A target detection and imaging system, comprising a RADAR unit and at least one ultra-low phase-noise frequency synthesizer, is provided.
RADAR unit configured for detecting the presence and characteristics of one or more objects in various directions.
The RADAR unit may include a transmitter for transmitting at least one radio signal and a receiver for receiving at least one radio signal returned from one or more objects. Signals. The ultra-low phase-noise frequency synthesizer may utilize a dual loop design comprising one main PLL and one sampling PLL. The main PLL might include a DDS or Fractional-N PLL plus a variable divider. The synthesizer may utilize a sampling PLL only to reduce phase noise from the returned radio signal. This system helps detect and classify human beings on the road clearly and in time to provide timely corrective input to the autonomous vehicle.
Provide disciplinary information to the autonomous vehicle timely
Embodiments of the present disclosure are generally related to sensors for autonomous vehicles.
(for example, Self-Driving Cars) and in particular to systems that use ultra-low phase-noise frequency synthesizers for RADAR Sensor Applications for autonomous vehicles.
Autonomous vehicles are paving the way for a new mode of transportation. Autonomous cars require minimum or no intervention from the vehicle’s driver.
Generally, some autonomous vehicles need only an initial input from the driver, whereas some other designs of autonomous cars are continuously under the driver’s control. Some autonomous vehicles can be remotely controlled. For example, automatic parking in cars is an example of an autonomous vehicle in operation.
Autonomous vehicles face a dynamic environment that keeps changing every time. Autonomous cars need to track lane markings, road edges, track road curves, and varying surfaces, including flat surfaces, winding roads, hilly roads, etc. Alongside, autonomous vehicles also need to check on stationary or mobile objects like trees, humans, or animals. Hence, autonomous cars must capture vast information that keeps changing every time.
Therefore, autonomous vehicles are provided with various sensors to overcome and meet these challenges. These sensors help the car to gather general information and help in increasing the degree of autonomy of the car. The different types of sensors currently being used in autonomous vehicles are LiDAR sensors, Ultrasonic sensors, Image sensors, Global Positioning System (GPS) sensors, Inertial Measurement Unit (IMU) sensors, dead reckoning sensors, Microbolo sensors, Speed sensors, Steering-angle sensors, Rotational speed sensors, Real-time Kinematics sensors, and RADAR sensors. Two of the most used sensors are LiDAR and RADAR sensors.
LiDAR sensors: LiDAR is a device that maps objects in 3-dimensional by bouncing laser beams off its real-world surroundings. LiDAR in automotive systems typically uses a 905 nm wavelength that can provide up to 200 m range in restricted FOVs (field of views). These sensors scan the environment, around the vehicle, with a non-visible laser beam. LIDAR sensor continually fires off beams of laser light and then measures how long it takes for the light to return to the sensor—the laser beam generated is of low intensity and non-harmful. The shaft visualizes objects and measures ranges to create a 3D image of the vehicle’s surrounding environment. LiDAR sensors are very accurate and can gather information to even up to very close distances around the car. However, LiDAR sensors are generally bulky, complex in design, and expensive. The costs can be between around $8,000 and even up to $100,000. Smaller and less costly LiDAR sensors are starting to be on the market. LiDAR may also require complex computing of the collected data, adding to the costs. Also, in general, LiDARs can capture data up to a distance of around 200 m.
It is to be noted that LiDAR requires optical filters to remove sensitivity to ambient light and to prevent spoofing from other LiDARs. Also, the laser technology used has to be “eye-safe.” Recently mechanical scanning LiDAR, which physically rotates the laser and receiver assembly to collect data over an area that spans up to 360°, has been replaced with Solid State LiDAR (SSL), which has no moving parts and is, therefore, more reliable, especially in an automotive environment for long-term reliability. However, SSLs currently have lower field-of-view (FOV) coverage. In the current LiDAR sensor design, coverage is also a problem in terms of sensor gap and overlap since the LiDAR in autonomous vehicles has minimal redundancy sensors that can provide the level of imaging that a LiDAR provides under optimal conditions. LiDAR is also weather susceptible. It turns blind when it comes to imaging in adverse weather conditions. LiDAR has limitations in creating precise imaging in fog, rain, snow, direct sunlight, and darkness conditions. Also, LiDAR cannot read letters on a signboard. This is so because the signboard is flat.
