUM>) target object, at close range (<NUM>) and impossibly fast acceleration (<NUM>/s<NUM>). There is a slight separation between the lower conditioned interference plus noise estimate <NUM> and the conditioned noise estimate <NUM>, but the detection metric <NUM> is not significantly disturbed, and no false alarms are issued.

Various embodiments of the above-described RF receivers, detectors and methods may be implemented in digital electronic circuitry, in computer hardware, firmware, and/or software. The implementation can be as a computer program product (i.e., a computer program tangibly embodied in an information carrier). The implementation can, for example, be in a machine-readable storage device and/or in a propagated signal, for execution by, or to control the operation of, data processing apparatus. The implementation can, for example, be a programmable processor, a computer, and/or multiple computers.

A computer program can be written in any form of programming language, including compiled and/or interpreted languages, and the computer program can be deployed in any form, including as a stand-alone program or as a subroutine, element, and/or other unit suitable for use in a computing environment. A computer program can be deployed to be executed on one computer or on multiple computers at one site.

Method steps can be performed by one or more programmable processors and/or controllers executing a computer program to perform functions of the invention by operating on input data and generating output. Method steps can also be performed by, and an apparatus can be implemented as, special purpose logic circuitry. The circuitry can, for example, be a FPGA (field programmable gate array) and/or an ASIC (application-specific integrated circuit). Modules, subroutines, and software agents can refer to portions of the computer program, the processor, the special circuitry, software, and/or hardware, e.g., a controller such as a microcontroller, which implements that functionality.

Generally, a processor receives instructions and data from a read-only memory or a random access memory or both. Generally, a computer can be operatively coupled to receive data from and/or transfer data to one or more mass storage devices for storing data, e.g., magnetic, magneto-optical disks, or optical disks.

Data transmission and instructions (e.g., for process control parameter selection, etc.) can also occur over a communications network. Information carriers suitable for embodying computer program instructions and data include all forms of non-volatile memory, including by way of example semiconductor memory devices. The information carriers can, for example, be EPROM, EEPROM, flash memory devices, magnetic disks, internal hard disks, removable disks, magneto-optical disks, CD-ROM, and/or DVD-ROM disks. The processor and the memory can be supplemented by and/or incorporated in special purpose logic circuitry.

To provide for interaction with a user, the above described techniques can be implemented on a computer having a display device. The display device can, for example, be a cathode ray tube (CRT) and/or a liquid crystal display (LCD) monitor. The interaction with a user can, for example, be a display of information to the user and a keyboard and a pointing device, e.g., a mouse or a trackball, by which the user can provide input to the computer, e.g., interact with a user interface element. Other kinds of devices can be used to provide for interaction with a user. Other devices can, for example, be feedback provided to the user in any form of sensory feedback, e.g., visual feedback, auditory feedback, or tactile feedback. Input from the user can, for example, be received in any form, including acoustic, speech, and/or tactile input.

The above described techniques can be implemented in a distributed computing system that includes a back-end component. The back-end component can, for example, be a data server, a middleware component, and/or an application server. The above described techniques can be implemented in a distributing computing system that includes a front-end component. The front-end component can, for example, be a client computer having a graphical user interface, a Web browser through which a user can interact with an example implementation, and/or other graphical user interfaces for a transmitting device. Examples of communication networks include a local area network (LAN), a wide area network (WAN), the Internet, wired networks, and/or wireless networks.

The system can include clients and servers. A client and a server are generally remote from each other and typically interact through a communication network.

Packet-based networks can include, for example, the Internet, a carrier internet protocol (IP) network, e.g., local area network (LAN), wide area network (WAN), campus area network (CAN), metropolitan area network (MAN), home area network (HAN)), a private IP network, an IP private branch exchange (IPBX), a wireless network, e.g., radio access network (RAN), <NUM> network, <NUM> network, general packet radio service (GPRS) network, HiperLAN), and/or other packet-based networks. Circuit-based networks can include, for example, the public switched telephone network (PSTN), a private branch exchange (PBX), a wireless network, e.g., RAN, Bluetooth, code-division multiple access (CDMA) network, time division multiple access (TDMA) network, global system for mobile communications (GSM) network), and/or other circuit-based networks.

The computing system can also include one or more computing devices. A computing device can include, for example, a computer, a computer with a browser device, a telephone, an IP phone, a mobile device, e.g., cellular phone, personal digital assistant (PDA) device, laptop computer, electronic mail device, and/or other communication devices. The browser device includes, for example, a computer, e.g., desktop computer, laptop computer, with a World Wide Web browser, e.g., Microsoft® Internet Explorer® available from Microsoft Corporation, Mozilla® Firefox available from Mozilla Corporation. The mobile computing device includes, for example, a Blackberry®, iPAD®, iPhone® or other smartphone device.

Whereas many alterations and modifications of the disclosure will no doubt become apparent to a person of ordinary skill in the art after having read the foregoing description, it is to be understood that the particular embodiments shown and described by way of illustration are in no way intended to be considered limiting. It is noted that the foregoing examples have been provided merely for the purpose of explanation and are in no way to be construed as limiting of the present disclosure.

Claim 1:
A method (<NUM>) of detecting RF interference, comprising the steps of:
receiving (<NUM>) an RF signal (<NUM>) detected at a receiver (<NUM>), the received RF signal (<NUM>) including a desired RF signal (<NUM>);
estimating (<NUM>) thermal noise (<NUM>) of the receiver (<NUM>) by statistically analyzing a plurality of short time intervals of spectral magnitude data of the received RF signal (<NUM>);
estimating (<NUM>) an intermittent-interference-plus-noise level (<NUM>) by statistically analyzing an extended time interval of the spectral magnitude data, the extended time interval being longer than each short time interval;
determining (<NUM>) an interference metric (<NUM>) based on a ratio of the estimated intermittent-interference-plus-noise level (<NUM>) to the estimated thermal noise (<NUM>); and
evaluating (<NUM>) the interference metric (<NUM>) against one or more thresholds (<NUM>) to detect the presence or absence of degrading RF interference comprising an intermittent interference signal that is not included in at least one of said short time intervals of spectral magnitude data, said intermittent interference signal occupying an interference bandwidth.