Systems and methods for object detection and / or classification based on an harmonic model of the radar signature

Abstract

Embodiments pertain to methods and system for detecting and / or characterizing at least one object in a scene that is being propelled by at least one rotating device. The methods may include transmitting electromagnetic radiation (EM) into free space towards the scene; receiving, from the scene, EM radiation reflections that are reflected from at least one rotating device of the at least one object that is present in the scene; and processing the reflections to detect, based on harmonic characteristics of the reflected EM radiation, the presence of the at least one object in the scene. In some examples, the methods include processing the reflections to characterize, based on the harmonic characteristics of the reflected EM radiation, the type of the at least one detected object. In some examples, the processing comprises applying an harmonic filter on the reflected EM radiation to detect harmonics of the reflected EM radiation.

Description

SYSTEMS AND METHODS FOR OBJECT DETECTION AND / OR CLASSIFICATION BASED ON AN HARMONIC MODEL OF THE RADAR SIGNATURE

[0001] This application claims priority of US Provisional Patent Application No. 63 / 522,198 filed on February 12, 2024, titled “Drone Detection by Radar Using Harmonic Signature Model”; and from Israel patent application 318251, filed January 8, 2025, titled “Systems and Methods for Object Detection and Classification Based on Harmonic Model of the Radar Signature”. The contents of the above applications are all incorporated by reference as if fully set forth herein in their entirety. TECHNICAL FIELD

[0002] The present disclosure relates generally to systems and methods for object detection and classification. BACKGROUND

[0003] Detecting drones poses several challenges due to their small size, low altitude, and ability to fly quietly. Drones are often small and fast, making them hard to spot with conventional radar or sensors. Many drones fly at low altitudes, where traditional radar systems may have limited coverage.

[0004] In addition, drones may be designed to be relatively quiet, which makes them harder to detect acoustically. The detection of drones can sometimes be masked by environmental noise, interference from other objects, or their small radar cross-section. These challenges make it difficult to detect drones reliably, especially in real-time or complex environments.

[0005] The description above is presented as a general overview of related art in this field and should not be construed as an admission that any of the information it contains constitutes prior art against the present patent application. BRIEF DESCRIPTION OF THE FIGURES

[0006] In the following description, for purposes of explanation and not limitation, details and descriptions are set forth to provide a thorough understanding of the present disclosure.

[0007] However, it will be apparent to those skilled in the art that the present disclosure may be practiced in other embodiments that depart from these details and descriptions.

[0008] In the following description, the figures which are described illustrate generally, by way of example, but not by way of limitation, various embodiments discussed in the present document.

[0009] For simplicity and clarity of illustration, elements shown in the figures have not necessarily been drawn to scale. For example, the dimensions of some of the elements may be exaggerated relative to other elements for clarity of presentation.

[0010] Furthermore, reference numerals may be repeated among the figures to indicate corresponding or analogous elements. References to previously presented elements are implied without necessarily further citing the drawing or description in which they appear.

[0011] The expression “perspective view” may also encompass the meaning of the term “isometric view” and / or any other representation of 3-dimensional objects in a 2D format.

[0012] The number of elements shown in the Figures should by no means be construed as limiting and is for illustrative purposes only. The figures are listed below.

[0013] Figure 1 is a schematic illustration of radar system for detection and / or classification of an object in a scene, according to some embodiments.

[0014] Figure 2A is a schematic illustration of an object or target, according to some embodiments.

[0015] Figure 2B is a schematic illustration of a rotating device of an object, according to some embodiments.

[0016] Figure 2C is a schematic representation of a virtual abstraction of the rotating device, according to some embodiments.

[0017] Figures 3A-3B are schematic flowchart diagrams of methods for deriving a Harmonic model, according to some embodiments.

[0018] Figure 4 is a schematic illustration of a radar system configured to transmit and receive electromagnetic radiation (EM) for detection and / or classification of an object in a scene, according to some embodiments.

[0019] Figure 5A is a schematic illustration of the spatial-temporal dynamic of a rotating device of an object, according to some embodiments.

[0020] Figure 5B is a corresponding representation of a virtual abstraction of the rotating device spatial temporal dynamics and associated EM reflections, according to some embodiments.

[0021] Figure 6A-6B are schematic illustrations of a virtual abstraction of the rotating device at two different time stamps depicting a simplified representation of a transmitted EM radiation before and after being incident onto the rotating device, according to some embodiments.

[0022] Figure 7A is a schematic illustration of a virtual abstraction of the rotating device for schematically depicting EM radiation propagating in space towards the rotating device, according to some embodiments.

[0023] Figure 7B is a schematic illustration of a virtual abstraction of the rotating device for schematically depicting EM radiation reflected from the rotating device, according to some embodiments.

[0024] Figure 8A is a schematic illustration of a virtual abstraction of EM radiation pattern reflected from the rotating device, according to some embodiments.

[0025] Figure 8B illustrates a graph representing detected intensity as a function of time, without applying a harmonic, of EM radiation reflected from a rotating device comprising two propellers, according to some embodiments.

[0026] Figure 9A illustrates a graph representing the detected intensity without applying a harmonic, over time, of EM radiation reflected from a rotating device comprising four propellers, according to some embodiments.

[0027] Figure 9B illustrates a graph representation of a log-likelihood of the rotation speed of four rotating devices of an object when applying a harmonic model, according to some embodiments.

[0028] Figure 10A illustrates a graph of conventional doppler processing of reflected EM radiation.

[0029] Figure 10B illustrates a graph of a log-likelihood of the rotation speed of four rotating devices of an object when applying a harmonic model, according to some embodiments.

[0030] Figure 11 is a schematic flow chart diagram of a method for the disclosed radar system applying both harmonic model and adaptive transmission of EM radiation based on the processing of the harmonic model outputs, according to some embodiments.

[0031] Figure 12 is a schematic flow chart diagram of a method for a cognitive radar scheme, according to some embodiments.

[0032] Figure 13 is a schematic block diagram illustration of the data flow in the disclosed radar system, according to some embodiments.

[0033] Figure 14 is a schematic illustration of possible objects to be detected and / or classified by the disclosed radar system, according to some embodiments.

[0034] Figure 15 is a schematic flowchart of a method for detecting and classifying objects in a scene, according to some embodiments.

[0035] FIG. A1 is a schematic illustration of rotor blade illumination, according to an example.

[0036] FIG. A2 is a schematic block diagram illustration of a Cognitive scheme for linear system with additive noise, according to an example.

[0037] FIG. A3 is a graph representation of a Detection probability versus SNR, using ^^ = 0.24 ^^^^,according to an example.

[0038] FIG. A4 is a graph representation of Detection probability versus ^^^, using ^^ = 0.48 ^^^^,according to an example.

[0039] FIG. A5 is a graph representation of Detection probability versus number of steps, with ^^^ =8 ^^, using ^^ = 0.48 ^^^^, according to an example.

[0040] FIG. A6 are eight consecutive graph representations of an optimal sampled signal versus time (first and third rows) and posterior pdfs versus Ω0 (second and fourth rows) using KLD optimization,assuming ^ = 4 unknown fundamental frequencies Ω^ = [87.590.59399], according to an example.

[0041] FIG. A7 is a graph representation ofclassification probability versus SNR, using ^ = 4,according to an example.

[0042] Fig. A8 shows correct estimation probability versus SNR, using ^ = 4, according to an example.DETAILED DESCRIPTION

[0043] The present disclosure describes various embodiments of radar systems and methods for detecting and / or classifying one or more (e.g., moving) objects or targets (e.g., mobile platforms) in a scene. The object may include at least one rotating device (e.g., wheel, propeller, turbine disc) employed for facilitating motion of and / or for exerting thrust on the object. Examples of an object include, for example, a vehicle such as, for example, an unmanned vehicle, a manned vehicle, an aerial vehicle (e.g., UAV, drone), a water-based vehicle, and / or a land-based vehicle. It is noted that the term “rotating device” may also refer to a “rotating element”, having one or more rotating reflecting surfaces.

[0044] In some embodiments, electromagnetic (EM) radiation emitted by the radar system may be directed towards a scene. The term “scene” may refer to a particular subdivision of (e.g., free) space, where an area of interest may reside. A scene may comprise at least one object occupying the subdivision of an (e.g., free) space. At least one object occupying the scene may deflect the EM radiation directionality and / or alter EM radiation intensity. In some scenarios, the object may reflect back EM radiation back to the antenna arrangement. EM reflections may herein also be referred to as “radar echo” or “echo signal”.

[0045] According to embodiments, object detection and / or classification may be performed based on the object’s at least one rotating device. In some examples, object detection and / or classification may be performed based on the harmonic reflection characteristics of the at least one rotating device.

[0046] Detection and / or classification of a rotating device may be a challenging problem due to the temporospatial dynamic nature of the element comprising translation, rotation and / or vibration of the rotating device, which may vary across time.

[0047] Metrics, models, tests, approaches, and / or models may be considered to be relevant to the presently disclosed subject matter include micro-Doppler, cognitive radar (CR), Kullback-Leibler divergence (KLD), generalized likelihood ratio test (GLRT), a Harmonic model, or any combination of the aforesaid. The term “cognitive” is used herein as mimicking human cognition through exerting adaptive behaviours. In some embodiments, a non-cognitive approach may be employed.

[0048] In some embodiments, the disclosed radar system and method may be configured to track the detected and / or classified object in the scene.

[0049] It is noted that the term “method” may also encompass the meaning of the term “process”.

[0050] Rotating devices are central constituents of various mobile platforms including, for example, the following: a rotorcraft, a one-wheeled vehicle, two-wheeled vehicle, a three-wheeled vehicle, a four- wheeled vehicle, a land-based vehicle, a watercraft and / or a multipurpose vehicle.

[0051] A rotating device may comprise, for example, one of the following: a rotor, a wheel, a propeller blade, a turbine blade, a turbofan fan blade, an airfoil configuration, and / or any rotating device configured for causing motion of a mobile platform, e.g., a vehicle.

[0052] Embodiments of may relate to detection and / or classification of a rotating device, including a system and method configured to employ a computational and / or statistical module implemented in conjunction with a method for detection and / or classification of the object and / or of the rotating device.

[0053] Furthermore, embodiments may concern a system and method for detecting and / or classifying a vehicle having a rotating device configured to impart onto the vehicle temporospatial dynamics in the scene such as, for example, translation, rotation and / or vibration.

[0054] In some embodiments, it is appreciated that certain features of the present disclosure, which are, for clarity, may be described separately, may also be provided in any combination in a single embodiment .

[0055] In some examples, the rotating device may be described separately from the mobile platform (e.g., the vehicle).

[0056] Currently, multiple innovations may incorporate artificial intelligence (AI) techniques when employing a detection and / or classification scheme, an exemplary state of art classification process may use artificial intelligence (AI) techniques involving convolutional neural networks (CNNs), which may provide improved results on image classification when trained, for example, on radio frequency (RF) spectrograms of different off-the-shelf UAV controller RF signals.

[0057] Aspects of the present disclosure pertain to a method configured to employ, e.g., computational and / or statistical modules, which may refrain from using artificial intelligence (AI) techniques involving training and testing the AI model on a database descriptive of the rotating device characteristics.

[0058] In some embodiments, the system may comprise at least one memory configured to store data and executable instructions.

[0059] In some embodiments, the system may comprise at least one processor, which may be operable to execute instructions stored in the at least one memory element.

[0060] In some embodiments, the executable instructions, which may be stored in at least one memory and / or executed by the at least one processor, may comprise, for example:

[0061] deriving and / or providing (also: implementing, constructing) a harmonic model of a radar signature (also: EM reflections);

[0062] executing a cognitive radar scheme;

[0063] identifying a rotating device;

[0064] classifying a rotating device; and / or

[0065] adaptively calibrating transmitted electromagnetic (EM) radiation.

[0066] Merely to simplify the discussion that follows, without be construed as limiting, methods and processes disclosed herein may be outlined herein separately and / or in conjunction with each other. Namely, the present disclosure may describe the harmonic model separately and / or in conjunction with the execution of the cognitive radar scheme.

[0067] The term “Cognitive radar” may refer to the adaptive nature of the radar system which employs feedback control loop, e.g. adaptively synchronizing the transmitted EM radiation based on the analysis of the current reflected EM radiation harmonic characteristics, observation history, and / or data gathered from external databases, to optimize detection and classification dynamically.

[0068] Where applicable, the term “transmitting” may also encompass the meaning of the term “emitting”.

[0069] For simplicity, without to be construed as limiting, embodiments may refer to a UAV comprising at least one rotating device. Additionally, or alternatively, exemplary reference may be directed to a partially submerged mobile platform (e.g., a boat) and / or fully submerged mobile platform (e.g., a submarine) having at least one rotating device.

[0070] While the disclosure may discuss embodiments relating to the detection of reflections of electromagnetic (EM) radiation from the rotating device, this should by no means be construed in a limiting manner. Analogous principles may also be applied with respect to the detection of acoustic reflections from rotating devices.

[0071] While the present disclosure may be described with respect to a limited number of embodiments, these should not be construed as limitations on the scope of the present disclosure, but rather as exemplifications of some of the embodiments.

[0072] Embodiments of the present disclosure may relate to a radar system for detecting and classifying a rotating device, which may have rotating device characteristics such as, for example, geometric shape (e.g., length, width, thickness); kinematics (e.g., angular velocity), vibrations, etc.

[0073] In some embodiments, characteristics of an object’s rotating device may be processed (also: analyzed) for classifying, based on the analysis, the object.

[0074] In some embodiments, the radar system configured for receiving the executable instructions, which may be stored in at least one memory element and / or operably executed by at least one processor, may comprise, for example, an antenna arrangement comprising a transmitter and / or a receiver for transmitting and / or receiving electromagnetic (EM) radiation, respectively.

[0075] In some embodiments, the radar system may further comprise a controller configured to (e.g., dynamically and / or adaptively) modulate the EM radiation transmitted by the transmitter.

[0076] In some embodiments, the modulation of the transmitted EM radiation may be configured such to synchronize the timing of the transmitted EM radiation intersection with a distinct orientation of the rotating device, e.g., to increase or maximize intensity of the reflected EM radiation. For example, the radar system may be configured to transmit EM radiation in timed coordination with the rotation of the rotating device to attain increase or maximize EM reflections.