RADAR sensors: RADAR sensors send out electromagnetic waves. When these waves hit an obstacle, they get reflected. Thus, revealing how far away an object is and how fast it is approaching.
RADAR sensors are very crucial in today’s autonomous vehicle applications. They are required to be more accurate.
Automotive RADARs can be categorized into long-range, medium-range, and short-range radars. Long-range RADARs are used for measuring the distance to and speed of other vehicles. Medium-range RADARs are used for detecting objects within a wider field of view, e.g., for cross-traffic alert systems. Short-range RADARs are used for sensing in the car’s vicinity, e.g., for parking aid or obstacle detection. Depending on the application, RADAR requirements differ. Short-range applications require a steerable antenna with a large scanning angle, creating a wide field of view. On the other hand, long-range applications require more directive antennas that provide a higher resolution within a more limited scanning range. Two frequency bands are mainly used for automotive RADARs: the 24 GHz and 77 GHz bands. The 77 GHz band offers higher performance but is also more challenging to implement since losses are much higher at these frequencies. The 24 GHz RADARs are easier to develop but are more extensive, making it challenging to integrate them into a vehicle. RADARs operating at 24 GHz require around three times larger antennas than RADARs operating at 77 GHz to achieve the same performance. A 77 GHz RADAR would thus be much smaller, resulting in easier integration and lower cost. Moving to higher frequencies enables RADARs with a better resolution. However, a significant challenge is developing steerable antennas for 77 GHz RADARs with high enough performance at a reasonable cost. In one embodiment of the inventions, different types of antenna and meta-material-based antennas less prone to phase noise disturbance will benefit from our story.
Automotive RADAR systems use a pulse-Doppler approach, where the transmitter operates for a short period, known as the pulse repetition interval (PRI). The system switches to receive mode until the next transmit pulse. As the RADAR returns, the reflections are processed coherently to extract detected objects’ range and relative motion. Another approach is to use continuous wave frequency modulation (CWFM). This approach uses a constant carrier frequency that constantly varies over time with a receiver. To prevent the transmit signal from leaking into the receiver, separate send and receive antennas are used.
Generally, there are three types of RADARs in use in autonomous vehicles. Short-range RADAR helps in collision warning and provides assisted parking support. Medium Range RADAR helps to watch corners of the car, helps in blind spot detection and lane detection, and avoids side/corner collisions. Further, long-range RADARs help in adaptive cruise control and early collision detection functions.
RADAR signal processing also needs to be efficient. It must intelligently group the bouncing signals from the same object in the range. Otherwise, the RADAR signal processing will be overwhelmed with the amount of signal processing and may get confused. Grouping is made possible by using the Doppler shift of the signals bouncing off from the surfaces with a. velocity different from the observation domain. Thus, Doppler maps are created that depicts the range to object returns on one axis and extracted rate of the targets on the other.
RADARs have also been used for identifying and classifying humans. Though, RADARs are not efficient in performing human identification. However, there have been techniques that are being used for human identification.
One of the techniques uses the reflectivity of humans using an ultra-wideband RADAR. In this technique, the polarization of the reflected signal can be analyzed; It can be determined that there are some frequencies where, in one polarization, there is maximum reflectivity and a minimum in the other. However, the polarized signal depends mainly on the person’s shape, posture, and position. Thus, making it a highly unreliable technique for classification.
Another technique used for human classification uses a dual-band frequency-modulated continuous wave RADAR. This technique compares the difference in reflected signal from an object at different frequencies (commonly 10 Hz to 66 Hz). This comparison established the threshold for the ratio of the received intensity between two frequencies, above which detected objects can be classified as animals.
Some techniques utilized and analyzed the Doppler spectrum of CW RADAR to obtain a Doppler or micro-Doppler Signature for a walking human. At the same time, some of the methods used wavelet transform to extract the micro signatures created by human walking.
Other techniques that should be mentioned here include using two or more different frequencies and multiple chirp types; these and the methods above support the evaluation and recognition of the electromagnetic characteristics and properties of a human being or other targeted object. These techniques can be used for material detection and human classification. Also, using multiple frequencies and chirp types provides information for micro-Doppler and radar signature evaluation and recognition.
The techniques mentioned aim to identify humans and distinguish them from other walking objects like animals.
