Apparatus and method

Abstract

A method for validating a RADAR model having a transmit chain, a receive chain, and a data processing chain corresponding to a RADAR system, the method being implemented by a computer having a processor and memory, the method comprising: acquiring output obtained from a RADAR system in an environment including a target and optionally clutter (2802); creating a RADAR model corresponding to the RADAR system (2804); determining simulated output of the RADAR model for an environment including a target and optionally clutter by simulating the RADAR model (2806); and validating the RADAR model by comparing the simulated output with the acquired output (2808).

Description

[Technical Field]

[0001] This invention relates to a RADAR system. In particular, this invention relates to the simulation of a RADAR system and / or the validation of a simulated RADAR system. [Background technology]

[0002] A conventional RADAR system includes, for example, a transmitter (i.e., a transmit chain) configured to emit radio frequency (RF) signals (i.e., radio waves also known as radar signals) into an environment containing targets (and optionally clutter), and a receiver (i.e., a receive chain) configured to receive the respective reflected RF signals from the targets (and / or optionally clutter). In this way, the targets can be detected, for example, by a data processor (i.e., a data processing chain) using the respective received reflected RF signals.

[0003] The development of RADAR systems needs improvement. [Overview of the Initiative]

[0004] The first aspect is a method for simulating a RADAR system defined by a corresponding RADAR model comprising a transmit chain, a receive chain, and a data processing chain, the method being implemented by a computer comprising a processor and memory, the method The transmission chain transmits radio frequency (RF) signals into an environment containing clutter, both targeted and optionally. To calculate the reflected RF signals of the RF signals transmitted from the target and an optional clutter, The receiving chain receives each reflected RF signal, The data processing chain uses each received reflected RF signal to detect the target, Determining a simulated output of a RADAR system using the result of detecting a target A method is provided that includes:

[0005] A second aspect is a method of providing a RADAR system, the method being at least partially implemented by a computer comprising a processor and a memory, the method comprising: Simulating a RADAR model comprising a transmit chain, a receive chain, and a data processing chain corresponding to the RADAR system; Providing a RADAR system using the result of simulating the RADAR model; A method is provided that includes:

[0006] A third aspect provides a RADAR system provided by the method according to the second aspect.

[0007] A fourth aspect provides a computer comprising a processor and a memory configured to implement the method according to any one of the first aspect and / or the second aspect, a computer program comprising instructions that, when executed by a computer comprising a processor and a memory, cause the computer to execute the method according to any one of the first aspect and / or the second aspect, or a non-transitory computer-readable storage medium comprising instructions that, when executed by a computer comprising a processor and a memory, cause the computer to execute the method according to any one of the first aspect and / or the second aspect.

[0008] A fifth aspect is a method of validating a RADAR model comprising a transmit chain, a receive chain, and a data processing chain corresponding to a RADAR system, the method being implemented by a computer comprising a processor and a memory, the method comprising: Obtaining an output obtained from a RADAR system in an environment including a target and optionally clutter; Creating a RADAR model corresponding to the RADAR system; By simulating the RADAR model, we can determine the simulated output of the RADAR model for the target and optionally for the environment, including clutter. The RADAR model is validated by comparing the simulated output with the acquired output. To provide a method that includes [this].

[0009] A sixth aspect is a method for providing a RADAR system, the method being at least partially implemented by a computer having a processor and memory, the method is To validate a RADAR model that includes a transmit chain, a receive chain, and a data processing chain, which are compatible with the RADAR system. To provide a RADAR system using the results of validating the RADAR model. To provide a method that includes [this].

[0010] The seventh aspect provides a RADAR system provided by the method of either the fifth aspect and / or the sixth aspect.

[0011] The eighth aspect provides a computer having a processor and memory configured to implement the method described in either the fifth aspect and / or the sixth aspect, a computer program having instructions that, when executed by the computer having a processor and memory, cause the computer to perform the method described in either the fifth aspect and / or the sixth aspect, or a non-transient computer-readable storage medium having instructions that, when executed by the computer having a processor and memory, cause the computer to perform the method described in either the fifth aspect and / or the sixth aspect. [Detailed description of the invention]

[0012] The first aspect is a method for simulating a RADAR system defined by a corresponding RADAR model comprising a transmit chain, a receive chain, and a data processing chain, the method being implemented by a computer comprising a processor and memory, the method The transmission chain transmits radio frequency (RF) signals into an environment that includes clutter, both targeted and optionally. To calculate the reflected RF signals of the RF signals transmitted from the target and an optional clutter, The receiving chain receives each reflected RF signal, The data processing chain uses each received reflected RF signal to detect the target, and uses the result of target detection to determine the simulated output of the RADAR system. To provide a method that includes [this].