[0077] In some embodiments, the modulation of the transmitted EM radiation may be performed based on a processing algorithm employed by an analysis engine. In some examples, the analysis engine may employ computational and / or statistical modules adapted to analyze data stored in memory.

[0078] The term "data" may relate to and / or be descriptive of any digitally or electronically storable and / or transmittable information, such as, for example, data files, data signals, data packages, and / or the like.

[0079] The term “data” may also relate to previously observed EM radiation, optionally detected in response to previously transmitted EM radiation. The data may thus be descriptive of past EM radiation transmissions and corresponding reflections (also: transmission / reflection observation history).

[0080] The data may also be descriptive of various types of mobile platforms, vehicles, and / or frequencies, in association with the corresponding EM radiation transmission and / or reflection.

[0081] The term “data” may comprise theoretical data gathered from external databases, which may be closed-source, restricted-sourced and / or open-source databases.

[0082] It is noted that expressions and grammatical variations of “storing data on a memory element”; “storing data in memory” and “storing data at a radar system” may be used interchangeably.

[0083] In some embodiments, the harmonic model may comprise and / or relate to, for example, a computational and / or statistical module. In some embodiments, the harmonic model may be implemented within a processing algorithm of an analysis engine. In some embodiments, a processing algorithm may employ or comprise a harmonic model. In some embodiments, an analysis engine may comprise a harmonic model. In some embodiments, the processor may execute instructions stored in the memory resulting in the implementation of the analysis engine. In some embodiments, the system may be configured to perform steps as outlined herein.

[0084] In some embodiments, the system comprising the analysis engine may be configured to perform the following:

[0085] virtually subdividing the at least one rotating device into a plurality of rotating segments each having a distinct angular velocity;

[0086] identifying each segment of the virtually subdivided rotating device characterizable reflected EM radiation pattern;

[0087] processing of the received EM reflections based on a plurality of EM radiation patterns associated with the plurality of rotating segments;

[0088] processing of the received EM reflections based on a superposition of the reflected EM radiation patterns; and / or

[0089] deriving from the preformed superposition a coherent harmonic EM radiation.

[0090] In some embodiments, the harmonic model (or the analysis engine) may implement an enhanced micro-Doppler scheme configured for deriving a coherent harmonic EM radiation by the superposition of at least two different receiving EM radiation.

[0091] The term “micro-Doppler” may refer to Doppler frequency shifts which may be caused by micro- motions within an object, e.g., relative to a reference frame (e.g., the scene, and / or the mobile platform). Micro-motions may include, for example, vibrations, rotations, and / or oscillations of parts of the object, such as, for example, limbs, propellers, and / or mechanical components.

[0092] In some examples, micro-motions may include the temporospatial dynamics of the mobile platform, and / or of at least one rotating device of the mobile platform.

[0093] In some embodiments, enhanced micro-Doppler scheme may refer to the processing of a plurality of Doppler frequency shifts caused by micro-motions, e.g., by employing the harmonic model configured to construct a coherent harmonic EM radiation, for receiving an enhanced micro-Doppler output having an improved detectable and / or identifiable micro-Doppler signature, e.g., reflected EM radiation harmonic characteristics.

[0094] The term “micro-Doppler signature” may refer to a distinctive characteristic of the observed micro-Doppler effect in an object. Moreover, the term “signature” may refer to the characteristic expression of an object or a process.

[0095] Thus, when processing data descriptive of the micro-Doppler frequency shifts, the distinctive micro-Doppler characteristic, i.e., the micro-Doppler signature of an object, may enable the detection and / or identification of an object through its idiosyncratic dynamics.

[0096] In some embodiments, attaining a coherent harmonic EM radiation may enable the radar system to disregard “smears” of the reflected EM radiation received by the radar system and / or to enhance the observed EM radiation data, which may exceed a noise threshold.

[0097] The term “smear” may refer to the variation in the radiation intensity, e.g., radiation directivity and / or radiation efficiency, of the reflected EM radiation, which may be consequential of the rotating device angular velocity, spreading the propagation trajectory of the incident and / or reflected EM radiation.

[0098] The term “noise threshold” may refer to a noise magnitude limit which beneath it the received EM radiation may be indistinguishable from EM radiation reflected by surrounding objects the scene.

[0099] In some embodiments, executing a cognitive radar scheme may be configured for (e.g., adaptively) synchronizing the transmitted EM radiation based on the analysis of the reflected EM radiation harmonic characteristics.

[0100] In some embodiments, adapting or controlling the transmission of EM radiation by the radar system may be performed such to excite natural modes of a micro-Doppler signature of the rotating device and / or to maximize reflections and / or radiation intensity from the at least one rotating device.

[0101] In some examples, the term “natural modes” of the micro-Doppler signature may refer to the synchronization of the transmitted EM radiation with the angular velocity of the rotating device, e.g., to result in a reflected EM radiation having a harmonic characteristic, e.g., when applied with harmonic model processing.

[0102] In some embodiments, the adapting (also: controlling) of the transmitted EM radiation may be performed to modulate separately or in any combination, for example, the following:

[0103] EM radiation carrier frequency, pulse repetition interval (PRI), and / or radiation transmission time.

[0104] In some embodiments, processing of EM radiation reflections may include applying a computational and / or statistical module, for example Generalized Likelihood Ratio Test (GLRT), to determine the presence of the at least one rotary element in the scene.

[0105] It is noted that the term “determining” may also encompass the meaning of the term “determining an estimate”.

[0106] In some embodiments, the processing of the reflected EM radiation may further comprise identifying reflections of EM radiation having the greatest intensity.

[0107] Additionally, and / or subsequently, the processing of the reflected EM radiation may comprise virtually subtracting from the memory to be processed the reflected EM radiation having the greatest intensity.

[0108] The term “virtually subtracting” may relate to removing the measured reflected EM radiation having the greatest intensity, such that any subsequential processing of reflected EM radiation may disregard it, e.g., when employing computational and / or statistical modules.

[0109] For example, virtual subtraction may relate to projection of values, which are related to the observed reflected EM radiation having the greatest intensity, onto a complementary subspace, which may be disregarded computationally in any further processing.

[0110] The term “measured”, “sensed”, “observed”, “monitored”, “computed” may be used interchangeably. The terms cited above denote also grammatical variations thereof.

[0111] In some examples, the term “measured” may be referring to estimating a probable value of a parameter of interest, for example, estimating a probable value of, for example, transmitted radiation intensity; radiation intensity incident onto the at least one rotating device; reflected radiation intensityincident onto the receiver; range and / or velocity of the observed object; geometric characteristics of the object; geometric characteristics of the object of the rotating device (e.g., length, width, and / or thickness of a rotor blade); angle of incidence of the EM radiation onto the at least one rotating device; angle of reflection of EM radiation from the at least one rotating device; and / or reflectance of the at least one rotating device.

[0112] In some examples, the estimation of a probable value of a parameter of interest may be a result of employing a method employing, for example, a computational and / or statistical module.

[0113] In some examples, based on detected EM reflections, an (e.g., updated) calibration of the transmitted electromagnetic (EM) radiation may be applied, for synchronizing EM radiation to be transmitted based on the analysis of the reflected EM radiation harmonic characteristics, e.g., after virtually subtracting the EM radiation reflections having the greatest intensity.

[0114] In some examples, the cognitive radar scheme may be employed (e.g., continuously) until all identified EM radiation reflections have detectible and identifiable harmonic characteristics corresponding to the respective intensity.

[0115] In some embodiments, the disclosed cognitive radar scheme may be configured for identifying reflections having the greatest intensity, e.g., in sequentially decreasing order, which may allow for further identifying and / or classifying a rotating device.

[0116] In some embodiments, the method may comprise the following steps, separately or in any combination:

[0117] transmitting a first electromagnetic (EM) radiation with a first distinct EM radiation characteristic into free space towards a scene;

[0118] receiving, from the scene, EM radiation reflections that are reflected from at least one rotating device component of at least one object that is present in the scene;

[0119] processing the reflections to detect, based on harmonic characteristics of the reflected EM radiation, the presence of the at least one object in the scene;

[0120] processing the reflections to characterize, based on the harmonic characteristics of the reflected EM radiation, the type of the at least one detected object;

[0121] determining the harmonic characteristics of the at least one rotating device based on the harmonic characteristics of the reflected EM radiation, e.g., micro doppler signature;

[0122] comparing the harmonic characteristics of the at least one rotating device and / or the harmonic characteristics of the reflected EM radiation, e.g., micro doppler signature to previous EM radiation reflections;

[0123] storing in memory the harmonic characteristics of the reflected EM radiation, e.g., micro doppler signature, and / or the harmonic characteristics of the at least one rotating device; and / or

[0124] modulating the transmitted electromagnetic (EM) radiation with a distinct EM radiation characteristic to be synchronized with the harmonic characteristics of the at least one rotating device.

[0125] In some embodiments, the expression “based on harmonic characteristics” may refer to considering harmonic characteristics of the rotating device, e.g., derived or obtained by employing a harmonic model for processing of the reflected EM radiation.

[0126] In some embodiments, the harmonic model may involve applying a harmonic filter, which sweeps through, e.g., continuously and / or actively scans, a range of frequency values with the purpose of detecting harmonics, e.g. base frequencies, for deriving information about the rotating device(s) of the object including, for example, the number of rotating devices of the object, and / or their rotational speed. Based on the number of detected rotational devices of an object, and / or their rotational speed, the object may be classified.

[0127] The following may be employed for the detection:

[0128] Assuming additive white Gaussian noise (AWGN): -i) Estimation (ML): Ω^ = arg m& *+ / -&"ax #$%'Ω(%'Ω() % 'Ω(.# ^ / = $%&- ii) Detection(.#≷33546

[0129] In someenumeration may be obtained, e.g., through sequential subtraction (via orthogonal projection) of previously estimated propellers.

[0130] Further based on the above, the rotating devices may be characterized, e.g., with respect to number of propellers, their rotation speed, and profile of the propellers, / ^. Characterization of the rotating device may allow object classification.

[0131] In some examples, Cognitive transmission may be employed, e.g., by adapting pulse repetition interval (PRI) and / or transmission timing, e.g., based on previous measurements, for instance, to increase or maximize EM reflections.

[0132] For example, the systems and / or methods described herein may determine the number of rotational devices of an object, and the associated rotational speed. In some embodiments, the system and / or method may include, based on the frequency sweep, determining a likelihood of the presence of a rotating device having a certain rotational speed. In some examples, a likelihood threshold may be employed. Rotational speeds that exceed the likelihood threshold may be identified as those being associated with the object. The likelihood threshold may be predetermined, or may be adaptively modified. In some examples, a maximum likelihood condition may be employed for identifying rotational speeds of rotating devices of an object. In some examples, the likelihood threshold may be a log-likelihood threshold. The term “threshold” may encompass the meaning of the term “threshold value”.

[0133] The various features and steps discussed above, as well as other known equivalents for each such feature or step, can be mixed and matched by one of ordinary skill in this art to perform methods in accordance with principles described herein.

[0134] In some embodiments, the scene may relate to real-world environments comprising one or more physical objects capable of reflecting EM radiation transmitted by the radar system’s transmitter toward the scene comprising the one or more objects.

[0135] In some embodiments, the scene may comprise objects which may be confused with the subject of interest, e.g., a vehicle having a rotating device, which may be referred to as “confusers”.

[0136] Aspects of the present disclosure may relate to identifying and / or classifying subjects of interest in comparison to common confusers for that subject, for example, including sub-signature differentiation of at least one object within a group of objects, and / or discrimination of at least two different vehicle types.

[0137] In some embodiments, sub-signature differentiation of at least one object within a group may relate, for example, to the identification and classification of an object having a unique sub-signature resulting from the reflected EM radiation, which may reside in proximity to a plurality of objects having a micro-Doppler signature distinguishable from the sub-signature. For example, identification and classification of a specific drone type within a swarm of drones.

[0138] In some embodiments, discrimination of at least two different vehicle types may relate, for example, to discriminating between at least two distinguishable micro-Doppler signatures. For example, discriminating between a bicopter, e.g., a dual-rotor drone, and a helicopter; discriminating between different types of objects based on the number of rotating devices employed by each object and / or based on the detected micro-Doppler signature reflected from the objects.

[0139] According to some embodiments, the system and / or method may employ algorithms for detection and / or classification of the rotating device and may, optionally, involve measuring micro- Doppler signatures of the rotating device.

[0140] In some embodiments, micro-Doppler signatures of rotating devices may be extracted by monostatic, bistatic, and / or multi-static radars.

[0141] In some embodiments, micro-Doppler signatures may be processed to estimate parameter values of the rotating device for the detection and / or classification.

[0142] In some embodiments, the processing algorithm may be configured to separate and disambiguate the observed micro-Doppler signatures, which may allow, for example, the following: detection, classification, sub-signature differentiation, tracking and / or discrimination, of at least two object types.

[0143] In some examples, the verbs “identifying”, “classifying”, “recognizing”, “characterizing” may herein be used interchangeably. The terms cited above denote also grammatical variations thereof.

[0144] In some examples, the verbs “identifying”, “detecting”, “spotting”,” sensing” may herein be used interchangeably. The terms cited above denote also grammatical variations thereof.

[0145] It is noted that the verb “identifying” may be user in both detection and classification meaning. This should by no means be construed in a limiting matter and should be interpreted within the context of the relevant paragraph in the present disclosure.

[0146] In some examples, the verbs “tracking”, “following”, “monitoring”, “observing” and “surveilling” may be used interchangeably. The terms cited above denote also grammatical variations thereof.

[0147] Reference is now made to the computational and / or statistical module implemented within a processing algorithm, which, according to some embodiments, may comprise parametric statistical models, non-parametric statistical models, clustering models, nearest neighbor models, and / or regression methods.

[0148] Moreover, the computational and / or statistical module may further comprise, for example, linear regression, logistic regression, and / or time series models, Bayesian models, and / or the like, may be therefore used to describe and / or express the relationships between rotating device characteristics, e.g., to understand and / or interpret the underlying structure of data accordingly and / or to make predictions about the corresponding vehicle classification.