RADAR sensors are low-priced and provide excellent sensors. RADARs also cost much less than LIDAR and may be procured within $150. These sensors work extremely accurately in bad weather conditions like fog, snow, dirt, etc. RADAR sensors use straightforward circuitry and thus are smaller in size, making them easy to manufacture, install and use. However, one of the significant drawbacks of the RADAR sensors is that they give confusing results when multiple objects are within the range. They are not able to filter noise in such situations. Existing RADARs do not offer the necessary resolution to distinguish objects with sufficient reliability. One of the main problems faced is separating small and large things that travel at the same distance and velocity in adjacent lanes, e.g., a motorcycle driving in the lane next to a truck.
Significant factors affecting RADAR performance are described in the following paragraphs:
Transmitter Power and Antenna:
The maximum range of a RADAR system depends mainly on its transmitter’s average power and its antenna’s physical size. This is also called the power-aperture product. The antenna remains a challenge for autonomous vehicles, and a lot is invested in various antenna developments. The invention described here can be used with a type of antenna, including antennas based and made out of meta-materials. The synergy between a meta-material antenna and this invention would result in a high-performing radar sensor.
The sensitivity of a RADAR receiver is determined by the unavoidable noise that appears at its input. At microwave RADAR frequencies, the noise that limits detectability is usually generated by the receiver itself (i.e., by the random motion of electrons at the receiver’s input) rather than by external noise that enters the receiver via the antenna.
The target size, as “seen” by RADAR, is not always related to the object’s physical size. The measure of the target size, as observed by RADAR, is called RADAR cross-section and is determined in units of area (square meters). Two targets with the same physical cross-sectional area can differ considerably in RADAR size or cross-section. For example, a flat plate 1 square meter in the area will produce a RADAR cross-section of about 1,000 square meters at a frequency of 3 GHz when viewed perpendicular to the surface. A cone-sphere (an object resembling an ice-cream cone), when viewed in the direction of the cone rather than the sphere, could have a RADAR cross-section of about 0.001 square meters even though its projected area is also 1 square meter. Hence, this may cause calculation mistakes and give the wrong estimation of the identified objects.
Echoes from environmental factors like land, rain, birds, and other similar objects may cause a nuisance in detecting objects. Clutter makes it difficult to identify objects and their properties to a considerable extent.
Signals from nearby RADARs and other transmitters can be strong enough to enter a RADAR receiver and produce spurious responses. Interference is more easily addressed by automatic detection and tracking systems. Hence, interference may further add noise to the RADAR signals.
Comparison Between LiDAR and RADAR
Compared to LiDAR sensors, RADAR sensors provide more robust information to vehicles. LiDAR sensors are generally mounted on the car and mechanically rotated to gather surrounding information. This rotational movement is prone to dysfunction. Whereas in the RADAR case, as they are solid state and have no moving parts, they have a minimal rate of failures.
Also, LiDAR sensors produce pulsed laser beams and can gather information only when the pulsed beam generates the laser beams. RADAR sensors can generate continuous beams and thus provide constant details.
Also, LiDAR sensors generate enormous and complex data for which complex computational modules are required to be used. For example, some types of LIDAR systems generate amounts of 1-Gb/s data that need substantial computation by powerful computers to process such a high data payment. In some cases, these massive computations require additional analysis and correlation of information from other sensors and sources of information. These increases cost overheads for vehicle manufacturers. RADAR sensors only generate small fractions of data that are easy to compute.
LiDAR sensors are also sensitive to adverse weather conditions such as rain, fog, and snow, while RADAR sensors are not prone to weather conditions. Though RADAR is not affected by darkness and can work well in adverse weather conditions, it drastically lowers its resolution.
RADAR signatures of a walking human being are a big problem. These signatures need to be more easily recognizable. Detection of human beings is a solution to which most computing algorithms need better solutions. There are various algorithms known as segmenting algorithms that do provide a certain level of the solution. The processing engine of an autonomous vehicle may take input from RADAR, LiDAR, Camera, Ultrasound, and other multiple sensors to build an image of the vehicle’s surroundings.
Further, RADARs are generally used to detect preceding and approaching objects. Using RADARs helps decelerate the vehicle in practical situations and warn the driver.
Stationary RADAR sensors are also used to monitor a predetermined space; e.g., railway crossings may be monitored using stationary RADARs. The usage includes the identification of objects in such railway crossings. A warning can be generated in such situations, or the train may decelerate. However, to effectively use stationary RADARs, it is essential to determine the size of the objects identified by the RADAR sensor. These RADARs may include height estimating systems for things in such a RADAR range. This must be accurate as even minor deviations between the plane and vertical sensor axis can result in significant errors in estimating the object’s size.