[0013] In one example, transmitting an RF signal through a transmission chain into a target and / or optionally clutter-filled environment comprises generating a continuous RF signal having a provided amplitude envelope, optionally converting the continuous RF signal into a pulse amplitude modulated RF signal, and optionally amplifying the pulse amplitude modulated RF signal.

[0014] In one example, a RADAR model includes a transmit phase-locked loop (PLL) and generates a continuous RF signal with a provided amplitude envelope, or it includes generating a continuous RF signal using the PLL.

[0015] In one example, the RADAR model includes an envelope generator and generates a continuous RF signal having a provided amplitude envelope, which involves providing the amplitude envelope using the envelope generator.

[0016] For example, a RADAR model includes a vector modulator and a function to convert a continuous RF signal to a pulse amplitude modulated RF signal, or a function to convert a continuous RF signal to a pulse amplitude modulated RF signal using a vector modulator.

[0017] In one example, the RADAR model includes an amplifier to amplify the pulse amplitude modulated RF signal, or it includes using the amplifier to amplify the pulse amplitude modulated RF signal.

[0018] In one example, a RADAR model includes a transmitter and transmits an RF signal to a target and optionally to an environment containing clutter, and includes the use of a transmitter.

[0019] In one example, the objective includes and / or is a moving target or a stationary target, and optionally, the clutter includes and / or is stationary clutter.

[0020] For example, calculating the reflected RF signals of RF signals transmitted from a target and an optional clutter comprises the following:

[0021] In one example, receiving each reflected RF signal by a receiving chain includes receiving each reflected RF signal of the RF signals transmitted from the target and optional clutter, optionally adding thermal noise to each received reflected RF signal, optionally amplifying each received reflected RF signal, optionally downconverting each received reflected RF signal to, for example, a first intermediate frequency (IF1) signal, optionally filtering the IF1 signal, and optionally downconverting the IF1 signal to a second intermediate frequency (IF2) signal.

[0022] In one example, the detection chain detects a target using each received reflected RF signal, and the data processing chain processes each received reflected RF signal, which includes one or more of the following: performing a Hilbert transform on each received reflected RF signal, matching filtering on each received reflected RF signal, windowing on each received reflected RF signal, performing a Fast Fourier Transform (FFT) on each received reflected RF signal, and generating a range Doppler plot.

[0023] In one example, this method involves adapting a RADAR model, such as a transmit chain, a receive chain, and a data processing chain.

[0024] For example, updating a RADAR model involves updating the RADAR model using the simulated output of the RADAR system.

[0025] For example, this method modifies target and / or optional clutter. The calculation of the reflected RF signals of the RF signals transmitted from the target and the optional clutter comprises calculating the reflected RF signals of the RF signals transmitted from the modified target and the optional modified clutter.

[0026] In one example, the transmitting block includes a quantum oscillator.

[0027] A microwave generator unit (MGU) is a comprehensive device capable of generating signals in the microwave domain by phase-coherent splitting of high-fidelity optical signals using frequency comb technology. The MGU includes a frequency comb for down-converting optical signals and an ultra-stable optical system that serves as a reference for the frequency comb. The frequency comb can also be referenced by an external optical signal. The frequency comb is a pulsed mode-locked laser that generates uniformly spaced frequency outputs in the frequency domain. Mode-locking is a technique for generating pulsed lasers on the order of picoseconds or femtoseconds. The uniformly spaced frequency outputs, known as "teeth," are the repetition rate frequency (f) of the pulsed laser. rep They are separated by ). The frequency of each tooth is equal to the number of teeth (number of modes) multiplied by the spacing between teeth. The phase change between laser pulses is the carrier envelope offset frequency (f ceo We introduce another parameter called ).

[0028] MGUs can generate frequencies in both the optical and RF domains. RF signals generated by MGUs have low phase noise characteristics due to the conversion of optical signals to RF. MGUs also have the potential to lock to optical atomic references and convert the ultra-high precision and stability of optical atomic clocks in the RF domain. MGUs are used in applications including deep space navigation, precision measurement, telecommunications, and next-generation wireless communications. The low phase noise RF output makes MGUs a potential candidate for realizing quantum oscillators in radar. While an MGU-generated RF source locked to an optical atomic clock represents the ultimate realization of a quantum oscillator for radar applications, an RF signal generated from an MGU locked to its cavity-stabilized internal laser is referred to in the paper as one manifestation of a quantum oscillator.