[0149] In some embodiments, the method comprises steps relating to, for example, hypothesis testing, anomaly detection, and / or measuring differences between probability distributions. The method may for example comprise: Generalized Likelihood Ratio Test (GLRT), Kullback-Leibler Divergence (KLD), BayesianInformation Criterion (BIC), Akaike Information Criterion (AIC), Principal Component Analysis (PCA), Cross- Validation, and / or the like.

[0150] The term “models” may refer, but not limited, to a plurality of model types implementable by the radar system with the aim of, e.g., continuously, predicting a vehicle classification probability related to and / or based on detected and / or determined rotating device characteristics.

[0151] The term “engine” and / or “module” may be implemented one or more software and / or hardware components. A module may be a self-contained hardware and / or software component that interfaces with a larger system.

[0152] A module may comprise and / or implement a machine or machines executable instructions. A module may be embodied by a circuit and / or a controller programmed to cause the system to implement the method, process and / or operation as disclosed herein.

[0153] A memory may comprise one or more types of the following computer-readable storage media: transactional memory and / or long-term storage memory, which may facilitate and / or function as file storage, document storage, program storage, and / or as a working memory.

[0154] In some embodiments, the memory may be, for example, in the form of a static random-access memory (SRAM), dynamic random-access memory (DRAM), read-only memory (ROM), cache and / or flash memory.

[0155] In some embodiments, working memory may, for example, include, e.g., temporally based and / or non-temporally based instructions.

[0156] In some embodiments, long-term memory may, for example, include a volatile and / or non- volatile computer storage medium, a hard disk drive, a solid-state drive, a magnetic storage medium, a flash memory and / or other storage facility.

[0157] In some embodiments, hardware memory capability may, for example, store a fixed information set (e.g., software code), which may include, but not limited to, a file, program, application, source code, object code, data, and / or the like.

[0158] The terms “memory element”, “memory”, “data storage” may be used interchangeably.

[0159] The above examples of memory elements should by no means be construed in a limiting manner. Additional examples may be incorporated to facilitate memory capabilities of the system.

[0160] Reference is now made to the processing element, e.g., processor, according to some embodiments, at least one processing element may be implemented by various types of processor devices and / or processor architectures, which may comprise, for example, embedded processors, communicationprocessors, graphics processing unit (GPU), specialized accelerated computing processors, soft-core processors and / or general-purpose processors.

[0161] The terms “processing element”, “processor”, “analyzer” may be used interchangeably.

[0162] Moreover, the above examples of processing elements should by no means be construed in a limiting manner. Additional examples may be incorporated to facilitate processing capabilities of the system.

[0163] According to some embodiments, it will be appreciated that separate memory and / or processor may be allocated for each radar system element. In some embodiments, a same memory and / or processor may be allocated for two or more elements of the radar system.

[0164] However, merely for simplicity and without being construed in a limiting manner, the present disclosure may refer to a single memory and / or processor. In some embodiments, the memory and processor may embody a controller.

[0165] For example, although the processor may be implemented by several processors, the following disclosure will refer to processor as the component that conducts all the necessary processing functions of the radar system.

[0166] It should be noted that separate hardware components such as processors and / or memories may be allocated for each component and / or module in the radar system. For instance, separate processors and memories may be allocated to implement the transmitter, receiver and / or the controller.

[0167] The various components and / or modules of the radar system may communicate with each other over one or more communication buses (not shown), signal lines (not shown) and / or a network infrastructure (not shown). For example, the controller may be in communication with the transmitter.

[0168] The following description of radar system and method for detecting and / or classifying a vehicle in a scene and having a rotating device, may be given with reference to the example of a drone, with the understanding that such system and method are not limited to this example.

[0169] Merely for simplicity, without be construed as limiting, the following description may herein for instance generically refer to drone as outlined herein. Therefore, the disclosure is not intended to be limited by the specific disclosures of embodiments herein.

[0170] Reference is now made to Figure 1, a radar system, herein referenced by alphanumeric label “1000”, may comprise, for example, at least one memory 1100, at least one processor 1200, a controller 1300, an antenna arrangement 1400 having a transmitter 1410, and / or a receiver 1420. In some examples, memory 1100 and processor 1200 may embody or implement controller 1300, i.e., controller 1300 may not necessarily be a separate component.

[0171] In some embodiments, radar system 1000 may be configured to transmit EM radiation 7.by transmitter 1410 towards a scene, and to (e.g., responsively) receive EM radiation 8., from the scene by receiver 1420. EM radiation Rx may be reflected from one or more objects being present in the scene. The term “EM radiation Rx” as used herein may be referred to as “EM reflections Rx” or simply “EM reflections”.

[0172] In some embodiments, the transmitted EM radiation 7.may propagate into free space towards a scene 500 until being incident onto or until it intersects with a physical object in the scene.

[0173] For example, the transmitted EM radiation 7.may propagate into free space towards scene 500 until being incident with a drone 2000, which may reflect EM radiation Rx that may be detected by receiver 1420.

[0174] In some embodiments, drone 2000 may be a quadcopter comprising of a first 2001, second 2002, third 2003, and fourth 2004 rotating device.

[0175] In some examples, with respect to the top view illustration shown in Figure 1, the first 2001 and third 2003 rotating devices may rotate clockwise (herein defined as the positive rotation direction). Conversely, the second, 2002 and fourth 2004 rotating devices may rotate counterclockwise, herein defined as the negative rotation direction.

[0176] Further reference is now made to Figs. 2A-2C. Each rotating device may have its own angular velocity 9:about rotating axis ;:, and a corresponding local polar axis <:. Index j corresponds to the increased numbering of rotating devices 2001-2004, i.e., third rotating device 2003 has angular velocity9=, and corresponding angle of rotation, >?, where @ = A1,2,3,4D.

[0177] Considering the schematically illustrated example rotation directions, rotating device 2003 may have a positive radial velocity +F<and a negative radial velocity −F<at its edges, as seen from an observer viewpoint relative to world reference coordinate system Wxyz.

[0178] In some embodiments, a rotating device may have a length H, with a midpoint I, which may H divide the rotating device into at least two equal length segmentsJ.

[0179] When considering drone 2000 as the referencethe midpoint I of a rotating device maybe stationary, experiencing a radial velocity of KL = 0 at the center of rotation of the rotating device.

[0180] In some embodiments, rotating device 2003 may, for example, include a plurality of blades branching out of from the center of rotation, e.g. midpoint, I . The blades of the same rotating device may each have identical characteristics (e.g., with respect to geometry, weight, etc.). In some other examples, at least two blades of the same rotating device may have different characteristics.iii) In some embodiments, as schematically shown in Figure 2C. the rotating device 2003 may bevirtually subdivided 2103 into N infinitesimal portions (not shown) having a length of M? such that N =∑QPR+ ^P . Applicably, it may be noted that each infinitesimal portion may be associated with acorresponding angular velocity ω, radial velocity S<and / or tangential velocity ST. The term “virtual subdivision” may refer to a computer-generated division, which may serve the purpose of a computational and / or statistical module processing data related to EM radiation reflected back from each infinitesimal portion of a rotating device.

[0182] In some embodiments, the subdivision of the rotating device may be employed within the derivation of the harmonic model.

[0183] In some embodiments, the virtual subdivision may serve as a theoretical construction, which may be applied when deriving the harmonic model assumptions. Namely, when assuming harmonic behavior of the reflected EM radiation, a dependency on a harmonic coefficient and a base frequency may be required. Each segment obtained through the virtual subdivision may have a quarter wavelength of the transmitted EM radiation.

[0184] In some examples, harmonic coefficients may be descriptive of a computational construction accounting for EM radiation reflections intensity with the corresponding infinitesimal rotating portions of the virtually subdivided rotating device.

[0185] Reference is now made to Figure 3A. In some examples, a method for detecting and / or classifying objects 3000 may include any one of the following:

[0186] virtually subdividing a rotating device into a plurality of infinitesimal rotating portions each having a distinct angular velocity (block 3100);

[0187] associating EM radiation reflections with the corresponding infinitesimal rotating portions of the virtually subdivided rotating device (block 3200);

[0188] analyzing the EM radiation reflections (block 3300);

[0189] additively constructing, based on the processed reflected EM radiation patterns, a superposition of the EM reflections (block 3400);

[0190] determining, based on the superposition, a coherent harmonic EM radiation (block 3500).

[0191] In some embodiments, employing a computational and / or a statistical module may be based on processing an observation history and / or data gathered from external databases, which may comprise a plurality of EM radiation patterns associated with their corresponding type of rotating device.

[0192] Further reference is made to Figure 3B. In some examples, a method for detecting and / or classifying objects 3010 may include, additionally and / or alternatively, any one of the following:

[0193] virtually subdividing a rotating element into a plurality of infinitesimal rotating portions in the range of quarter wavelength, each having a distinct angular velocity, for computational modeling purposes (block 3110);

[0194] assigning a harmonic coefficient descriptive of computational construction associating EM radiation reflections intensity with the corresponding infinitesimal rotating portions of the virtually subdivided rotating device (block 3210);

[0195] assuming harmonic behavior of the reflected EM radiation, which is dependent on the harmonic coefficient and a base frequency (block 3310);

[0196] applying harmonic filtering by sweeping through a range of plausible base frequencies (block 3410);

[0197] determining, based on sweeping through a range of plausible base frequencies, dominant base frequencies (block 3510);

[0198] additively constructing, based on dominant base frequencies multiplication by a natural number, a superposition representation of the EM reflections, e.g., Fourier transformation (block 3610); and

[0199] determining, based on the superposition, a coherent harmonic EM radiation (block 3710);

[0200] the term “base frequency” and “harmonic” may be used interchangeably and may refer to “natural modes” of the micro-Doppler signature resulting peak intensity value of the reflected EM radiation having a greatest Log-Likelihood value in comparison to other frequencies.

[0201] The term “harmonic filtering” may refer to sweeping through a range of plausible base frequencies, which may facilitate continuous and / or active scan of frequencies measurements that may result in identifying base frequencies associated with a rotating device.

[0202] Appropriately, when a harmonic model is applied, the processing algorithm may execute harmonic filtering across a range of anticipated base frequencies, which may relate to observation history and / or acquired data from a database.

[0203] Reference is now made to Figure 4. In some examples, Radar system 1000 may be situated withrespect to a reference frame having a cartesian coordinate system with . − U − ; axis. As an exampleonly, the reference frame is herein referred to as a World reference frame described by cartesian coordinates Wxyz.

[0204] In some embodiments, radar system 1000 may transmit EM radiation 7.into free space towards the scene with the aim to be incident onto an object 2000 located in a scene 500, to obtain EM reflections 8., comprising a Doppler signature and / or micro-Doppler signature of the object, which the transmitted EM radiation 7.may have been reflected from.

[0205] In some embodiments, the object in scene 500 may be, for example, a quadcopter drone 2000. For simplicity and clarity of illustration, the transmitted EM radiation 7., may for example, be incident onto rotating device, e.g. rotor-blade, 2002, which may have an angular velocity 9Jabout rotating axis, e.g. pole, ;Jand a corresponding polar axis <J, in addition to angular displacement, e.g. angle of rotation, >J.

[0206] In some illustrative scenarios, drone 2000 may have, for example, linear velocity V S relative to World coordinates Wxyz causing a relative displacement ∆.. Furthermore, while the drone traverses in the scene, rotating device 2002 may experience angular displacement ∆X, in relation to the local polar coordinate descriptive of rotating device 2002.

[0207] In some embodiments, the temporospatial dynamics of rotating device 2002 may cause the reflected EM radiation 8.to “smear”, possibly causing variation in the intensity, e.g., variation in the radiation directivity and / or radiation efficiency, of reflected EM radiation 8.detected by receiver 1420.

[0208] Additional reference is now made to Figure 5A and Figure 5B. Rotating device 2003 having an angular velocity 9Ymay be represented by virtual subdivision into infinitesimal portions. Block 2203 schematically illustrates snapshots of the virtually subdivided rotating device at three instances or time stamps, indexed with a, b and c, separated by time tn, indexed 1, 2 ,3, respectively, where t1> t2>t3.

[0209] Virtually subdivided rotating device may arise, at the respective time stamps, corresponding EM reflection patterns. As shown schematically in Figure 5B, virtually subdivided rotating device 8Zmay arise reflected EM radiation pattern 8Z, TR[, virtually subdivided rotating device 8 / may arise reflected EM radiation pattern 8 / , TRJ, and virtually subdivided rotating device 8\may arise reflected EM radiation pattern 8\, TR=.

[0210] Merely for simplicity of illustration, reference may herein be made to rotating device, e.g., rotor blade, 2003 of drone 2000 when referring to a rotating device. This should not be construed in a limiting manner, and it may be appreciated that the present disclosure may be applicable to any additional rotating devices to be detected and / or classified by radar system 1000.

[0211] Further reference is made to Figs. 6A and 6B. Virtually subdivided rotating device at an initialtimestamp T = T? may be referenced by alphanumeric label 2303. At this instantaneous initial timestamp,the transmitted EM radiation 7.prior to being incident onto the object located in scene 500.

[0212] Subsequently, virtually subdivided rotating device at a later timestamp T > T? may be referencedby alphanumeric label 2403. At this instantaneous later timestamp, the transmitted EM radiation 7.is incident onto the rotating device, resulting in a reflected EM radiation 8..

[0213] For illustrative purposes, a dashed line ^_perpendicular to the defining line of the virtually subdivided rotating device 2403 indicates, at their intersection, the point of contact between the transmitted EM radiation 7.and the rotating device.

[0214] The transmitted EM radiation 7.be incident onto the virtually subdivided rotating device 2403 at an angle of incidence `?, which may result the reflected EM radiation Rxhaving an equivalent angle ofreflection `<, e.g. `? = `<.

[0215] The angle of reflection `<may also depend, for example, on surface roughness, resulting in the reflected EM radiation also having reflection angle that is different from the angle of incidence `?, `?≠ `<. Virtual lobes of EM radiation Rx reflected by each virtual segment are schematically referenced by designations 2700.