However, RADAR sensors are challenged when dealing with slow-moving objects such as cars, bicycles, and pedestrians. Furthermore, these traditional RADAR systems, whether using a modulated or non-modulated signal, have difficulties identifying things very close to each other since one of them will be obscured due to the phase noise of the system. Also, the drawback of existing RADAR sensors is the impact on their accuracy due to the phase-noise of its frequency source, the synthesizer. RADAR sensors cannot relay the size and shape of objects as accurately as LiDAR. RADAR sensors might be a more complex solution. Ultrasonic sensors or cameras can accompany them. Though RADAR is excellent in finding tangible things over long distances, it may be challenging to identify items in a short range.
Therefore, there is a need for an enhanced detection system capable of implementing artificial intelligence using various sensory fusions, including a plurality of LiDAR, Camera, Ultrasound, and RADAR sensors for helping in making informed decisions based on surrounding information for semi or autonomous vehicles. Furthermore, the system should be capable of overcoming the shortcomings of the existing systems and technologies.
Some of the Benefits of the Invention:
The present invention emphasizes that by incorporating the ultra-low phase-noise synthesizer in an existing RADAR system, the performance of the RADAR system will be improved substantially in terms of target detection accuracy and resolution. Because of this, it can become the dominant sensor for handling autonomous cars.
Herein, the Synthesizer drastically reduces the phase-noise of RADAR signals so that such RADAR sensors can replace current sensor systems at a meager cost and with reliability in all lighting and adverse weather conditions.
A system that utilizes an ultra-low phase-noise synthesizer will be able to provide data to a processor that can determine the electromagnetic characteristics of an object with sufficient accuracy so that the system can determine if the object is a living object such as a human being or an animal or if it is inanimate. It will also be able to provide accurate data to differentiate between the materials things are made of, such as wood and stone. As an example, the data generated by the RADAR system could be used to identify and verify the presence of a human on the sidewalk about to cross the street or a bicycle rider at the side of the road.
Further, as a derivative of the capability to determine the material an object is made of combined with the electromagnetic waves capability to penetrate through many materials, an object detection system utilizing an ultra-low phase-noise synthesizer will provide data that will enable a processing unit (such as a specialized processor of the object detection system) to find objects that are visually obscured by another entity and determine the material of the covered and obscuring object. Thus, the system may be able to find a human behind a billboard/bus station advertisement or wildlife behind a bush or determine that these are only two bushes (or non-animated objects), one behind the other.
Additionally, a RADAR system that utilizes an ultra-low phase-noise synthesizer may be used as an imaging RADAR that can discover silhouettes and create an accurate 3-dimensional map of the vehicle’s surroundings, including mapping the objects that are not visible with light. Such a RADAR System would also be able to utilize Synthetic Aperture Radar (SAR) technology, Interferometry, and Polarimetry (or other SAR-related technologies) to define the exact characteristics of an objects backscatter such as, but not limited to, Surface roughness, Geometric structure, Orientation and more. Further, an ultra-low phase-noise RADAR system enables the determination of electrical characteristics such as, but not limited to, Dielectric constant, Moisture content, Conductivity, and more. The data creation of electromagnetic traits can also be achieved by combining this invention’s ultra-low phase noise synthesizer and using two or more different frequencies and multiple chirp types.
According to an embodiment of the present disclosure, an object detection system for autonomous vehicles is provided; the object detection system may include a RADAR unit coupled to at least one ultra-low phase-noise frequency synthesizer, configured for detecting the presence of one or more objects in one or more directions, the RADAR unit comprising: a transmitter for transmitting at least one radio signal; and a receiver for receiving at least one radio signal returned from one or more objects/targets. Further, the object detection system may include the at least one ultra-low phase-noise frequency synthesizer that may be utilized in conjunction with the RADAR unit, for refining both the transmitted and the received signals, and thus determining the phase-noise and maintaining the quality of the transmitted and the received radio signals, wherein the at least one ultra-low phase-noise frequency synthesizer comprises: (i) at least one clocking device configured to generate at least one first clock signal of at least one first clock frequency; (ii) at least one sampling Phase Locked Loop (PLL), wherein the at least one sampling PLL comprises: (a) at least one sampling phase detector configured to receive the at least one first clock signal and a single reference frequency to generate at least one first analog control voltage; and (b) at least one reference Voltage Controlled Oscillator (VCO) configured to receive the at least one analog control voltage to generate the single reference frequency; and (c) a Digital Phase/Frequency detector configured to receive the at least one first clock signal and a single reference frequency to generate at least a second analog control voltage; and (d) a two-way DC switch in communication with the Digital Phase/Frequency detector and the sampling phase detector; (iii) at least one first fixed frequency divider configured to receive the at least one reference frequency and to divide the at least one reference frequency by a first predefined factor to generate at least one clock signal for at least one high frequency low phase-noise Direct Digital Synthesizer (DDS) clock signal; (iv) at least one high-frequency low phase-noise DDS configured to receive the at least one DDS clock signal and to generate at least one second clock signal of at least one second clock frequency; and (v) at least one main Phase Locked Loop (PLL).