[0029] Atomic clocks can be subdivided into two categories based on frequency: microwave and optical. Optical atomic clocks are oscillators with the highest level of frequency stability. There are two main categories of optical clocks: the first category uses one or more trapped ions, and the second category uses a number of neutral atoms in a lattice of light with a “magic” wavelength. Optical atomic clocks mainly consist of a local oscillator and an atomic reference. The local oscillator provides an optical frequency controlled against the atomic reference. A carefully generated sample of atoms or a single ion typically has some “forbidden” electronic transitions that serve as the atomic reference. Electronic transitions that are forbidden under the electric dipole approximation are known as forbidden transitions. Forbidden transitions can be enabled by applying an external magnetic field. These forbidden transitions have very narrow linewidths, making them useful and well-defined frequency references. By tuning the frequency of the local oscillator (laser) to the optical atomic resonance frequency, the optical clock references the atomic transitions. The highest frequency stability of optical atomic clocks is due to the narrow linewidth of the transitions. The RF output of an MGU locked to an optical atomic clock is another potential candidate for realizing a quantum oscillator in radar.

[0030] A second aspect is a method for providing a RADAR system, the method being at least partially implemented by a computer having a processor and memory, the method is To simulate a RADAR model that includes a transmit chain, a receive chain, and a data processing chain, corresponding to a RADAR system, To provide a RADAR system using the results of simulating a RADAR model and To provide a method that includes [this].

[0031] The method according to the second embodiment may include any of the steps described with respect to the first embodiment.

[0032] In one example, this method includes updating the RADAR model based on the results of validating the RADAR model.

[0033] In one example, this method includes validating an updated RADAR model.

[0034] A third aspect provides a RADAR system provided by the method according to the second aspect.

[0035] A fourth aspect provides a computer having a processor and memory configured to implement the method described in either the first aspect and / or the second aspect, a computer program having instructions that, when executed by the computer having a processor and memory, cause the computer to perform the method described in either the first aspect and / or the second aspect, or a non-transient computer-readable storage medium having instructions that, when executed by the computer having a processor and memory, cause the computer to perform the method described in either the first aspect and / or the second aspect.

[0036] A fifth aspect is a method for validating a RADAR model comprising a transmit chain, a receive chain, and a data processing chain corresponding to a RADAR system, the method being implemented by a computer comprising a processor and memory, the method The objective is to acquire output from RADAR systems in environments including clutter, and optionally, Creating a RADAR model compatible with the RADAR system, By simulating the RADAR model, we can determine the simulated output of the RADAR model for the target and optionally for the environment, including clutter. The RADAR model is validated by comparing the simulated output with the acquired output. To provide a method that includes [this].

[0037] The method according to the fifth embodiment may include any of the steps described in relation to the first embodiment and / or the second embodiment.

[0038] In one example, comparing the simulated output with the acquired output involves comparing the signal power and / or signal-to-noise ratio (SNR) values ​​of the transmit chain, receive chain, and / or data processing chain to their expected values.

[0039] For example, comparing the signal power and / or signal-to-noise ratio (SNR) values ​​of the transmit chain, receive chain, and / or data processing chain to their expected values ​​comprises comparing the signal power and / or signal-to-noise ratio (SNR) values ​​of the outputs of the transmit chain, receive chain, and / or data processing chain to their expected values.

[0040] For example, comparing the simulated output with the acquired output involves comparing the basic parameters of the simulated output with the basic parameters of the acquired output.

[0041] For example, comparing the basic parameters of the simulated output with the basic parameters of the acquired output involves comparing the basic parameters of the generated range Doppler plot of the simulated output with the basic parameters of the range Doppler plot of the acquired output.

[0042] In one example, comparing the simulated output with the acquired output involves comparing the clutter-to-noise ratio (CNR), signal-to-noise ratio (SNR), and / or thermal noise floor of the simulated output and the acquired output.

[0043] In one example, comparing the CNR, SNR, and / or thermal noise floor of the simulated output and the acquired output comprises comparing the CNR, SNR, and / or thermal noise floor of the generated range Doppler plot of the simulated output and the range Doppler plot of the acquired output.

[0044] In one example, CNR takes thermal noise into account.

[0045] In one example, this method is Adapting the RADAR model, for example, the transmit chain, receive chain, and data processing chain, Optionally, modify the target and / or optional clutter. The calculation of the reflected RF signals of the RF signals transmitted from the target and the optional clutter comprises calculating the reflected RF signals of the RF signals transmitted from the modified target and the optional modified clutter.

[0046] In one example, the transmitting block includes a quantum oscillator.

[0047] A sixth aspect is a method for providing a RADAR system, the method being at least partially implemented by a computer having a processor and memory, the method is To validate a RADAR model that includes a transmit chain, a receive chain, and a data processing chain, which are compatible with the RADAR system. To provide a RADAR system using the results of validating the RADAR model. To provide a method that includes [this].

[0048] The method according to the sixth aspect may include any of the steps described with respect to the first aspect, the second aspect, and / or the fifth aspect.

[0049] In one example, this method involves fitting the RADAR model based on the results of validating the RADAR model.

[0050] In one example, this method includes validating the adapted RADAR model.

[0051] The seventh aspect provides a RADAR system provided by the method of either the fifth aspect and / or the sixth aspect.