[0216] Reference is now made to Figure 7A. Virtually subdivided rotating device at an initial timestampT = T? may be referenced by alphanumeric label 2503. At this instantaneous initial timestamp, the EMradiation ∑bcR[ 7. may propagate through free space in scene 500 towards the object.

[0217] reference is made to Figure 7B. Virtually subdivided rotating device at a latertimestamp T > T? may be referenced by alphanumeric label 2603. At this instantaneous later timestamp,the transmitted EM radiation ∑bcR[ 7. was incident onto the rotating device, resulting in reflected EMradiation ∑bcR[ 8..

[0218] In some examples, each infinitesimal portion of the virtually subdivided rotating device may be associated with a dashed straight line descriptive of propagating wavefronts of EM radiation 7., and also with a corresponding EM radiation 8.responsively reflected from the infinitesimal portion, resulting in areflected EM radiation ∑bcR[ 8. pattern.

[0219] It should be noted that previously referenced transmitted EM radiation 7.and reflected EMradiation 8 are simplified representation of t ∑b. ransmitted EM radiation cR[ 7. , and reflected EMradiation ∑b 8 . For illustrative pur ∑bcR[ . poses, transmitted EM radiation cR[ 7. and reflected EMradiation ∑bcR[ 8. , accounts for EM propagation comprising of a plurality of wavefronts of anelectromagnetic (EM) field propagating through free space, contrary, to the simplified straight-line representation of transmitted EM radiation 7.and reflected EM radiation 8..

[0220] The term “electromagnetic radiation” and / or “EM” as used herein may refer to electromagnetic radiation of any suitable wavelength for the purposes of the applications disclosed herein.

[0221] Additionally, the “electromagnetic radiation” and / or “EM” may be coherent, non-coherent or partially coherent. The “electromagnetic radiation” and / or “EM” may be polarized, non-polarized or partially polarized. The “electromagnetic radiation” and / or “EM” may have a wide spectral width (e.g. of the range of hundreds of nanometers such as originated from a black body), the “electromagnetic radiation” and / or “EM” may have a mid-spectral width (e.g. of the range of tens of nanometers such as originated from a LED) or the light may have a narrow spectral width (e.g. of the range of a few nanometers such as originated from a laser).

[0222] The terms “radiated energy”, “beam”, “radar signal”, and “EM radiation”, “emitted EM radiation”, and corresponding grammatical variations thereof, may herein be used interchangeably.

[0223] Reference is now made to Figure 8A, for illustrative purposes only, a set of (e.g., feasible) reflected EM radiation lobes 8.reflected from an infinitesimal segment as a result from transmitted EM radiation incident onto the infinitesimal transmitted EM radiation may herein be referenced by alphanumeric label 2703. In some examples, the set of reflected EM radiation lobes may be generated due to diffraction. The radiation lobes of the respective EM reflections 8.of the set may have different angles of reflections `<.

[0224] A visual representation of main and side lobes provided. The intensity of the reflected radiation EM radiation 8.increases as the incident radiation EM radiation 7.becomes more perpendicular to the reflecting surface.

[0225] Therefore, causing the incident radiation EM radiation 7.to become more perpendicular to the reflecting surface, may enhance the detectability of the reflected EM radiation 8.by the radar system.

[0226] The term “beam pattern” and / or “beam formation” may refer to any parameters which may determine the spatial focus and / or efficiency of the energy distribution associated with the EM radiation. Beam pattern characteristics may be determined by various parameters such as, for example, steering angles, polarization, intensity, phase difference, and / or surface reflectance.

[0227] Further reference is made to Figure 8B. Graph 2803 schematically depicts an example of experimental data descriptive of the intensity EM radiation [dB] reflected from a rotating device over time [sec], for illustrative purposes only.

[0228] Furthermore, graph 2803 illustrates a series of peak intensities, denoted on the graph by a dot. In some embodiments, at least one peak intensity may be under a noise threshold value, which may render the peak intensity indistinguishable from surrounding observed intensities, if no additional and / or different processing of the observed intensities is applied.

[0229] A first maximal or peak intensity measurement of reflected EM radiation is measured at time stamp 1, 8dZ., [, and a second maximal or peak intensity measurement of reflected EM radiation ismeasured at time stamp 2, 8dZ., J. A reflection intensity may be determined as being “maximal” if exceeding a reflection intensity threshold. ∆T may define the time period between the appearance of two consecutive peaks exceeding, e.g., a reflection intensity threshold. In some examples, the reflection intensity threshold may be predetermined, or determined adaptively.

[0230] In some examples, for each time delta, ∆T , e.g., cycle time, separating, for example, time stamp 1 associated with 8dZ., [, and time stamp 2 associated with 8dZ., J, the rotating device may have completed a full revolution and / or a half-revolution, indicating an perpendicular intersection that transmitted EM radiation 7.was incident onto a rotating surface of the rotating device, causing (e.g., maximal) intensity measurement of reflected EM radiation.

[0231] In some examples, intensity observations made with time delta which may be equal to whole number of time cycle corresponding to natural number scalar multiplication with the time difference, ∆T, may result in an (e.g., maximal) intensity measurement of reflected EM radiation, 8dZ., T.

[0232] Conversely, intensity observations made in accordance to scalar multiplication of the time delta by a purely rational number, excluding whole number, may result a reduced RM radiation intensity, for example, intensity measurement at time stamp 1.75, 8e?Mf,[.gh, may result a reduction in about a half of the intensity when compared to maximal intensity measurements 8dZ., [and / or 8dZ., J.

[0233] Reference is now made to Figure 9A. Graph 4100 schematically depicts an example of experimental data descriptive of the intensity EM radiation [dB] reflected from a rotating device having four propellers over time [sec].

[0234] It should be noted that graph 4100 is brought forth for illustrative purposes only, additional experimental data may include maximum intensity values which may not be distinguishable from surrounding intensity values without applying a processing algorithm. Appropriately, maximum intensity values which may not be distinguishable from surrounding intensity values may be below a noise threshold value.

[0235] Reference is now made to Figure 9B, Graph 4200 schematically depicts an example of harmonic model algorithm output, which may be applied on experimental data depicted in graph 4100. The harmonic model processing may be graphed into Log-Likelihood over rotation speed [rpm] of a rotating device having four propellers.

[0236] It may be appreciated that graph 4200 may schematically depict with grater contrast the appearance of maximal values. Moreover, the processed output of the harmonic model may map the leading rotation speed detected in a scene.

[0237] Thus, the above may enable identifying and / or classifying the corresponding rotating device which may exert such behaviors, e.g. the number of identifiable leading rotational speed and / or their corresponding value.

[0238] Reference is now made to Figure 10A, Graph 4300 schematically depicts an example of conventional Doppler processing algorithm output, which may be applied on experimental data of a rotating device having four propellers, as depicted in graph 4100. The conventional Doppler processing output may be graphed into intensity over Doppler [KHz].

[0239] Reference is now made to Figure 10B, Graph 4400 schematically depicts an example of harmonic model algorithm output, which may be applied on experimental data depicted in graph 4100. The harmonic model processing may be graphed into Log-Likelihood over rotation speed [rpm] of a rotating device having four propellers, similar to Figure 9B.

[0240] It may be apricated the harmonic processing 4400 may schematically depict with grater contrast the appearance of maximal values in comparison to conventional Doppler processing 4300.

[0241] Reference is now made to Figure 11, an illustrative example of radar system 1000 method may comprise the following steps:

[0242] Transmitting a first electromagnetic (EM) radiation with a first distinct EM radiation characteristic into free space towards a scene (block 9100).

[0243] Receiving, from the scene, EM radiation reflections that are reflected from at least one rotating device component or element of at least one object that is present in the scene (block 9200).

[0244] Processing the reflections to detect, based on harmonic characteristics of the reflected EM radiation, the presence of the at least one object in the scene (block 9300).

[0245] Processing the reflections to characterize, based on the harmonic characteristics of the reflected EM radiation, the type of the at least one detected object (block 9400).

[0246] Determining the harmonic characteristics of the at least one rotating device based on the harmonic characteristics of the reflected EM radiation, e.g. micro doppler signature (block 9500).

[0247] Comparing the harmonic characteristics of the at least one rotating device and / or the harmonic characteristics of the reflected EM radiation, e.g. micro doppler signature to previous EM radiation reflections (block 9600).

[0248] Storing in memory the harmonic characteristics of the reflected EM radiation, e.g. micro doppler signature, and / or the harmonic characteristics of the at least one rotating device (block 9700).

[0249] modulating the transmitted electromagnetic (EM) radiation with a distinct EM radiation characteristic to synchronize with harmonic characteristics of the at least one rotating device (block 9800).

[0250] In some examples, the modulated transmitted EM radiation of block 9800 may be fed back to block 9100 to enable further iteration of the process, which may result in an increase in accuracy of detection and / or classification of the rotating device.

[0251] Reference is now made to Figure 12, flowchart descriptive of the cognitive radar scheme, e.g. block 10000, may comprise, for example, the following:

[0252] Adaptively synchronizing the emitted EM radiation based on the analysis of reflected EM radiation harmonic characteristics (block 10100).

[0253] In some embodiments, the adaptive synchronization may comprise for example:

[0254] Excitation of natural modes of a micro-Doppler signature of the rotating device and / or to maximize reflections and / or intensity from the at least one rotating device (block 10210).

[0255] Modulation of the transmitted EM radiation parameters, for example, EM radiation carrier frequency, pulse repetition interval (PRI), radiation transmission time, or any combination of the aforesaid (block 10220).

[0256] The above transmitted EM radiation parameters examples should by no means be construed in a limiting manner, additional parameters may be modulated to achieve adaptive synchronization of the transmitted EM radiation.

[0257] The terms referring to the transmitted EM radiation “parameters”, “factors” “characteristics”, “defining features”, “beam forming and / or beam pattern” may be used interchangeably.

[0258] In addition, the cognitive radar scheme, e.g. block 10000, may comprise, for example, the following additional successive steps:

[0259] processing of EM radiation reflections includes applying a computational and / or statistical module for determining the presence of the at least one rotary element in the scene (block 10300).

[0260] processing of the reflected EM radiation comprises identifying reflections having the greatest intensity (block 10400).

[0261] processing of the reflected EM radiation comprises virtually subtracting the reflection having the greatest intensity (block 10500).

[0262] In some examples, following the virtual removal, e.g. virtual subtracting, of the measured reflection having the greatest intensity (block 10500), it may be disregarded from further processing.Thus, the cognitive radar scheme may redirect to an additional iteration of adaptive synchronization of the transmitted EM radiation (block 10200).

[0263] In some embodiments, cognitive radar scheme 11000 may result in the identification of the reflections having the greatest intensity, in sequentially decreasing order. (block 10500).

[0264] Further reference is made to Figure 13. Radar system 1000 may include an antenna arrangement 1400 having a transmitter configured to generate output EM transmission and a receiver configured to receive input EM observation comprising reflected EM radiation 8..

[0265] In some embodiments, the output EM transmission comprising transmitted EM radiation 7.may be adaptively synchronized by modulating the transmitted EM parameters based on a harmonic model and / or a cognitive radar scheme.

[0266] In some embodiments, the modulation of the transmitted EM parameters may be operably manipulated by a controller, in accordance with received executable instructions stored in memory.

[0267] Moreover, the transmitted EM parameters may be directed to the processor to be incorporated within the computational and / or statistical module employed by the processing algorithm.

[0268] For example, the transmitted EM parameters may be directed to the processor, enabling the received signal, e.g. reflected EM radiation, to be evaluated in light of the transmitted EM parameters. Thus, enabling the desired adjustments of the parameters adapted for synchronization.

[0269] In some embodiments, the radar system may employ an adaptive technique, e.g. a Cognitive radar scheme, such that selected transmitted EM radiation parameters, for example, transmission rate and / or duration, may be determined and / or monitored for enhancing the detection performance. Thus, a Cognitive radar scheme may use previous measurements, e.g. observation history, stored in memory, and process the performance of the radar system, to control parameters of the signal to be transmitted in the next step.

[0270] In some embodiments, previous EM radiation reflections may be processed to determine previous reflection patterns, wherein the previous EM radiation reflections may be obtained in response to a preceding EM radiation transmission cycle and / or retrieved from a database.

[0271] In some embodiments, the radar system 1000 may output detection and / or classification of a vehicle having a rotating device, for example, a drone, in a scene.

[0272] Referring to Figure 14, a plurality of optional objects having a rotating device may be presented for illustrative purposes, the following may be detected and / or classified by the disclosed radar system:

[0273] micro-Doppler signature of a human arm and / or leg movement 14100, which may exhibit a partly rotating motion about the respective joints;

[0274] micro-Doppler signature of a bicycle wheel 14200;

[0275] micro-Doppler signature of a helicopter having at least one rotating rotor blade 14300;

[0276] micro-Doppler signature of a turbine having at least one rotating turbine blade, which may be a wind turbine blade 14400;

[0277] micro-Doppler signature of a swarm of drones 14500;

[0278] micro-Doppler signature of a submerged mobile platform having at least one rotating device, e.g. propelled submarine 14600.

[0279] In some examples, with further reference to arm and / or leg movement (e.g., walking, running) 14100, the radar system may be configured for detection and / or classification of any partly rotating limb of an animal, which may extend in meaning to flying and / or gliding animals, aquatic animals and / or land animals.

[0280] In some examples, with further reference to a wind turbine having at least one rotating turbine blade 14400, additional rotating turbine blade may be detected and classified by the radar system, for example, a jet-engine having rotating turbine blade.

[0281] In some examples, with further reference to a submerged mobile platform having at least one rotating device, e.g. propelled submarine 14600, the radar system may detect and / or classify at least one object in a scene that is being propelled by at least one rotating device submerged in water.

[0282] In some embodiments, the method for detecting and / or classifying at least one rotating device submerged in water may, for example, comprise the following action steps:

[0283] receiving, from the scene, acoustic waves generated due to the rotation of the rotating device in water; iv) processing the reflections to detect, based on harmonic characteristics of the acoustic waves, the presence of the at least one object in the scene.

[0284] The above examples are brought forth for illustrative purposes only, and by no means should they be construed in a limiting manner. Additional examples may be applicable for the detection and / or classification by the disclosed radar system.