Hereinabove, the main PLL may include: (a) at least one high-frequency Digital Phase/Frequency detector configured to receive and compare the at least one second clock frequency and at least one feedback frequency to generate at least one second analog control voltage and at least one digital control voltage; (b) at least one primary VCO configured to receive the at least one first analog control voltage or the at least one second analog control voltage and generate at least one output signal of at least one output frequency, wherein the at least one digital control voltage controls which of the at least one first analog control voltage or the at least one second analog control voltage is received by the at least one primary VCO; (c) at least one down convert mixer configured to mix the at least one output frequency and the reference frequency to generate at least one intermediate frequency; and (d) at least one second fixed frequency divider configured to receive and divide the at least one intermediate frequency by a second predefined factor to generate the at least one feedback frequency.
Herein, the RADAR unit or units create a 3-dimensional RADAR image using one or more RADAR sensors and one or more frequencies. The transmitting RADAR may be at one location of the vehicle while the receiving unit is at another. The RADAR sensors may utilize Synthetic aperture RADAR (SAR) technology to create a 3-dimensional image. The 3-dimensional image may include information about objects obscured by visible light. In an embodiment, Bi-static and multi-static may also involve one vehicle transmitting while one or more other cars receive the return signals.
Further, the data from the Radar unit comprising an ultra-low phase-noise synthesizer can and should be used for improved compressed sensing, micro-Doppler classification, object classification by electromagnetic characteristics, and radar-based mapping of cities, roads, and other venues that can or are being mapped with visual sensors.
Also, in an embodiment of the invention presented here, the system can use a type of antenna, including antennas made out of meta-materials or other materials.
Herein, the radar unit determines the distance and the direction of each of one or more objects. Further, the radar unit determines one or more characteristics of two close things irrespective of the size of one or more objects. Again other, the radar unit differentiates between two or more types of objects when one object is visually obscuring another. Additionally, the radar unit utilizes a modulated or non-modulated radio signal to determine the presence of a slow-moving target despite the tiny Doppler frequency shift. Also, the radar unit uses a modulated or non-modulated radio signal to determine the presence of a close-range target despite the short signal travel time.
Additionally, a vehicle with RADAR imaging capabilities may contact other cars with that feature. The group of 2 cars or more will have an identification scheme or will set up one so that every car will be able to detect return signals from every other vehicle and thus combine a 3-dimensional map of the surroundings for primary autonomous driving purposes and mapping or another purpose. This modus operandi should not be limited only to vehicles transmitting and receiving a Radar signal; the transmitter or receiver can also be stationary, such as a radar on a traffic light that warns vehicles about congestion at a crossing. Actions derived from that information might include slowing down or having the GPS recalculate the route to avoid congestion.
The object detection system may include at least one additional sensor system available on the autonomous vehicle or a database connection in conjunction with the RADAR unit. The combined object detection, classification, and imaging system may be used as real-time sensors and as (real-time) mapping dedevicesor single or multiple-vehicle use through a direct vehicle-to-vehicle (V2V) or other connectivity solution.
Further, considering the example of the pedestrian on the sidewalk or the bicycle rider at the side of the road, once the RADAR unit has detected something of interest, this can be used in conjunction with other sensors, a LiDAR device, for instance. In such a case, the visible field for the LiDAR could be reduced to 1/100 of its usual Field of View (FOV) and the elevation angle by another 1/10 to 1/100 of its original FOV reducing the computation needed for the LiDAR by 1/1000 to 1/10000.