[0052] The eighth aspect provides a computer having a processor and memory configured to implement the method described in either the fifth aspect and / or the sixth aspect, a computer program having instructions that, when executed by the computer having a processor and memory, cause the computer to perform the method described in either the fifth aspect and / or the sixth aspect, or a non-transient computer-readable storage medium having instructions that, when executed by the computer having a processor and memory, cause the computer to perform the method described in either the fifth aspect and / or the sixth aspect. definition

[0053] Throughout this specification, the terms “comprising” or “comprises” mean including the specified component(s) but not excluding the presence of other components. The terms “consisting essentially of” or “consists essentially of” mean including the specified component(s) but excluding other components, except for materials present as impurities, unavoidable materials present as a result of processes used to provide the components, and components such as colorants added for purposes other than achieving the technical effects of the present invention, and similar items.

[0054] The terms "consisting of" or "consists of" mean that the specified components are included but other components are excluded.

[0055] Where appropriate, depending on the context, the use of the terms “comprises” or “comprising” may also be interpreted to mean “consist essentially of” or “consisting essentially of,” and also to mean “consist of” or “consisting of.”

[0056] The optional features described herein may be used individually or in combination with each other, as appropriate, particularly in combinations as described in the appended claims. The optional features of each aspect or illustrative embodiment of the Invention as described herein are, as appropriate, also applicable to all other aspects or illustrative embodiments of the Invention. In other words, a person skilled in the art reading this specification should consider the optional features of each aspect or illustrative embodiment of the Invention to be interchangeable and combinable between different aspects and illustrative embodiments.

[0057] References to the accompanying figures are made only as examples, in order to better understand the present invention and to illustrate how illustrative embodiments of the present invention may be carried out. [Brief explanation of the drawing]