[0285] Although the disclosure has been provided in the context of certain embodiments and examples, it will be understood by those skilled in the art that the disclosure extends beyond the specificallydescribed embodiments to other alternative embodiments and / or uses and obvious modifications and equivalents thereof.

[0286] In some embodiments, the radar system may be configured to differentiate between different objects of a group (e.g., drone swarm). In some examples, the disclosed radar system may be operably employed to differentiate between a subset of objects of a group of objects. In some examples, the radar system may be configured to differentiate not only between objects of different types (e.g., drone vs propelled paraglider), but also between different objects of the same type (e.g., distinguish between various types of quadcopter drones). In some examples, the radar system may be configured to distinguish between the various objects of the same type in swarm.

[0287] Further reference is made to Figure 15. A method for detecting and / or classifying an (e.g., moving) object in a scene comprises, in some embodiments, transmitting and / or emitting electromagnetic radiation into free space (block 15100).

[0288] In some embodiments, the method comprises receiving, (e.g., responsive to the transmission of the EM radiation), from the scene, EM radiation reflections that are reflected from at least one rotating device of at least one object that is present in the scene (block 15200).

[0289] In some embodiments, the method comprises processing (also: analyzing) the EM radiation reflections to detect, based on harmonic characteristics of the reflected EM radiation, the presence of the at least one object in the scene (block 15300).

[0290] In some embodiments, the method comprises processing (also: analyzing) the EM radiation reflections to characterize the at least one object in the scene.

[0291] In some embodiments, the method comprises processing (also: analyzing) the EM radiation reflections to characterize the at least one rotating device of the object.

[0292] In some embodiments, the method comprises processing (also: analyzing) the EM radiation reflections to characterize the object, based on the characterization of the at least one rotating device.

[0293] In some embodiments, the method comprises processing (also: analyzing) the EM radiation reflections to determine the number of rotating devices of the object.

[0294] In some embodiments, the method comprises detecting the presence of an object in the scene, based on the detection of a rotating device in the scene.

[0295] In some embodiments, the determining, analyzing and / or processing may employ a harmonic model, and / or assume harmonic behavior of a rotating device present in the scene.

[0296] The above example of radar system implementations should by no means be construed in a limiting manner, additional implementations may be achieved through the employment of the radar system as a whole, and / or by employing the harmonic model, and / or the disclosed cognitive radar scheme comprising applying the harmonic model within the disclosed scheme.

[0297] It is noted that embodiments of the radar system may be configured to implement and / or perform any one of the steps and / or processes described herein, alone or any suitable combination and / or order.

[0298] It is noted that steps and / or processes described herein may be performed by any embodiment and / or example of the system described herein.

[0299] According to some embodiments, some implementations and / or portions and / or processes and / or elements and / or functions of radar system may be implemented by the different system constitutes. For example, in the context of detecting and / or classifying a submerged mobile platform, the antenna arrangement may be substituted with an acoustic wave generator.

[0300] It is noted that aspects of the present disclosure pertain to a radar system configured for implementing a method for deriving harmonic model and / or the executing a cognitive radar scheme which comprises applying the harmonic model within the disclosed scheme.

[0301] As mentioned herein, drone detection by radars is a challenging problem due to their small radar- cross section.

[0302] Further examples:

[0303] This problem was addressed in two levels. The first was derivation of detection methods based on parameterized harmonic micro-Doppler signature of the drones. The generalized likelihood ratio test (GLRT) was derived for target detection based on this model.

[0304] For this purpose, the number of propellers and their parameters, such as length and rotation speed, were estimated. These parameters can be used for drone classification. A minimum description length (MDL) approach was derived to estimate the number of propellers / drones.

[0305] The proposed method substitutes the traditional Doppler processing with a new processing. The computational complexity of the proposed approach is similar to the conventional Doppler processing.

[0306] The second level of this contribution is a cognitive approach where the transmit waveform (pulse repetition time, and transmission time) was sequentially modified based on the observations history in order to optimize the detection performance.

[0307] The transmit signal excites the natural modes of the micro-Doppler signature. The proposed method can deal with multiple drones with arbitrary and unknown number of propellers.

[0308] Index Terms—Unmanned aerial vehicle (UAV), Drone detection, micro-Doppler, cognitive radar (CR), Kullback-Leibler divergence (KLD), generalized likelihood ratio test (GLRT).

[0309] I. INTRODUCTION

[0310] Unmanned aerial vehicles (UAVs), commonly referred to as drones, are becoming increasingly ubiquitous. They have many benefits in commercial and military applications, but at the same time they significantly threaten public safety.

[0311] Thus, detection and localization of drones have become an important threat to public safety and challenge the existing air defense systems. Detection of drones by radar systems is a difficult task due to their low radar cross section (RCS). Thus, for drone detection by radar systems, high power transmission is required.

[0312] This solution is not acceptable due to RF environmental pollution. In addition, the cost of high- power radar systems does is higher and results in large cost systems, precluding massive deployment of radar systems. In congested urban environments, for safety reasons, it is important to develop methods for early drone detection. Thus, development of low-cost and low-power radar systems for early drone warning is of great importance.

[0313] Several works have investigated the RCS time-variations of drones and their rotor blades (see e.g. [1], [2]). Due to the Doppler effect, radar signal frequency is modulated when reflected from a moving reflector. The micro-Doppler frequency modulation is commonly used to classify moving targets [3], [4]. In [5] time-varying Doppler from rotating objects was analyzed. The rotating blades of drones modulate the Doppler frequency induced by their motion.

[0314] Thus, drones have a unique feature of periodic time-varying Doppler frequency modulations, i.e., micro-Doppler signature [6]. The micro-Doppler contributions contain additional information for the target classification.

[0315] The drone’s radar echo is periodic time-varying, and it can be modeled as a harmonic signal. The detection and parameter estimation problems of a harmonic model were derived in [7], [8] in the context of speech and audio signal processing. In [7] a maximum-likelihood (ML) estimator for a harmonic model parameter was derived. In [8] a generalized likelihood ratio test (GLRT) for harmonic signal detection was derived.

[0316] The idea of cognitive radar (CR) was proposed in [9] and has been investigated in various approaches (see e.g.

[0010] ,

[0011] ,

[0012] ). A cognitive radar system adapts it to transmitted signal based onthe available information from history observations, external databases, and task priorities. Thus, a cognitive radar employs a performance criterion, which takes into account the information collected in the previous observations on the environment and targets of interest. In

[0010] an algorithm for optimal waveform design for CR based on maximizing the output signal-to-noise ratio (SNR) and the mutual information between the target ensemble and observations, was derived.

[0317] In

[0011] two different waveform design techniques based on sequential hypothesis testing for active sensors operating in a target recognition application were derived. Adaptive design of waveforms has been applied for target detection and estimation applications, for example in

[0013] ,

[0014] . In

[0013] a general cognitive radar framework for a system engaged in target tracking was developed. In

[0014] the range and velocity estimation problem for multiple extended targets was investigated.

[0318] Embodiments pertain to a cognitive radar configuration, which exploits the drone signature characteristics in order to significantly improve the detection performance in low SNRs. First, a harmonic model for drone detection is derived and a GLRT for target detection is proposed.

[0319] Then, a cognitive approach in which the pulse / chirp repetition internal (PRI) and transmit time are adaptively modified based on previous observations is derived in order to optimize the detection performance. The resulting transmit signal excites the natural modes of the micro-Doppler signature of the observed target. The resonated target signature increases the SNR.

[0320] In Section II, a system model is described, and the problem is formulated. In Section III, an object detection algorithm is derived using its spectral-domain signature. In Section IV, a cognitive approach is derived, in which the transmit signal is adaptively synchronized based on previous observations.

[0321] In Section V, the performance of the proposed techniques are evaluated and compared to other known methods for the problem of object detection in low SNRs. Conclusions appear in Section VI.

[0322] II. OBJECT (E.G., DRONE) DETECTION USING HARMONIC MODEL

[0323] In this section, the echo signal model was derived for rotor blades and develop a GLRT-based signal detection algorithm.

[0324] A. Harmonic Data Model

[0325] Reference is made to Figure A1. Consider a rotor blade of length i, which is directed to angle j'k( as depicted in Figure A1. The rotor blade is illuminated by the radar signal ^'k(^l-mnop, where ^'k( is the transmit signal baseband and qris the carrier frequency. In order to model the echo signal from the blade, it was divided into infinitesimal segments of size ^s and compute the total echo signal as a superposition of echoes from each segment. It is assumed that along each segment, the delay is approximately constant and each segment is considered as a point reflector.

[0326] The echo signal from segment η along the blade, after down-conversion to baseband is given by (1)

[0327] where t's( is the complex amplitude of the reflected echo at segment s and u's, j( is the time-delay of the two-way propagation path between the radar and the segment. Assuming far-field assumption, it can be verified that the delay is given by (2)

[0328] where u^is the two- blade center, and ^ is thepropagation speed. Assuming that the signal bandwidth, v , is small, such that v w i / ^ ≪ 1, then(3)

[0329] Substitution of (3) into (1), integration over the rotor blade yield, the baseband data model can be stated as (4 )

[0330] where y'k( is the additive noise process, ^ is the observation time or PRI, and z 'j( is beam pattern of the rotor blade, defined as (5 )

[0331] where { ^ ^ / qr is the wavelength.

[0332] For a rotating blade with angular frequency, Ω^, the angle, j, linearly depends on time (6 )

[0333] For simplicity of the derivations, it is assumed that z 'j'k(( is constant for k ∈ ^0, ^^. Thisassumption is satisfied if the blade rotation angle during the PRI is much smaller than the beam width, i.e.2^ / 'qri( ≪ Ω^^.

[0334] It can be verified that in most practical scenarios, this assumption is valid. This assumption allows employing a conventional matched filter (^}) without taking into account the variation of the beam pattern.

[0335] In the following, it was considered the data model for each PRI after ^} for a single range bin.Let ~^ and β^ ^ j'0( E ^Ω^^ denote the ^} output and the blade angle at the ^p^ PRI. Then, the datamodel for the considered range bin is given by (7 )

[0336] where ^^^ is the ^} output for the signal ^ 'k G u^(. This model can be used also for range bins,with no target, in which ^^^= 0.

[0337] From (5) it can be observed that for linearly varying blade angle, j^, the pattern is periodic in time with period of the blade rotation, 2^ / Ω^. Thus, a Fourier series can be used for representation of the signal component in (7). (8 )

[0338] The recorded signal can be expressed by the above Fourier series, which is a harmonic model, whose fundamental frequency is given by the angular frequency of the blade. Using the Fourier expansion in (8), the data model in (7) can be rewritten in matrix-vector notation as (9 )

[0339] where the elements of ^'Ω^( ∈ ℂQ^'-^^+(and / ∈ ℂ-^^+ are given by ^^'Ω^(^nq = ^lΩ4^^^, and^ / ^^ ^ ^^,^ ∈ ^1, ^^, ^ ∈ ^G2^, 2^^.

[0340] The noise vector, S, is assumed to be zero-mean, circularly symmetric complex Gaussian with known covariance matrix, 8S. With no loss of generality, it can be assumed that the covariance matrix ofthe noise is diagonal, 8^ ^ ^^-^, since the signal can be pre-whitened.

[0341] The unknown parameters in the model described in (9), are the fundamental frequency, Ω^, and the vector of harmonics, / .

[0342] The number of harmonics, ^, is assumed to be known. Generally, it can be determined using model order selection methods.

[0343] Alternatively, it can be determined using experimental data for various types of drones and carrier frequencies. Our goal here is to estimate the fundamental frequency, Ω^. The vector of harmonics, / , is a nuisance parameter which is also required to be estimated.

[0344] III. DRONE DETECTION ALGORITHM

[0345] In the following, a test is presented based on GLRT for target detection under the model in (9). A single blade target is first considered and then extend it to multiple targets.

[0346] A. Single Blade

[0347] Under the assumptions stated above, the ML estimator of 'Ω^, / (is given by maximizing the log-likelihood function of the unknown parameters using the data vector .: (10 )

[0348] Where i.'w,w( is the log-likelihood function defined as (11 )

[0349] and log denotes the natural logarithm function. By maximizing the log-likelihood function in (11) with respect to / , one obtains (12 )

[0350] Accordingly, the log-likelihood function can be rewritten as(13 )

[0351] where I%'Ω^( ≜ %'Ω^(0%3'Ω^(%'Ω^(1*+ %'Ω^( is the projection matrix into the subspace spanned by the columns of %'Ω^the ^i estimator of the fundamental angular frequencyfor known noisei^'Ω^, / (, that(14 )

[0352] The estimator described(15 )

[0353] The first hypothesis, H ,harmonic with additive noise. Hypothesis H0 corresponds to the case in which the signal contains noise only.

[0354] The GLRT for decision between the two hypotheses stated above is: (16 )

[0355] The log-likelihood function under hypothesis H1 is given by maximization of: (17 )

[0356] With respect of Ω^: i1 ^ ^^~Ω4i^'Ω^, ^ / ( ^ i^'^ Ω ^, ^ / (. The log-likelihood function underhypothesis H0 is given by: (18 )

[0357] For deriving the GLRT,(19 )

[0358] and the GLRT is expressed as: (20 )

[0359] B. Multiple Blades

[0360] In the hypothesis of multiple blades, the model in (9) is now composed of the superposition of ^ harmonic sources (^ blades) with different fundamental frequencies AΩ^D ^ ^^ 1(21 )

[0361] Estimation of Ω^ ^ 'Ω+, ... , Ω^ (^ will be produced in ^ steps. At each step a differentfundamental frequency Ω^is estimated, and the vector of harmonics, / ^, which have the maximum energy, by using the ^i estimator for a single blade, derived in III-A. This frequency is expected to be the resonance frequency of the ^p^blade. Then, the harmonic source obtained by Ω^is ”peeled” from the signal by projecting it onto the complementary subspace of the column-space of %'Ω^(, as follow: (22 )

[0362] where z⊥^^0 Ω^1 is the projection matrix onto the complementary subspace of the column-spaceofsignal at the ^p^step. Therefore, the signal after ^ steps will obtain: (23 )

[0363] At each step perform a GLRT is performed on x(k), as described in (20), to determine whether there are more harmonic sources to detect. After ^ steps the following is obtained: (24 )

[0364] The selected model order is therefore the number of steps performed until the GLRT decision is hypothesis H0.

