According to certain embodiments, a method by a network node for linear chirp detection includes obtaining a first number, N, of samples of a signal. The samples are divided into at least a first group and a second group, where the first group includes a second number, D, of the samples of the signal and the second group includes a third number, N−D, of the samples of the signal. A correlation is performed between the first group of samples and the second group of samples to generate a resultant group of samples of the signal. Within the resultant group of samples, a peak value is identified in the frequency domain Based on at least one property associated with the peak value, it is determined whether there is a linear chirp within the signal.

The present description concerns a method of processing by a radio transmitter/receiver (12) of a radio signal (SR) comprising a telecommunications signal disturbed by pulses of a radar signal, the method comprising the steps of: estimating the instantaneous power of the pulses, estimating the ratio of the average power of the telecommunications signal to the instantaneous power of the radar pulses, and modifying the radio signal at the locations of the radar pulses when said ratio is smaller than a threshold.

A feature detection system, the system comprising: at least one processor in operative communication with a signal source, said processor further comprising at least one non-transitory storage medium, wherein at least one non-transitory storage medium contains instructions configured to cause the processor to: apply a joint group sparse denoising and delay estimation approach to a signal received from said signal source; and output statistics regarding the signal, wherein the joint group sparse denoising and delay estimation approach comprises; using the following equation:

[00001]$\left\{x^{*},{\tau }^{*}\right\}=\underset{x,\tau }{\mathrm{argmin}}\left\{\frac{1}{2}\underset{j=1}{\overset{M}{.Math.}}{.Math.y_j-x.Math.}_2^2+\underset{i=0}{\overset{N}{.Math.}}{\lambda }_i{\phi }_i\left(D_i^{l_i}x\right)\right\}$

where: ϕ.sub.i are regularization functions; ∥y−x∥.sub.2.sup.2 is a data-fidelity term and, in embodiments, is chosen as the least-square term; l.sub.i are real numbers; D.sub.i are operators, which may be linear filters that can be written in matrix form; λ.sub.i are regularization parameters; and x*,τ* represent estimates of at least one transmitted pulse and associated delay, and solving the equation for multiple values of ϵ; choosing a vector, x, such that a cost function comprising the data fidelity term and regularization function is minimized; determining the ϵ that corresponds to the x that minimizes the cost function; and calculating the pulse width of the received signal, which corresponds to the desired estimate of the vector, x.

A signal divider includes: a dividing circuit arranged to generate an output oscillating signal according to a first input oscillating signal; and a signal generating circuit, coupled to the dividing circuit, for generating an injection signal to the dividing circuit. The dividing circuit is arranged to generate the output oscillating signal with a predetermined phase according to the injection signal and the first input oscillating signal.

The present invention relates to devices and methods for detecting the blockage of a radar sensor, and radar apparatuses. The radar sensor blockage detection device may include a first determiner for determining an external environment state based on at least one of external weather data, external image data, and vehicle status data, and determining detection sensitivity according to a result of the determined external environmental state, and a second determiner for determining whether the radar sensor is blocked based on a result of the determined detection sensitivity and signal data of the radar sensor.

A signal transmission method includes a radar detection apparatus selecting a transmit frequency band from a predefined or pre-specified first frequency band. The first frequency band is pre-divided into M sub-frequency bands, and the transmit frequency band includes N sub-frequency bands in the M sub-frequency bands, where a bandwidth of the transmit frequency band is greater than or equal to an operating bandwidth of the radar detection apparatus. A sum of bandwidths of any N−1 sub-frequency bands in the N sub-frequency bands is less than the operating bandwidth of the radar detection apparatus. In addition, a minimum quantity of sub-frequency bands is used to transmit a signal.

A cycle estimation device (10) includes: a candidate cycle extraction unit (11) which extracts a candidate cycle that is a cycle determination target from an input time-series pulse train; a pulse train shape analysis unit (12) which converts arrangement of the time-series pulse train into numerical values on the basis of the extracted candidate cycle and outputs a constant that adjusts a random noise threshold value of pulse repetition interval (PRI) conversion in response to an index indicating a degree of concentration of calculated numerical values; and a cycle detection unit (13) which executes PRI conversion using a value of the candidate cycle and the constant and performs cycle determination and cycle value detection.

A method of assessing the effectiveness of an electronic countermeasure (ECM) applied against an unknown, ambiguous, or unresponsive radar threat includes monitoring changes in a radar-associated factor while applying the ECM and determining if the ECM is disrupting the hostile radar. The radar-associated factor can be a weapon that is controlled by the radar threat, and assessing the ECM can include determining whether the weapon is misdirected due to applying the ECM. Or the radar-associated factor can be a feature of an RF waveform emitted by the radar threat, and assessing the ECM can include determining if the feature is changed due to applying the ECM. Continuous changes in the feature can indicate unsuccessful attempts to mitigate the ECM. Return of the feature to a pre-threat state can indicate disruption of the radar. The ECM can be selected from a library of countermeasures pre-verified as effective against known threats.

One or more defined countermeasures are selected from a countermeasure library, populated with parameters, and applied against an unknown, ambiguous, or unresponsive imminent radar threat based on an analysis of a hostile RF waveform emitted by the radar threat. The analysis can include comparing static and/or dynamic features of the hostile RF waveform with features of known hostile RF waveforms. A parameter set associated with the selected defined countermeasure in the countermeasure library can be selected. Waveform features can be categorized and sub-categorized for comparison with the known hostile waveforms. A plurality of features can be detected and compared. The analysis can include correlating behavior patterns of a plurality of hostile RF waveforms emitted by the radar threat. A cognitive intelligence trained using a threat database and library of corresponding countermeasures can analyze the hostile RF waveform, select the defined countermeasure, and/or select or generate the parameters.

A method of selecting and optimizing a countermeasure for application against a novel, ambiguous, or unresponsive radar threat includes selecting a candidate countermeasure and an initial parameter set and varying at least one of the parameters while the effectiveness of the candidate countermeasure against the radar threat is assessed, for example by a human observer. Embodiments include repeating the process with additional candidate countermeasures. For an unresponsive radar threat, a previously effective countermeasure can be selected as the candidate countermeasure. For an ambiguous radar threat, at least one countermeasure previously verified as effective against a partially matching known threat can be selected as the candidate countermeasure. Correlated parameters can be simultaneously varied. An optimization surface and trajectory formed by a plurality of correlated parameters can be identified by machine intelligence, used to guide the parameter variations, and/or stored for use against the same or similar threats in the future.