[0058] [Figure 1] A schematic diagram of the overall front-end model of the radar model in a simulation is shown using an illustrative embodiment. The front-end comprises four main sections. The transmit chain transmits an amplified pulsed sinusoidal signal to the environment through the transmit antenna. Clutter and targets in the environment reflect the signal to the radar receive antenna. In the receive chain, the received signal undergoes amplification and down-conversion stages to generate an IF signal. The IF signal is converted to a baseband signal in the data processing chain. The baseband signal undergoes further data processing stages to improve the signal-to-noise ratio of the target. [Figure 2] A typical PLL comprising a reference oscillator, phase detector, filter, VCO, and frequency divider is schematically illustrated according to an illustrative embodiment. The phase detector compares the output of the reference oscillator with the output of the VCO. The output of the phase detector is used to optimize the phase of the VCO until the VCO is locked to the reference oscillator. The frequency divider is used to divide the VCO output frequency, and the filter is used to filter out noise. [Figure 3] A schematic diagram illustrates a charge pump integer N PLL for generating a phase-locked 1 GHz signal using a 10 MHz reference signal. The VCO output is divided by a factor of 100 and compared to the reference oscillator in the PFD. The UP and DOWN signals at the PFD output drive the charge pump. The charge pump output is forwarded through a loop filter to the VCO control input. The PLL is finally locked when both the reference signal and the divided signal are synchronized with each other. [Figure 4] (a) A 10 MHz pulsed signal at the input of the PLL, and (b) a time-domain representation of a 1 GHz phase-locked VCO signal at the output of the PLL are shown. [Figure 5] The power spectrum of a 1 GHz phase-locked VCO signal at the output of the PLL is shown. [Figure 6] This shows the time-domain representation of the signal at the output of different blocks in the transmit chain. This representation shows only a partial section (10 seconds) from the complete PRI. (a) Envelope generator output showing the amplitude envelope for approximately 1 second, (b) Vector modulator output showing the continuous sinusoidal signal inside the amplitude envelope. The transmit frequency in the L band makes it difficult to decompose the sinusoidal signal in the representation. The particular shape of the vector modulator output is an artifact of the envelope generator's sampling rate. [Figure 7]The power spectra of the signals at the output of the amplifier and transmitting antenna are shown. The power spectrum at the amplifier output peaks at 7.9 dB, which is equal to a signal power of 33 dB. The power spectrum at the output of the transmitting antenna peaks at 20.4 dB, which is equal to a signal power of 45.5 dB. The transmitting antenna gain is equal to 12.5 dB. The side lobes in the power spectrum are artifacts of the envelope generator's sampling rate. [Figure 8] This shows the time-domain representation of the signal at the antenna output, exhibiting a 4-second time delay equivalent to a 600m range. This representation shows only a partial section (10 seconds) from the complete PRI. (a) Signal at the output of the transmitting antenna, and (b) Signal at the output of the receiving antenna. [Figure 9] The histogram shows the thermal noise at the output of the thermal noise block in the simulation. The histogram shows a Gaussian noise distribution that peaks at -107 dB for bandwidths of 5 GHz and 290 K, respectively, and temperature. [Figure 10] The power spectra of the signals at the outputs of the receiving antenna and thermal noise block are shown. The power spectrum at the output of the receiving antenna peaks at -99.6 dB, which is equal to the received signal power of -74.5 dB. The power spectrum at the output of the thermal noise block shows a uniform thermal noise floor at -137 dB. Since the spectrum has an RBW of 5 MHz, -137 dB is equal to the expected thermal noise power of -107 dB. [Figure 11] The power spectra at the outputs of the thermal noise block and LNA are shown. The power spectrum at the output of the LNA is amplified to -35.1 dB, which is equal to the peak power of -10 dB. The noise floor is amplified to -68 dB, which is equal to the noise power of -38 dB. The SNR at the output of the LNA is smaller than the SNR at the output of the thermal noise block by a factor of 4.5 dB (equal to the LNA NF). [Figure 12(a)]The histograms at the output of the thermal noise block show peak signal and noise power values, with (a) being the histograms at the output of the thermal noise block showing signal power and noise power at -74.5 dB and -107 dB, respectively. [Figure 12(b)] The histograms at the output of the LNA show the peak signal and noise power values, and (b) shows the histograms at the output of the LNA showing the signal power and noise power at -10dB and -38dB, respectively. [Figure 13] A schematic diagram of a typical mixer, including RF and LO inputs and an output, is shown. The output includes two frequencies, the sum of the two input frequencies, and the difference between the two input frequencies. [Figure 14] The power spectra of the signals at the outputs of the LNA and mixer are shown. The signal peak at the mixer output is reduced by 6 dB compared to the peak at the LNA output. The noise power at the mixer output is reduced by 3 dB compared to the noise power at the LNA output. The reduction in signal and noise power values ​​is as expected. [Figure 15(a)] The histograms at the mixer output show peak signal and noise power values, with (a) being the histograms at the mixer output showing signal power and noise power at -10 dB and -41 dB, respectively. [Figure 15(b)] The image shows histograms at the output of the BPF, indicating peak signal and noise power values. (b) shows histograms at the output of the BPF, indicating signal power and noise power at -16 dB and -61 dB, respectively. [Figure 16] A typical ADC, including a sample block, hold block, quantization block, and encoder block, is schematically illustrated. The input to the ADC is sampled at the required frequency in the sample block and stored in the hold block until the next sample. The signal is quantized using the quantization block. The digital signal at the output of the quantization block is converted to binary format in the binary block. [Figure 17]The histograms at the ADC output show the signal power and noise power at -16dB and -61dB, respectively. [Figure 18(a)] A schematic diagram of the data matrix in the data processing chain of a radar system is shown, where (a) is a data matrix containing a single PRI on the high-speed time axis and a stack of PRIs on the low-speed time axis. [Figure 18(b)] A schematic diagram of the data cube in the data processing chain of a radar system is shown, where (b) is a data cube containing data matrices from each receiving channel stacked on top of each other. [Figure 19] (a) The time-domain representation of the signal at the input of the matched filter, and (b) the time-domain representation of the signal at the output of the matched filter. [Figure 20] The histograms at the output of the matched filter show the signal power and noise power at 79 dB and 24.5 dB, respectively. [Figure 21(a)] The simulated range Doppler plot of a stationary test target at 600m is shown, including 15 range bins and a central Doppler bin equal to 1400Hz, where (a) is a range Doppler plot without Blackman-Harris windowing showing a target peak power of 145dB and an average noise power of 56.5dB, equal to an SNR of 88.5dB. [Figure 21(b)] (b) shows a simulated range Doppler plot of a stationary test target at 600m, including 15 range bins and a central Doppler bin equal to 1400Hz. [Figure 22] A simulated range-Doppler plot is shown, with the range bin on the vertical axis and the Doppler frequency on the horizontal axis. The spectrum is shown for -700 to +700 Hz. A single stationary clutter and a single Doppler target are highlighted in green and red boxes, respectively. The range-Doppler plot is normalized for clutter. [Figure 23(a)]This shows a comparison between simulated range Doppler plots and actual range Doppler plots. Both range Doppler plots are normalized for the strongest clutter. The target is highlighted in the red box. The target is present across both plots in the same range and Doppler bin. (a) is a simulated range Doppler plot without oscillator phase noise. The plot includes stationary clutter and a single simulated target with a uniform thermal noise floor. It can be seen that the phase noise floor emerges from the thermal noise floor for range bins with higher clutter. The plot also includes other unwanted targets, such as those seen by radar. [Figure 23(b)] The image shows a comparison between simulated and actual range Doppler plots. Both range Doppler plots are normalized for the strongest clutter. Targets are highlighted in red boxes. Targets are present across both plots in the same range and Doppler bin. The plots include stationary clutter and a single simulated target in a uniform thermal noise floor. (b) is an actual range Doppler plot from a gaze radar trial at an airfield. It can be seen that the phase noise floor emerges from the thermal noise floor for range bins with higher clutter. The plots also include other unwanted targets as seen by radar. [Figure 24] A comparison graph is shown, displaying clutter power and thermal noise floor for all range bins. The comparison graph includes data from both actual and simulated range Doppler plots. The clutter power for both the simulation and actual data overlaps to a considerable extent. The thermal noise floor in the simulation completely overlaps with the actual data and is therefore indistinguishable. [Figure 25(a)] A simulated range Doppler plot with classical oscillator phase noise is shown. [Figure 25(b)]The image shows actual range Doppler plots from gaze radar trials with classical oscillator phase noise. The range Doppler plots also consist of other unwanted targets, such as those seen by the radar. [Figure 25(c)] This shows a comparison between a simulated range Doppler plot with classical oscillator phase noise and an actual range Doppler plot. It can be seen that the phase noise floor rises above the thermal noise floor for range bins with higher clutter power. The target of interest is highlighted in the red box. [Figure 26] A comparative graph is shown for both simulated and actual range Doppler plots with classical oscillator phase noise, displaying peak clutter power, overall noise floor, and thermal noise floor for each range bin. The overall noise floor for both the simulation and actual data overlaps to a considerable extent. The overall noise floor for range bins closer to the radar increases by approximately 25 dB compared to the thermal noise floor. The thermal noise floor in the simulation completely overlaps with the actual data and is therefore indistinguishable. [Figure 27] A schematic diagram illustrating the method according to an illustrative embodiment. [Figure 28] A schematic diagram illustrating the method according to an illustrative embodiment. [Modes for carrying out the invention]