[0365] IV. COGNITIVE APPROACH

[0366] In this section, an adaptive technique was derived such that at each step the transmission rate and time are determined to optimize the detection performance. Cognitive systems use the previous measurements history to optimize the performance of the radar system with respect to the parameters of the signal to be transmitted in the next step.

[0367] A. Modeling of the cognitive scheme

[0368] The model for the data received at step k can be stated using the harmonic model (25 )

[0369] Where the nthsample of xk is obtained by (26 )

[0370] Let define thein theparameters of the transmit signal at the ^p^ step, denoted by ^^ ^ ^^^^ , k^^^, given observations inprevious steps (history), denoted by .'^*+( ^ '. ^+ , … , .^*+^(^. For this detection problem we are defining (27 )

[0371] B. Criterion for detection performance optimization

[0372] Reference is now made to Figure A2, which describes the considered cognitive scheme. This problem can be formulated as follows (28 )

[0373] where ¡ 'w ,w ( denotes the objective or utility function, de-fined on the real numbers. In this paper, the utility function (optimization criterion) considered is the Kullback-Leibler divergence (KLD), which measures the divergence of the pdf under hypothesis H0from the pdf under hypothesis H1: (29 )

[0374] However, the pdf under hypothesis H1 involves integration of the joint pdf of ~^'^(, ^, withrespect to (w.r.t.) ^, which is a computationally expensive task. In the proposed approach, we compute the mean of KLD using the conditional pdf w.r.t ^. This term will be denoted by mean KLD (MKLD) of q^'¢(0~'^(£¤^1from q^'¢(0~'^(£¤+, ^ 1, where the mean is taken w.r.t. the conditional pdf of ^|.'^*+(,which is the posterior distribution for step ^ G 1 and serves as prior distribution for step ^. Thus, theMKLD is computed by (30 )

[0375] The last equality in (30) stems from the fact that given θ, the vectors xk and x(k−1)are statistically independent. The term ^'.'^*+((in the right-hand side of (30) is independent of the transmitted signal parameters at the ^p^step. Due to the Gaussian distribution of the data vector, xk, under both hypotheses with identical covariance matrices, and using the model described in (25), the first term in the right-hand side of (30) is given by (31 )

[0376] marking ¦'Ω^, ^^( ^ % 3^ 'Ω^, ^^(^^'Ω^, ^^( and using the law of total expectation, thefollowing is(32 )

[0377] Now the inner expectation can be calculated numerically: (33 )

[0378] In Appendix A it is shown that simplifying equation (33) in respect to the model obtained the following expression (34 )

[0379] Thus, the MKLD in (30) can be written as: (35 )

[0380] In Appendix B it is shown that after simplifying the posterior pdf q0Ω^£.'§*+(1 in respect to the model, the following expression is obtained: (36 )

[0381] While z ⊥'§*+(is the projection matrix onto the complementary subspace of the column-space ^ of ^'§*+(. Finally, the optimization is as follows:(37 )

[0382] C. Blade

[0383] In the hypothesis of multiple blades, the model is composed of the superposition of ^ harmonic sources with different fundamental frequencies. At the ^p^step the signal is: (38 )

[0384] At each step athe resonance frequency of the blades. At each step the ^i estimator is used for a single target, to estimate only one target’s parameters, and after tracking it for a few steps, this target is subtracted from the observation vector, until the signal remains with noise only: (39 )

[0385] Then, at the ^ E 1 step we have:(40 )

[0386] where is the projection matrix onto the complementary subspace of the column-after ^ ^ ^ E 1 steps we have(41 )

[0387] The selected model order is therefore the number of steps until the GLRT decision is hypothesis H0.

[0388] D. Drone Classification

[0389] Drones can be classified based on the number of propellers and the radar signature of each propeller. The model described above depends on the propellers’ signature through the vector / . v) Therefore, both the cognitive and non-cognitive approaches proposed in this paper are helpful for drone classification. For this purpose, the classes of drones should be defined and characterized.

[0390] V. SIMULATIONS

[0391] In this section, the performance of the proposed methods via several examples and demonstrate their advantages is evaluated compared to the conventional methods which use a bank of Doppler filters. The Doppler detector is given by the test (42 )

[0392] In the simulations, carrierbeampattern is given by the constant t ^ 1. The probability of false alarm is PFA = 0.01 and 1000 Monte Carlosimulations were performed.

[0393] The GLRT threshold is defined by setting 1000 Monte Carlo simulations of hypothesis H0and determining it by the false alarm probability. For example, for PFA = 0.01, the threshold is the value between the tenth and eleventh tests (sorted in descending order), such that one percent out of 1000 Monte Carlo simulations yield a false alarm. vi) A. Harmonic method

[0394] In this subsection, the proposed harmonic method was compared to conventional Dopplerprocessing. In the simulations, it was assumed that the blades are rotating in ^ ^ 4 unknownfundamental frequencies Ω^ ^ '87, 93, 101, 112( ¤ª, with various SNRs, using PRI ^^ ^ 0.24 ^^^^.

[0395] Furtheris made to Fig. A3. Fig. A3 compares the probability of detection of the different methods as a function of SNR, using Ts = 0.24 msec.

[0396] It can be seen that the proposed harmonic method provides better detection performance compared to the conventional Doppler processing. This is due to the fact it takes in account prior knowledge on the model.

[0397] B. Cognitive method

[0398] Additional reference is made to Figs. A4 and A5.

[0399] Fig. A4 compares the probability of detection of the different methods as a function of SNR, usingTs = 0.48 msec. Fig. A5 compares the detection probability versus number of steps, with ^^^ ^ 8 ^^,using ^^ ^ 0.48 ^^^^.

[0400] In this subsection, compare the proposed cognitive method to the harmonic and Dopplermethods. In the simulations, we assume that the blades are rotating in ^ ^ 4 unknown fundamentalfrequencies Ω^ ^ '87, 93, 101, 112( ¤ª, with various SNRs and in various number of steps, using a slowsampling time ^^ ^ 0.48 ^^^^. Figs. A4 and A5 compare the probability of detection of the differentmethods function of SNR and as a function of number of steps, respectively. It can be seen that the proposed cognitive method provides better detection performance compared to the harmonic and Doppler methods.

[0401] C. Blade Enumeration

[0402] Additional reference is made to Fig. A6. In this subsection, the proposed blade enumerationmethod is presented, using the cognitive approach. In Fig. A6, the blades are rotating in ^ ^ 4 unknownfundamental frequencies Ω^ ^ '87.5, 90.5, 93, 99( ¤ª, with ^^^ ^ 18 ^^. The figure shows theoptimized sampled versus time (first and third rows) and the posterior pdfs

[0403] q'Ω^|~(versus Ω^(second and fourth rows), as a function of pulse index, using the KLD optimization. It can be seen that in every three pulses (consider it as a ’step’), the algorithm is locked on one fundamental frequency, and afterwards there is a null at this frequency, and at the next ’step’ the algorithm is locked on another frequency. After M = 4 steps all the ^ frequencies are null, which impliesthe detection decision in the fifth step will be hypothesis H0, and therefore ^^ ^ 4.

[0404] Figs. A7 and A8 compare the probability of correct classification and correct estimation, respectively, in different false alarm probabilities as a function of SNR.

[0405] Table I shows the misclassification matrix, using ^^^ ^ 17 ^^ and PFA = 0.01. Each cell in thematrix ^N«, ¤@^, 0 « 6, 0 @ 4 represents the probability for decision NP under the hypothesis¤l. VI. CONCLUSION

[0407] Embodiments relate to a new model-based techniques for drone detection based on micro- Doppler signature. In this approach, the transmit signal excites the natural modes of the micro-Doppler signature of the observed target.

[0408] A new cognitive approach was derived in which the transmit signal is adaptively synchronized based on previous observations. Instead of transmission of constant time rates, in the proposed techniques, the sampling time is determined at each step, in order to minimize the MKLD for system parameters estimation w.r.t. the transmit time rates.

[0409] The proposed techniques were tested via simulations for adaptive transmit sampling time in the presence of single and multiple targets with a very weak SNR. The simulations show that the proposed techniques enable a significantly higher rate of detection probability, compared to other techniques.

[0410] APPENDIX A

[0411] DERIVATION OF EQUATION (33)

[0412] Since b is unknown, we will assume (43 )

[0413] and since (44 )

[0414] Reference is now made to Fig. A6, showing optimal sampled signal versus time (first and thirdrows) and posterior pdfs versus Ω0 (second and fourth rows) using KLD optimization, assuming ^ ^ 4unknown fundamental frequencies Ω^ ^ ^87.590.59399^.

[0415] we claim b and x(k−1)are multivariate normal distributed with (45 )

[0416] and therefor the conditional distribution is multivariate normal distributed, , where (46 ) (47 )

[0417] and define the singular value decomposition (SVD) of A(k−1)as follow (48 )

[0418] Based on (49 )(50 ) (51 )(52 )

[0420] Appendx B

[0421] DERIVATION OF f(Ω0|x(k−1)) (53 ) (54 )(55 )(56 ) (57 ) (58 )(59 ) (60 )

[0422] Aspects of the present disclosure may involve, in some embodiments, any digital computer system may be configured or otherwise programmed to implement a method disclosed herein, and to the extent that a particular digital computer system is configured to implement such a method, it is within the scope and spirit of the disclosure.

[0423] Once a digital computer system is programmed to perform particular functions pursuant to computer readable and executable instructions from program software that implements a method disclosed herein, it in effect becomes a special purpose computer particular to an embodiment of the method disclosed herein.

[0424] The techniques necessary to achieve this are well known to those skilled in the art and thus are not further described herein.

[0425] The methods and / or processes disclosed herein may be implemented as a computer program or computer program product tangibly embodied in an information carrier, for example, in a non-transitory tangible computer-readable or non-transitory tangible machine-readable storage device and / or in a propagating signal, for execution by or to control the operation of, a data processing apparatus including, for example, one or more programmable processors and / or one or more computers.

[0426] The terms “non-transitory computer-readable storage device” and “non-transitory machine- readable storage device” encompasses distribution media, intermediate storage media, execution memory of a computer, and any other medium or device capable of storing for later reading by a computer program implementing embodiments of a method disclosed herein.

[0427] A computer program product can be deployed to be executed on one computer or on multiple computers at one site or distributed across multiple sites and interconnected by one or more communication networks.

[0428] A computer readable signal medium may include a propagating data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagating signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof.

[0429] A computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

[0430] These computer readable and executable instructions may be provided to a processor of a general-purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0431] These computer readable and executable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and / or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function / act specified in the flowchart and / or block diagram block or blocks.

[0432] It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer readable program instructions .

[0433] The computer readable and executable instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0434] In the discussion, unless otherwise stated, adjectives such as “substantially” and “about” that modify a condition or relationship characteristic of a feature or features of an embodiment of the presentdisclosure, are to be understood to mean that the condition or characteristic is defined to within tolerances that are acceptable for operation of the embodiment for an application for which it is intended.

[0435] Unless otherwise specified, the terms 'about' and / or 'close' with respect to a magnitude or a numerical value may imply to be within an inclusive range of -10% to +10% of the respective magnitude or value.

[0436] Unless otherwise specified, the terms 'about' or 'close' imply at or in a region of, or close to a location or a part of an object relative to other parts or regions of the object.

[0437] The terms "substantially," "substantial," and the like refer to a considerable degree or extent. When used in conjunction with an event or circumstance, the terms can refer to instances in which the event or circumstance occurs precisely as well as instances in which the event or circumstance occurs to a close approximation, such as accounting for typical tolerance levels or variability of the embodiments described herein.

[0438] Positional terms such as "upper", "lower" "right", "left", "bottom", "below", "lowered", "low", "top", "above", "elevated", "high", "vertical" and "horizontal" as well as grammatical variations thereof as may be used herein do not necessarily indicate that, for example, a "bottom" component is below a "top" component, or that a component that is "below" is indeed "below" another component or that a component that is "above" is indeed "above" another component as such directions, components or both may be flipped, rotated, moved in space, placed in a diagonal orientation or position, placed horizontally or vertically, or similarly modified.

[0439] Accordingly, it will be appreciated that the terms "bottom", "below", "top" and "above" may be used herein for exemplary purposes only, to illustrate the relative positioning or placement of certain components, to indicate a first and a second component or to do both.

[0440] It is important to note that the method may include is not limited to those diagrams or to the corresponding descriptions. For example, the method may include additional or even fewer processes or operations in comparison to what is described herein.

[0441] In addition, embodiments of the method are not necessarily limited to the chronological order as illustrated and described herein.

[0442] It should be noted that the term "maximal contrast value" as used herein refers to a maximal attainable value of the contrast of transmitted electromagnetic radiation, measured at a given location by a detector, in consideration of constraints dictated by various parameters, including for example operation criteria and / or configuration of a corresponding authentication system and / or structure of an examined product unit.

[0443] Accordingly, the expressions "maximal" as used herein refer to a relative maximal attainable contrast value and not to an absolute maximal contrast value.

[0444] Discussions herein utilizing terms such as, for example, "processing", "computing", "calculating", "determining", "establishing", "analyzing", "checking", “estimating”, “deriving”, “selecting”, “inferring”, “recording”, “updating” and / or the like, may refer to operation(s) and / or process(es) of a computer, a computing platform, a computing system, or other electronic computing device, that manipulate and / or transform data represented as physical (e.g., electronic) quantities within the computer's registers and / or memories into other data similarly represented as physical quantities within the computer's registers and / or memories or other information storage medium that may store instructions to perform operations and / or processes.

[0445] The term determining may also refer to “heuristically determining”.

[0446] It should be noted that where an embodiment refers to a condition of "above a threshold", this should not be construed as excluding an embodiment referring to a condition of "equal or above a threshold".

[0447] Analogously, where an embodiment refers to a condition “below a threshold”, this should not be construed as excluding an embodiment referring to a condition “equal or below a threshold”.

[0448] It is clear that should a condition be interpreted as being fulfilled if the value of a given parameter is above a threshold, then the same condition is considered as not being fulfilled if the value of the given parameter is equal or below the given threshold.