Further, at least one ultra-low phase-noise frequency synthesizer comprises at least one fixed frequency multiplier configured to receive and multiply at least one output signal generated by at least one main PLL by a predefined factor to generate at least one final output signal of at least one absolute output frequency. At least one ultra-low phase-noise frequency synthesizer is implemented on the same or separate electronic circuitry. Further, the ultra-low phase-noise frequency synthesizer may be used to generate the up or down-converting signal of the RADAR unit.
Further, according to another embodiment of the present disclosure, a method for autonomous vehicles is disclosed. The process may include (but is not limited to): detecting the presence of one or more objects in one or more directions by a RADAR unit. Herein, the RADAR unit comprises a transmitter for transmitting at least one radio signal to one or more objects; and a receiver for receiving at least one radio signal returned from one or more things. Further, the method may include performing at least one ultra-low phase-noise frequency synthesizer for refining the transmitted signal and not adding phase-noise to the received signals, thereby determining a phase-noise and maintaining the quality of the transmitted and the received radio signals.
Herein, the method may include various steps such as receiving and multiplying, by the ultra-low phase-noise frequency synthesizer, at least one output signal by a predefined factor to generate at least one final output signal of at least one absolute output frequency. Further, the method may develop the converting or down-converting of movement of the RADAR unit. Furthermore, the process may determine the presence of a slow-moving target despite the tiny Doppler frequency shift. Again further, the process may include determining the presence of a close-range target despite the short signal travel time. Additionally, the method may determine the distance and direction of each of one or more objects. Furthermore, the process may select the type of material an object comprises. Also, the method may include activating one or more additional sensors for the operation thereof in conjunction with the RADAR unit. The process may determine the characteristics of two close objects irrespective of the size of the objects. Further, the way may differentiate between two or more types of things when one object visually obscures another. Additionally, the process may improve techniques such as compressed sensing, micro-Doppler classification, and object classification according to its electromagnetic characteristics.
According to an embodiment of the present disclosure, a detection system with a RADAR unit, which is perfectly coupled to at least one ultra-low phase-noise frequency synthesizer, is provided. The RADAR unit is configured to detect objects in one or more directions. Herein, the RADAR unit comprises a transmitter for transmitting at least one radio signal; and a receiver for receiving at least one radio signal returned from one or more objects/targets. Further, the detection system may include at least one ultra-low phase-noise frequency synthesizer that may be configured for refining the returning of at least one radio signal to reduce phase noise.
Herein, the system includes at least one ultra-low phase-noise frequency synthesizer configured to determine phase-noise and quality of the transmitted and the received at least one radio signal. The ultra-low phase-noise frequency synthesizer is a critical part of a System, regardless of how it is implemented. The ultra-low phase-noise frequency synthesizer comprises one main PLL (Phase Lock Loop) and one reference sampling PLL. The main PLL shall consist of one high-frequency DDS (Direct Digital Synthesizer), one Digital Phase Frequency Detector, one primary VCO (Voltage Controlled Oscillator), one internal frequency divider, one output frequency divider or multiplier, and one down convert mixer. The reference sampling PLL comprises one reference clock, one sampling phase detector, one digital phase/frequency detector, and one reference VCO. This embodiment provides a vast and critical improvement in the overall system output phase-noise. The synthesizer design is based on the following technical approaches—a) using of dual loop approach to reduce frequency multiplication number, b) using sampling PLL as the reference PLL to make its noise contribution negligible, c) using DDS to provide high-frequency input to the main PLL, and d) using a high-frequency Digital Phase Frequency Detector in the main PLL.
In an additional embodiment of the present disclosure, the system includes at least one ultra-low phase-noise frequency synthesizer configured to determine phase-noise and the quality of the transmitted and received at least one radio signal. The ultra-low phase-noise frequency synthesizer comprises one main PLL (Phase Lock Loop) and one reference sampling PLL. The main PLL comprises one Fractional-N Synthesizer chip, one primary VCO (Voltage Controlled Oscillator), and one down convert mixer. The Fractional-N Synthesizer chip includes one Digital Phase Detector and one software-controllable variable frequency divider. The reference sampling PLL comprises one reference clock, one sampling phase detector, one digital phase/frequency detector, and one reference VCO. This embodiment provides multiple improvements in system output which are based on the following technical approaches—a) using a dual loop approach to reduce frequency multiplication number, b) using sampling PLL to make its noise contribution negligible, and c) using a high-frequency Fractional-N Synthesizer chip in the main PLL.