[0059] There are many radar architectures in the world today, and the radar architecture depends on the application of the radar. Some common radar architectures include frequency-modulated continuous-wave radar, pulsed Doppler radar, and pulse-compressed radar. Any common radar includes a transmit chain, a receive chain, and a data processing chain. A good radar model is one that can simulate the building blocks in the transmit chain, receive chain, and data processing chain, along with environmental goals and clutter models. The radar model developed and discussed herein is based on the University of Birmingham's L-band gaze pulsed Doppler radar with a reasonable number of simplifications and focuses on modeling the behavior of the hardware building blocks.

[0060] There are several platforms on which radar can be modeled, including MATLAB® and LabVIEW. The radar model discussed herein was developed from scratch using a bottom-up approach in MATLAB's graphical interface, known as MATLAB and Simulink. Most of the building blocks in the radar were modeled in Simulink to develop a user-friendly radar model with the ability to optimize all parameters based on requirements. The front end of the entire simulated radar model is given in Figure 1. The radar model can be divided into four main sections as follows: • Transmission chain ·environment • Receiving chain • Data processing chain

[0061] The components within the transmit chain, environment, and receive chain were modeled as separate system-level building blocks in Simulink. Each radar building block included further subblocks to appropriately represent the functionality of the radar components in the model. The Simulink blocks representing the radar components were connected to run simulations against the complete radar model. Test probes were placed to evaluate the signals at the inputs and outputs of all building blocks in the simulation, both in the time domain and the frequency domain. Data at the output of the last block in the receive chain was imported into the MATLAB workspace. The data processing chain was implemented in MATLAB using separate MATLAB code.

[0062] The front-end transmit chain of the radar model included a transmit phase-locked loop (PLL) for generating the transmit RF signal and an envelope generator for providing the amplitude envelope. The continuous RF signal generated at the output of the transmit PLL was converted to a pulse amplitude modulated RF signal with the help of a vector modulator. The RF signal was then amplified using an amplifier and transmitted to the environment using a transmit antenna.

[0063] The environment included target and clutter models for reflecting the transmitted RF signal toward the receiving antenna. In the receiving chain, the signal reflected from the environment was collected by the receiving antenna. A thermal noise block after the receiving antenna added Gaussian thermal noise to the simulation. An LNA amplified the received signal at the output of the thermal noise block. A combination of a receiving PLL block and a mixer block down-converted the received RF signal to a first intermediate frequency (IF1) signal. The IF1 signal was filtered using a BPF to remove all unwanted frequencies. An ADC placed at the output of the BPF down-converted the IF1 signal to a second intermediate frequency (IF2). The ADC was used only for frequency down-conversion because the signal was already in the digital domain.