[0449] Conversely, should a condition be interpreted as being fulfilled if the value of a given parameter is equal or above a threshold, then the same condition is considered as not being fulfilled if the value of the given parameter is below (and only below) the given threshold.

[0450] Additionally, terms used in the singular shall also include the plural, except where expressly or otherwise stated or where the context otherwise requires.

[0451] In the description and claims of the present application, each of the verbs, "comprise" "include" and "have", and conjugates thereof, are used to indicate that the object or objects of the verb are not necessarily a complete listing of components, elements or parts of the subject or subjects of the verb.

[0452] As used herein the term "configuring" and / or 'adapting' for an objective, or a variation thereof, implies using materials and / or components in a manner designed for and / or implemented and / or operable or operative to achieve the objective.

[0453] Unless otherwise stated or applicable, the use of the expression “and / or” between the last two members of a list of options for selection indicates that a selection of one or more of the listed options isappropriate and may be made, and may be used interchangeably with the expressions “at least one of the following”, “any one of the following” or “one or more of the following”, followed by a listing of the various options.

[0454] As used herein, the phrase “A, B, C, or any combination of the aforesaid” should be interpreted as meaning all of the following: (i) A or B or C or any combination of A, B, and C, (ii) at least one of A, B, and C; and (iii) A, and / or B and / or C. This concept is illustrated for three elements (i.e., A, B, C), but extends to fewer and greater numbers of elements (e.g., A, B, C, D, etc.).

[0455] Usage of the term "typically although not necessarily", "although not necessarily so" "such as", "e.g.", "possibly", "it is possible", "it may be possible", "optionally", "say", "for example," "for instance", "an example" "one example", "illustrated example", "some examples", "another example", "other examples, "various examples", "examples", "instances", "one instance", "some instances", "another instance", "other instances", "one case", "some cases", "another case", "other cases", "cases", or variants thereof means that a particular described feature, structure, characteristic, stage, method, module, element, entity, or system is included in at least one example of the subject matter, but not necessarily in all examples.

[0456] Unless otherwise stated or applicable, the appearance of the same term does not necessarily refer to the same example(s)

[0457] The term " illustrated example", is used to direct the attention of the reader to one or more of the figures but should not be construed as necessarily favoring any example over any other.

[0458] As used herein, unless otherwise specified, the use of the ordinal adjectives "first", "second", etc., to describe like objects, merely indicate that different instances of like objects are being referred to and are not intended to imply that the objects so described must be in a given sequence, temporally, in ranking, and / or in any other manner.

[0459] Usage of conditional language, such as "may", "can", "could", or variants thereof should be construed as conveying that one or more examples of the subject matter may include, while one or more other examples of the subject matter may not necessarily include, certain features, structures, stages, methods, modules, elements, entities or systems.

[0460] Thus, such conditional language is not generally intended to imply that a particular described feature, structure, stage, method, module, element, entity, or system is necessarily included in all examples of the subject matter.

[0461] The term "non-transitory" is used to exclude transitory, propagating signals, but to otherwise include any volatile or non-volatile computer memory technology suitable to the application.

[0462] Conversely, various features of the present disclosure, which are, for brevity, described in the context of a single embodiment, example and / or option, may also be provided separately or in any suitable sub-combination or as suitable in any other described embodiment of the present disclosure.

[0463] Accordingly, certain features described in the context of various embodiments are not to be considered essential features of those embodiments, unless the embodiment, example, and / or option is inoperative without those elements.

[0464] Furthermore, embodiments, examples, features, structures, characteristics, stages, methods, modules, elements, entities, and / or systems disclosed herein, which are, for clarity, described in the context of separate examples, may also be provided in combination with a single example.

[0465] Contrariwise, various embodiments, examples, features, structures, characteristics, stages, methods, modules, elements, entities, and / or systems disclosed herein, which are, for brevity, described in the context of a single example, may also be provided separately or in any suitable sub-combination.

[0466] Usage of terms such as "observing", "estimating", "comparing", "determining", "outputting", "reporting", "evaluating", "noting", "performing", "recognizing", "handling", "dropping", "updating", "adjusting", "delaying", "stopping", "checking", "waiting", "causing", "removing", "placing", "storing", "intercepting", "forwarding", "executing", implementing", or the like, may refer to the action(s) and / or process(es) of a system such as any of the system(s) described below, or a part thereof

[0467] Discussions herein utilizing terms such as, for example, "processing", "computing", "calculating", "determining", "establishing", "analyzing", "checking", “estimating”, “deriving”, or the like, may refer to operation(s) and / or process(es) of a computer, a computing platform, a computing system, or other electronic computing device, that manipulate and / or transform data represented as physical (e.g., electronic) quantities within the computer's registers and / or memories into other data similarly represented as physical quantities within the computer's registers and / or memories or other information storage medium that may store instructions to perform operations and / or processes.

[0468] The term “determining” and “estimating” may also refer to “heuristically determining” and “heuristically estimating”, respectively.

[0469] The term “operatively coupled” may encompass the meanings of the terms “responsively coupled”, “communicably coupled”, and the like.

[0470] Moreover, “coupled with” means indirectly or directly "coupled with”.

[0471] Within this description, the term “elastically coupled” will be used to indicate that a first element is joined to a second element with a flexible connection that defines and tends to restore a nominal positional relationship between the elements but allows relative motion in at least one direction.

[0472] Unless otherwise indicated or applicable, the word "or" in the description and claims is considered to be the inclusive "or" rather than the exclusive or, and indicates at least one of, or any combination of items it conjoins.

[0473] It is appreciated that certain features of the present disclosure, which are, for clarity, described in the context of separate embodiments or examples, may also be provided in any combination in a single embodiment.

[0474] Conversely, various features of the present disclosure, which are, for brevity, described in the context of a single embodiment, example and / or option, may also be provided separately or in any suitable sub-combination or as suitable in any other described embodiment, example, and / or option of the present disclosure.

[0475] Furthermore, any feature disclosed herein can be disclaimed, alone or in any combination of features. Certain features described in the context of various embodiments, examples and / or options are not to be considered essential features of those embodiments, unless the embodiment, example and / or option is inoperative without those elements.

[0476] It is noted that the term "exemplary" is used herein to refer to examples of embodiments and / or implementations and is not meant to necessarily convey a more-desirable use-case.

[0477] In alternative embodiments, additional, fewer, and / or different elements may be used.

[0478] The terms cited above also denote inflections and conjugates thereof.

[0479] All references mentioned in this specification are herein incorporated in their entirety by reference into the specification, to the same extent as if each individual patent was specifically and individually indicated to be incorporated herein by reference.

[0480] In addition, citation, and / or identification of any reference in this application shall not be construed as an admission that such reference is available as prior art to the present application.

[0481] Throughout this application, various embodiments of this present disclosure may be presented in a range format. It should be understood that the description in range format is merely for convenience and brevity and should not be construed as an inflexible limitation on the scope of the present disclosure.

[0482] Accordingly, the description of a range should be considered to have specifically disclosed all the possible subranges as well as individual numerical values within that range. For example, description of a range such as from 1 to 6 should be considered to have specifically disclosed subranges such as from 1 to 3, from 1 to 4, from 1 to 5, from 2 to 4, from 2 to 6, from 3 to 6 etc., as well as individual numbers within that range, for example, 1, 2, 3, 4, 5, and 6. This applies regardless of the breadth of the range.

[0483] Additionally, whenever a numerical range is indicated herein, it is meant to include any cited numeral (fractional or integral) within the indicated range. The phrases “ranging / ranges between” afirst indicate number and a second indicate number and “ranging / ranges from” afirst indicate number “to” a second indicate number are used herein interchangeably and are meant to include thefirst and second indicated numbers and all the fractional and integral numerals therebetween.

[0484] The acceptable noise to signal ratio may be a threshold which is static, dynamic, and / or adaptive. Static thresholds are predetermined thresholds that remain constant. Dynamic thresholds are forcefully changed, for example, at a certain time of day, or a certain day of the year. Adaptive thresholds are changed in response to changes in characteristics, e.g., of the system, and may vary depending on a variety of parameters.

[0485] Unless specified or limited otherwise, the terms “mounted,” “connected,” “supported,” and “coupled” and variations thereof are used broadly and encompass both direct and indirect mountings, connections, supports, and couplings. Further, “com1ected” and “coupled” are not restricted to physical or mechanical connections or couplings.

[0486] The terms “determining” and “determine” refer to ascertaining a particular state of a system or variable.

[0487] REFERENCES

[0488] [1] M. A. Govoni, “Micro-Doppler signal decomposition of small commercial drones,” May 2017, pp.0425–0429.

[0489] [2] C. J. Li and H. Ling, “An investigation on the radar signatures of small consumer drones,” IEEE Antennas and Wireless Propagation Letters, vol.16, pp.649–652, 2017.

[0490] [3] I. Bilik, J. Tabrikian, and A. Cohen, “GMM-based target classification for ground surveillance Doppler radar,” IEEE Transactions on Aerospace and Electronic Systems, vol.42, no.1, pp.267–278, Jan. 2006.

[0491] [4] I. Bilik and J. Tabrikian, “Radar target classification using Doppler signatures of human locomotion models,” IEEE Transactions on Aerospace and Electronic Systems, vol. 43, no. 4, pp. 1510– 1522, Oct.2007.

[0492] [5] V. C. Chen, C. Lin, and W. P. Pala, “Time-varying Doppler analysis of electromagnetic backscattering from rotating object,” in 2006 IEEE Conference on Radar, Apr.2006, pp.6 pp.–.

[0493] [6] V. C. Chen, F. Li, S.. Ho, and H. Wechsler, “Micro-Doppler effect in radar: phenomenon, model, and simulation study,” IEEE Transactions on Aerospace and Electronic Systems, vol.42, no.1, pp.2–21, Jan.2006.

[0494] [7] J. Tabrikian, S. Dubnov, and Y. Dickalov, “Maximum a-posteriori probability pitch tracking in noisy environments using harmonic model,” IEEE Transactions on Speech and Audio Processing, vol.12, no.1, pp.76–87, Jan.2004.

[0495] [8] E. Fisher, J. Tabrikian, and S. Dubnov, “Generalized likelihood ratio test for voiced / unvoiced decision using the harmonic plus noise model,” in 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP ’03)., vol.1, Apr.2003, pp. I–I.

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[0010] S. Haykin, Y. Xue, and T. N. Davidson, “Optimal waveform design for cognitive radar,” in 2008 42nd Asilomar Conference on Signals, Systems and Computers, Oct.2008, pp.3–7.

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[0013] K. L. Bell, C. J. Baker, G. E. Smith, J. T. Johnson, and M. Rangaswamy, “Cognitive radar framework for target detection and tracking,” IEEE Journal of Selected Topics in Signal Processing, vol.9, no.8, pp.1427– 1439, Dec.2015.

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[0014] P. Chen, C. Qi, L. Wu, and X. Wang, “Estimation of extended targets based on compressed sensing in cognitive radar system,” IEEE Transactions on Vehicular Technology, vol.66, no.2, pp.941–951, Feb.2017.

[0502] Additional examples:

[0503] Embodiments pertain to a method and / or system for detecting and characterizing at least one object in a scene that is being propelled by at least one rotating device.

[0504] In embodiments, the system is configured to perform, and / or the method comprises:

[0505] transmitting electromagnetic radiation (EM) into free space towards the scene;

[0506] receiving, from the scene, EM radiation reflections that are reflected from at least one rotating device of the at least one object that is present in the scene; and

[0507] processing the reflections to detect, based on harmonic characteristics of the reflected EM radiation, the presence of the at least one object in the scene.

[0508] In embodiments, the method may comprise processing the reflections to characterize, based on the harmonic characteristics of the reflected EM radiation, the type of the at least one detected object.

[0509] In embodiments, the processing comprises applying an harmonic filter on the reflected EM radiation to detect harmonics of the reflected EM radiation.

[0510] In embodiments, the applying of the harmonic filter includes sweeping a range of frequency values with the purpose of detecting harmonics of the reflected EM radiation.

[0511] In embodiments, the emitting of the EM radiation is adapted based on previous EM radiation reflections such to match harmonic characteristics of the at least one rotating device.

[0512] In embodiments, the adapting is performed such to excite natural modes of a micro-Doppler signature of the rotating device.

[0513] In embodiments, the adapting is performed to maximize reflections and / or intensity from the at least one rotating device.

[0514] In embodiments, the EM reflections comprise micro-Doppler signatures.

[0515] In embodiments, the previous EM radiation reflections are processed to determine previous reflection patterns.

[0516] In embodiments, the previous EM radiation reflections are obtained in response to a preceding EM radiation emission cycle and / or retrieved from a database.

[0517] In embodiments, the processing of EM radiation reflections includes applying a Generalized Likelihood Ratio Test (GLRT) for determining the presence of the at least one rotary element in the scene.

[0518] In embodiments, the at least one object is one of the following: an aircraft, a rotorcraft, a one- wheeled vehicle, two-wheeled vehicle, a three-wheeled vehicle, a four-wheeled vehicle, a land-based vehicle, a watercraft, or a multipurpose vehicle.

[0519] In embodiments, the object comprises one or more of the following rotating devices: a rotor, a wheel, a propeller blade, a turbine blade, a turbofan fan blade and / or any airfoil configuration adapted for propelling mobile platform,

[0520] In embodiments, the characterizing comprises estimating, for the at least one object, for example, the number of propellers, dimensions, velocity, and / or acceleration.

[0521] In embodiments, the at least one object is an unmanned aerial vehicle (UAV), and / or a drone.

[0522] In embodiments, the at least one rotating device is virtually subdivided into a plurality of rotating segments each having a distinct angular velocity.

[0523] In embodiments, each segment of the virtually subdivided rotating device has a characterizable reflected EM radiation pattern.

[0524] In embodiments, the processing of the reflections is based on a plurality of EM radiation patterns associated with the plurality of rotating segments.

[0525] In embodiments, the processing of the reflections is based on a superposition of the reflected EM radiation patterns.

[0526] In embodiments, the superposition is performed to obtain a coherent harmonic EM radiation.