In an additional embodiment of the present disclosure, the system includes at least one ultra-low phase-noise frequency synthesizer configured to determine phase-noise and the quality of the transmitted and received at least one radio signal. The ultra-low phase-noise frequency synthesizer comprises one sampling PLL. The sampling PLL shall consist of one reference clock, one sampling phase detector, one digital phase/frequency detector, and one VCO.
According to an embodiment of the present disclosure, a detection system with a RADAR unit and an ultra-low phase-noise frequency synthesizer are provided for you. The method comprises a System on Chip (SoC) module. The RADAR unit is configured for detecting the presence or imaging of objects in one or more directions. The RADAR unit comprises a transmitter for transmitting at least one radio signal and a receiver for receiving at least one radio signal returned from one or more objects/targets. In an embodiment, the Transmit and receive signal frequencies might be equal. For example, if no Doppler effect exists, the signal frequencies may be similar. In an embodiment, the transmit and receive frequencies might also be different, for example, in cases where the Doppler effect is present. The ultra-low phase-noise frequency synthesizer comprises one main PLL (Phase Lock Loop) and one reference sampling PLL. The main PLL comprises one Fractional-N Synthesizer chip, one primary VCO (Voltage Controlled Oscillator), and one down convert mixer. The Fractional-N Synthesizer chip includes one Digital Phase Detector and one software-controllable variable frequency divider. The reference sampling PLL comprises one sampling PLL and one reference VCO. This embodiment provides multiple improvements in system output which are based on the following technical approaches—a) using a dual loop approach to reducing frequency multiplication numbers, b) using sampling PLL to make its noise contribution negligible, and c) using a high-frequency Fractional-N Synthesizer chip in the main PLL.
In an additional embodiment of the present disclosure, a vehicle having a detection system is disclosed. The detection system may be implemented for detecting information corresponding to one or more objects, the detection unit comprising: a RADAR unit for transmitting radio signals and further for receiving the returned radio signal(s) from one or more objects/targets; and at least one ultra-low phase-noise frequency synthesizer for refining the returned calls to reduce the effect of phase-noise in the returned radio signals. Further, the detection unit comprises a processor for processing the refined signals to determine one or more characteristics corresponding to one or more objects, the processor selecting one or more actions based on one or more factors, and the one or more attributes corresponding to one or more objects. The processor may further define one or more steps being adaptable by the vehicle based on one or more characteristics that may originate from the RADAR system and in conjunction with information from another sensor. The vehicle further includes one or more components communicably coupled to the processor for performing the determined one or more actions.
The detection system may include a memory for storing information and characteristics corresponding to one or more objects and actions the vehicle performs.
Hereinabove, at least one ultra-low phase-noise frequency synthesizer may be implemented in a way that is described further in the detailed description of this disclosure. Further, the RADAR unit comprises at least one: traditional single antenna RADAR, dual or multi-antenna RADAR, synthetic aperture RADAR, and one or more other RADARs. Further, in an embodiment, the processor may determine the phase shift in frequencies of the transmitted and returned radio signals. Such phase shift (difference in phase-noise frequency) may further be analyzed in light of the progressive radio signal frequency to self-evaluate the detection system’s overall performance (or a specific version of the ultra-low phase-noise frequency synthesizer).
The following is a simplified summary to help you understand some aspects of the present disclosure. This summary is neither an extensive nor exhaustive overview of the present disclosure and its various embodiments. The summary presents selected concepts of the embodiments of the present disclosure in a simplified form as an introduction to the more detailed description shown below. As will be appreciated, other embodiments of the present disclosure are possible using, alone or in combination, one or more of the features set forth above or described in detail below.
Current Radar sensors in existing vehicles provide coarse information about the vehicle’s surroundings. This invention discloses a system that utilizes a new and innovative frequency generation mechanism (Synthesizer) to significantly improve the Radar sensor performance. The spectral purity (Phase Noise) of a radar system’s local oscillator (LO) is usually perceived as a parameter that cannot be manipulated or improved. In addition, many radar systems today implement a single LO that serves the transmit and receive paths so that the phase noise close to the carrier frequency is assumed to be canceled by self-correlation. This assumption originates because near-field echoes, with a short travel time, will find the LO approximately in the same state as during transmission. As a result, the phase noise is partially canceled out for low-frequency deltas around the LO.