[0064] Several data processing methods were performed on the signal at the ADC output during the data processing chain. The data processing chain included Hilbert transform and matched filtering, windowing, FFT, and range Doppler plot generation. Matched filtering was used to improve the SNR in the presence of additive noise. In the radar domain, matched filtering is often referred to as pulse compression. FFT was used to generate a range Doppler plot from the time-domain signal and perform pulse integration to improve the SNR. Spectral leakage due to the Fourier transform was reduced through windowing. 2 Transmission Chain

[0065] The transmit chain of the entire simulated radar model included components for performing signal generation, signal modulation, signal amplification, and signal transmission. One of the key aspects of the radar transmit chain is the generation of the RF transmit frequency. As discussed herein, the transmit frequency is either directly generated or synthesized using a frequency upconversion process. Very early methods for directly synthesizing RF signals used a magnetron. More recently, direct digital synthesis (DDS) has been used to generate signals directly at the required RF frequency. A typical method for synthesizing RF signals using frequency upconversion involves a mixer. Another method for frequency upconversion is to use a PLL. The RF signal was generated using a transmit PLL in the entire radar model. The Simulink simulations discussed herein also include a general standalone PLL model to verify the operating principle of the PLL. In the entire radar model, a simplified version of the transmit PLL for generating signals directly at RF frequencies was modeled due to the limitations of modeling when integrating a standalone model of the PLL.

[0066] Most radar signals undergo some form of waveform modulation. Waveform modulation can be of different types, including amplitude modulation, frequency modulation, and phase modulation, as discussed herein. The overall radar model included amplitude modulation to generate pulsed radar signals. The amplitude envelope for amplitude modulation in the overall radar model was generated by an envelope generator block, as shown in Figure 1. Amplitude modulation is generally added to the RF signal with the help of a mixer. Mixers are also used in radar for frequency up-conversion and frequency down-conversion. In the radar model, a vector modulator performs the function of a mixer. The vector modulator integrated the amplitude modulation into the RF signal. In most radars, the generated RF signal needs to be amplified to high power before transmission to the environment. The power required for transmission and amplification depends on the application of the radar. The overall radar model simulated a simple amplifier design with constant amplifier gain. The transmitting antenna design can follow different architectures depending on the application. The transmitting antenna was modeled as a single transmitting antenna with constant antenna gain in the overall radar model. 2.1 Transmit PLL

[0067] The transmit PLL generates sinusoidal RF signals in the transmit chain of the radar model implemented in the simulation. This subsection discusses the basics of PLLs, how PLLs operate in simulations, and transmit PLL design in simulations. 2.1.1 Basics of PLLs

[0068] A PLL is an electronic circuit that uses a voltage or voltage-driven oscillator to continuously adjust its frequency to match an input signal. PLLs generate, stabilize, modulate, demodulate, filter, or recover signals in "noisy" communication channels. PLLs play a crucial role in modern electronic devices, particularly RF devices. In radar, PLLs are used to generate a stable RF signal at the radar transmission frequency using a highly stable reference oscillator.

[0069] A typical PLL block diagram is shown in Figure 2. The main components of a PLL include a reference oscillator, a voltage-controlled oscillator (VCO), a phase detector, a filter, and possibly a frequency divider. The PLL operates on the principle of synchronizing the phase and frequency of the VCO with the help of the reference oscillator. A control input within the VCO adjusts the VCO frequency, and the phase detector compares the phase of the VCO with that of the reference oscillator. When the phase of the VCO is synchronized with that of the reference oscillator, this state is known as phase lock, and is therefore called a phase-locked loop. The VCO can also be locked to the reference oscillator at different reference frequencies with the help of a frequency divider placed before the phase detector, as shown in Figure 2. The frequency divider can also be placed after the reference oscillator when a fractional relationship is required between the reference oscillator and the VCO frequency.

[0070] There are different types of phase detectors. A mixer phase detector operates on the principle of mixing two input signals. The mixer performs multiplication of the two inputs, and one of the outputs of the mixer phase detector will be the sine of the phase difference between the two inputs. A mixer phase detector only detects the phase difference between the two inputs and is only effective for the same frequency or frequencies that are very close to each other.

[0071] A phase-frequency detector (PFD) is the most common type of phase detector. A PFD can operate even when the frequencies of the two input signals to the phase detector are different. The PFD operates on the principle of zero transition of the input signals, tracking the difference in phase and frequency. The output of the PFD includes UP and DOWN signals. The UP and DOWN signals indicate the direction of the frequency change required to lock the PLL. Since the voltage signal controls the VCO, the UP and DOWN signals at the PFD output need to be converted to voltage signals. One standard method of converting the UP and DOWN signals is to use a charge pump. A current source in the charge pump can be controlled by the UP and DOWN outputs from the PFD. The charge pump current is sunk to the output when the UP signal is active, and drawn from the output when the DOWN signal is active. A capacitor at the output of the charge pump converts the current fluctuations into voltage fluctuations. These voltage fluctuations are supplied to the control input of the VCO.