[0527] In embodiments, a cognitive radar approach is employed for adaptively synchronizing the emitted EM radiation based on the analysis of reflected EM radiation harmonic characteristics.

[0528] In embodiments, the adapting is performed, for example, on one of the following radar characteristics:

[0529] EM radiation carrier frequency,

[0530] pulse repetition interval (PRI),

[0531] radiation transmission time, and / or any combination of the aforesaid.

[0532] In embodiments, the processing of the reflected EM radiation comprises identifying reflections having the greatest intensity, in sequentially decreasing order.

[0533] Embodiments pertain to a system and / or method for detecting and characterizing at least one object in a scene that is being propelled by at least one rotating device submerged in water.

[0534] In embodiments, the system is configured to perform, and / or the method comprises:

[0535] receiving, from the scene, acoustic waves generated due to the rotation of the rotating device in water processing the reflections to detect, based on harmonic characteristics of the acoustic waves, the presence of the at least one object in the scene.

[0536] Embodiments pertain to a method and / or system configured for detecting and characterizing at least one object in a scene that is being propelled by at least one rotating device submerged in water.

[0537] In embodiments, the system is configured to perform, and / or the method comprises:

[0538] transmitting acoustic waves towards the scene;

[0539] responsively receiving, from the scene, acoustic wave reflections which are reflected from the submerged rotating device;

[0540] processing the acoustic reflections to detect, based on harmonic characteristics of the reflected acoustic waves, the presence of the at least one object in the scene.

[0541] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art. The materials, methods, and examples provided herein are illustrative only and not intended to be limiting.

[0542] Implementation of the method and system of the present disclosure may involve performing or completing certain selected tasks or steps manually, automatically, or a combination thereof.

[0543] Moreover, according to actual instrumentation and equipment of preferred embodiments of the method and system of the present disclosure, several selected steps may be implemented by hardware (HW) or by software (SW) on any operating system of any firmware, or by a combination thereof.

[0544] For example, as hardware, selected steps of the disclosure could be implemented as a processor chip or a circuit. As software or algorithm, selected steps of the disclosure could be implemented as a plurality of software instructions being executed by a computer / processor using any suitable operating system.

[0545] In any case, selected steps of the method and system of the disclosure could be described as being performed by a data processor, such as a computing device for executing a plurality of instructions.

[0546] Various implementations of the systems and techniques described here can be realized in digital electronic circuitry, integrated circuitry, specially designed ASICs (application specific integrated circuits), computer hardware, firmware, software, and / or combinations thereof.

[0547] These various implementations can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0548] Any digital computer system, unit, device, module and / or engine exemplified herein can be configured or otherwise programmed to implement a method disclosed herein, and to the extent that the system, module and / or engine is configured to implement such a method, it is within the scope and spirit of the disclosure.

[0549] Once the system, module and / or engine are programmed to perform particular functions pursuant to computer readable and executable instructions from program software that implements a method disclosed herein, it in effect becomes a special purpose computer particular to embodiments of the method disclosed herein.

[0550] The methods and / or processes disclosed herein may be implemented as a computer program product that may be tangibly embodied in an information carrier including, for example, in a non- transitory tangible computer-readable and / or non-transitory tangible machine-readable storage device.

[0551] The computer program product may be directly loadable into an internal memory of a digital computer, comprising software code portions for performing the methods and / or processes as disclosed herein.

[0552] The methods and / or processes disclosed herein may be implemented as a computer program that may be intangibly embodied by a computer readable signal medium.

[0553] A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof.

[0554] A computer readable signal medium may be any computer readable medium that is not a non- transitory computer or machine-readable storage device, which may communicate, propagate, or transport a program for use by or in connection with apparatuses, systems, platforms, methods, operations, and / or processes discussed herein.

[0555] It should be understood that where the claims or specification refer to "a" or "an" element and / or feature, such reference is not to be construed as there being only one of that element. Hence, reference to “an element” or “at least one element” for instance may also encompass “one or more elements”.

[0556] It is important to note that the methods discussed herein are not limited to those diagrams or to the corresponding descriptions. For example, the method may include additional or even fewer processes or operations in comparison to what is described herein. In addition, embodiments of the method are not necessarily limited to the chronological order as illustrated and described herein.

[0557] It is appreciated that certain features of the disclosure, which are, for clarity, described in the context of separate embodiments or example, may also be provided in combination, in a single embodiment.

[0558] Conversely, various features of the disclosure, which are, for brevity, described in the context of a single embodiment, example and / or option, may also be provided separately or in any suitable sub combination or as suitable in any other described embodiment, example, or option of the disclosure.

[0559] Certain features described in the context of various embodiments, examples and / or options are not to be considered essential features of those embodiments, unless the embodiment, example and / or option is inoperative without those elements.

[0560] While the disclosure has been described with respect to a limited number of embodiments, these should not be construed as limitations on the scope of the disclosure, but rather as exemplifications of some of the embodiments.

[0561] While certain features of the described implementations have been illustrated as described herein, many modifications, substitutions, changes, and equivalents will now occur to those skilled in the art. It should be understood that they have been presented by way of example only, not limitation, and various changes in form and details may be made.

[0562] Any portion of the apparatus and / or methods described herein may be combined in any combination, except mutually exclusive combinations. The implementations described herein can include various combinations and / or sub-combinations of the functions, components and / or features of the different implementations and embodiments described.

Claims

CLAIMS WHAT IS CLAIMED IS:

1. A method for detecting and characterizing at least one object in a scene that is being propelled by at least one rotating device, the method comprising: transmitting electromagnetic radiation (EM) into free space towards the scene; receiving, from the scene, EM radiation reflections that are reflected from at least one rotating device of the at least one object that is present in the scene; and processing the reflections to detect, based on harmonic characteristics of the reflected EM radiation, the presence of the at least one object in the scene.

2. The method of claim 1, further comprising: processing the reflections to characterize, based on the harmonic characteristics of the reflected EM radiation, the type of the at least one detected object.

3. The method of claim 1 and / or claim 2, wherein the processing comprises applying an harmonic filter on the reflected EM radiation to detect harmonics of the reflected EM radiation.

4. The method of claim 3, wherein the applying of the harmonic filter includes sweeping a range of frequency values with the purpose of detecting harmonics of the reflected EM radiation.

5. The method of any one or more of the preceding claims, wherein the emitting of the EM radiation is adapted based on previous EM radiation reflections such to match harmonic characteristics of the at least one rotating device.

6. The method of any one or more of the preceding claims, wherein the adapting is performed such to excite natural modes of a micro-Doppler signature of the rotating device.

7. The method of any one or more of the preceding claims, wherein the adapting is performed to maximize reflections and / or intensity from the at least one rotating device.

8. The method of any one or more of the preceding claims, wherein the EM reflections comprise micro-Doppler signatures.

9. The method of any one or more of the claims 4 to 8, wherein the previous EM radiation reflections are processed to determine previous reflection patterns.

10. The method of claim 8, wherein the previous EM radiation reflections are obtained in response to a preceding EM radiation emission cycle and / or retrieved from a database.

11. The method of any one or more of the preceding claims, wherein the processing of EM radiation reflections includes applying a Generalized Likelihood Ratio Test (GLRT) for determining the presence of the at least one rotary element in the scene.

12. The method of any one or more of the preceding claims, wherein the at least one object is one of the following: an aircraft, a rotorcraft, a one-wheeled vehicle, two-wheeled vehicle, a three-wheeled vehicle, a four-wheeled vehicle, a land-based vehicle, a watercraft, or a multipurpose vehicle.

13. The method of any one or more of the preceding claims, wherein the object is a vehicle comprising one or more of the following rotating devices: a rotor, a wheel, a propeller blade, a turbine blade, a turbofan fan blade and / or any airfoil configuration adapted for propelling mobile platform, 14. The method of any one or more of the preceding claims, wherein characterizing comprises estimating, for the at least one object, the number of propellers, dimensions, velocity and / or acceleration.

15. The method of any one or more of the preceding claims, wherein the at least one object is an unmanned aerial vehicle (UAV).

16. The method of any one or more of the preceding claims, wherein the at least one rotating device is virtually subdivided into a plurality of rotating segments each having a distinct angular velocity.

17. The method of claim 16, wherein each segment of the virtually subdivided rotating device has a characterizable reflected EM radiation pattern.

18. The method of claim 17, wherein the processing of the reflections is based on a plurality of EM radiation patterns associated with the plurality of rotating segments.

19. The method of claim 18 and / or 19, wherein the processing of the reflections is based on a superposition of the reflected EM radiation patterns.

20. The method of any one or more of the claims 16 to 19, wherein the superposition is performed to obtain a coherent harmonic EM radiation.

21. The method of any one or more of the claims 5 to 20, wherein a cognitive radar approach is constructed configured to adaptively synchronize the emitted EM radiation based on the analysis of reflected EM radiation harmonic characteristics.

22. The method of claim 5 to 20, wherein the adapting is performed for: EM radiation carrier frequency, pulse repetition interval (PRI), radiation transmission time, or any combination of the aforesaid.

23. The method of any one of the previous claims, wherein the processing of the reflected EM radiation comprises identifying reflections having the greatest intensity, in sequentially decreasing order.

24. A method for detecting and characterizing at least one object in a scene that is being propelled by at least one rotating device submerged in water, the method comprising: receiving, from the scene, acoustic waves generated due to the rotation of the rotating device in water; processing the reflections to detect, based on harmonic characteristics of the acoustic waves, the presence of the at least one object in the scene.

25. A method for detecting and characterizing at least one object in a scene that is being propelled by at least one rotating device submerged in water, the method comprising: transmitting acoustic waves towards the scene; responsively receiving, from the scene, acoustic wave reflections which are reflected from the submerged rotating device; processing the acoustic reflections to detect, based on harmonic characteristics of the reflected acoustic waves, the presence of the at least one object in the scene.

26. A system for detecting and characterizing at least one object in a scene that is being propelled by at least one rotating device, the system comprising: at least one processor; and at least one memory storing software code portions executable by the at least one processor to enable the system to perform the following: transmitting electromagnetic radiation (EM) into free space towards the scene; receiving, from the scene, EM radiation reflections that are reflected from at least one rotating device of the at least one object that is present in the scene; and processing the reflections to detect, based on harmonic characteristics of the reflected EM radiation, the presence of the at least one object in the scene.

27. The system of claim 26, configured to perform the following:processing the reflections to characterize, based on the harmonic characteristics of the reflected EM radiation, the type of the at least one detected object.

28. The system of claim 26 and / or claim 27, wherein the transmitting of the EM radiation is adapted based on previous EM radiation reflections such to match harmonic characteristics of the at least one rotating device.

29. The system of any one or more of the claims 26 to 28, wherein the adapting is performed such to excite natural modes of a micro-Doppler signature of the rotating device.

30. The system of any one or more of the claims 26 to 29, wherein the adapting is performed to maximize reflections and / or intensity from the at least one rotating device.

31. The system of any one or more of the claims 26 to 30, wherein the EM reflections comprise micro- Doppler signatures.

32. The system of any one or more of the claims 26 to 31, wherein the previous EM radiation reflections are processed to determine previous reflection patterns.

33. The system of claim 32, wherein the previous EM radiation reflections are obtained in response to a preceding EM radiation emission cycle and / or retrieved from a database.

34. The system of any one or more of the claims 26 to 33, wherein the processing of EM radiation reflections includes applying a Generalized Likelihood Ratio Test (GLRT) for determining the presence of the at least one rotary element in the scene.

35. The system of any one or more of the claims 26 to 34, wherein the at least one object is one of the following:an a rotorcraft, a one-wheeled vehicle, two-wheeled vehicle, a three-wheeled vehicle, a four- wheeled vehicle, a land-based vehicle, a watercraft, or a multipurpose vehicle.

36. The system of any one or more of the claims 26 to 35, wherein the object is a vehicle comprising one or more of the following rotating devices: a rotor, a wheel, a propeller blade, a turbine blade, a turbofan fan blade and / or any airfoil configuration adapted for propelling mobile platform, 37. The system of any one or more of the claims 26 to 36, wherein characterizing comprises estimating, for the at least one object, the number of propellers, dimensions, velocity and / or acceleration.

38. The system of any one or more of the claims 26 to 37, wherein the at least one object is an unmanned aerial vehicle (UAV).

39. The system of any one or more of the claims 26 to 38, wherein the at least one rotating device is virtually subdivided into a plurality of rotating segments each having a distinct angular velocity.

40. The system of any one or more of the claims 26 to 39, wherein the processing of the reflections is based on a plurality of EM radiation patterns associated with the plurality of rotating segments.

41. The system of any one or more of the claims 26 to 40, wherein the processing of the reflections is based on a superposition of the reflected EM radiation patterns.

42. The system of any one or more of the claims 26 to 41, wherein a superposition is performed to obtain a coherent harmonic EM radiation.

43. The system of any one or more of the claims 26 to 42, wherein a cognitive radar approach is constructed configured to adaptively synchronize the emitted EM radiation based on the analysis of reflected EM radiation harmonic characteristics.

44. The system of any one or more of the claims 26 to 43, wherein the adapting is performed for: EM radiation carrier frequency, pulse repetition interval (PRI), radiation transmission time, or any combination of the aforesaid.

45. The system of any one or more of the claims 26 to 44, wherein the processing of the reflected EM radiation comprises identifying reflections having the greatest intensity, in sequentially decreasing order.

46. A system for detecting and characterizing at least one object in a scene that is being propelled by at least one rotating device submerged in water, the system comprising: at least one processor; and at least one memory storing software code portions executable by the at least one processor to enable the system to perform the following: receiving, from the scene, acoustic waves generated due to the rotation of the rotating device in water; processing the reflections to detect, based on harmonic characteristics of the acoustic waves, the presence of the at least one object in the scene.

47. A system for detecting and characterizing at least one object in a scene that is being propelled by at least one rotating device submerged in water, the system comprising: at least one processor; and at least one memory storing software code portions executable by the at least one processor to enable the system to perform the following: transmitting acoustic waves towards the scene; responsively receiving, from the scene, acoustic wave reflections which are reflected from the submerged rotating device;processing the acoustic reflections to detect, based on harmonic characteristics of the reflected acoustic waves, the presence of the at least one object in the scene.

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