[0072] Filters in a PLL are primarily used to control the loop dynamics and are therefore also known as loop filters. Filters are also used to low-pass filter out noise and unwanted products at the output of the phase detector. The filter is placed before the VCO, and its output is supplied to the VCO's control input. 2.1.2 PLL operation in simulation

[0073] A typical example of charge pump integer N PLL operation for generating a 1 GHz RF signal using a stable 10 MHz reference oscillator is given in Figure 3. In the PLL simulation, the 1 GHz output of the VCO was downconverted to 10 MHz with the help of a frequency divider where N is equal to 100. The reference signal and the divided signal were compared in a PFD, which generated an error signal dependent on the phase difference between the two input signals. The error signal drove a charge pump, and the charge pump output was supplied to the control input of the VCO. Once both the reference signal and the divided signal were phase-locked, the 1 GHz PLL output signal was ultimately locked to the 10 MHz reference signal. The 10 MHz pulsed reference signal to the PLL input and the 1 GHz phase-locked sinusoidal signal at the PLL output are given in Figure 4. The spectrum of the phase-locked VCO signal at the PLL output, given in Figure 5, shows a peak at 1 GHz, confirming the operation of the PLL on the simulation platform. 2.1.3 Transmit PLL in Simulation

[0074] The ideal scenario would be to incorporate a fully functional PLL model, as described in Section 2.1.2, into the overall radar model simulation. Due to design and timing considerations, integrating a fully functional PLL into the radar model was not feasible. As an alternative approach, the transmit PLL simulated in the model was modeled as a direct digital synthesizer for generating signals directly at RF frequencies, based on the RF block set in Simulink. The transmit PLL also has the option to add phase noise to the generated signal. Phase noise is critical to radar characterization and performance, and its impact on radar systems is discussed in detail herein. The transmit PLL can provide amplitude and frequency to any user-specified values. The block also has the option to provide amplitude to both the in-phase and quadrature components of the signal. Apart from generating a signal with a specific amplitude, the signal can also be specified in terms of current and power. The output frequency of the transmit PLL was set to the transmit frequency of an L-band gaze radar. The amplitude of the generated signal was set to 1V. The simplified equation for the mathematical model of the signal at the output of the transmitting PLL is: S PLL (t)=A PLL cos[2πf T t+θ] (1) Given by, where S PLL (t) represents the sinusoidal signal at the output of the transmitting PLL, and A PLL and θ represent the amplitude and phase of a sinusoidal signal, respectively, and f T This represents the transmission frequency. 2.2 Envelope Generator and Vector Modulator

[0075] Signal modulation is one of the important aspects of a radar's transmit chain. Amplitude and frequency modulation are the two most common signal modulations used in radar. Amplitude modulation provides an amplitude envelope over the generated RF signal, and the envelope can have different shapes depending on the radar requirements. The most common amplitude modulation is the rectangular envelope. In linear frequency modulation, the transmit signal frequency is modulated linearly. Linear frequency modulation is used to achieve high range resolution and a large detection range simultaneously by using the increased bandwidth of the transmit signal. An envelope generator is one method for generating the modulation required for a radar transmit signal. A vector modulator is essentially a mixer for adding baseband modulation to an RF signal. The vector modulator architecture is similar to that of a general mixer architecture. 2.2.1 Envelope Generator in Simulation

[0076] In the overall radar model, an envelope generator was used to provide the amplitude envelope required for the signal generated by the transmit PLL. The envelope generator can generate any user-defined amplitude envelope. The envelope generator may also include further subblocks to enable phase modulation and frequency modulation. In the simulation, the envelope generator produced a shaped envelope with a pulse width of approximately 1 second and a PRI of approximately 136 seconds. The envelope generator was implemented as a block capable of providing amplitude values ​​at a user-specified sampling rate. The amplitude values ​​were loaded into MATLAB as a mat file and read by Simulink at the specified sampling rate. The amplitude envelope at the output of the envelope generator is given in Figure 6(a). 2.2.2 Vector modulators in simulations

[0077] In the overall radar model, the vector modulator mixes the continuous sine wave signal at the output of the transmit PLL with the amplitude envelope at the output of the envelope generator. The vector modulator is realized in simulation using an RF block set mixer. The mathematical model of the signal at the output of the vector modulator is S V (t)=A V cos[2πf T t+θ] (2) where S V (t) represents the signal at the output of the vector modulator, and A V , f T , and θ represent the amplitude, frequency, and phase of the signal, respectively. The signal at the output of the vector modulator was a pulse signal, but for simplicity, the time dependence of the pulse is not explicitly mentioned in Equation 2. Therefore, Equation 2 is a simplified mathematical signal model for the signal at the output of the vector modulator. The pulsed RF signal at the output of the vector modulator is shown in Fig. 6(b). Fig. 6(b) clearly shows the amplitude envelope over the sine wave RF signal. The specific shape of the vector modulator output is an artifact of the sampling rate of the envelope generator. 2.3 Amplifier

[0078] An RF amplifier is an electronic amplifier used in most communication systems to amplify an input signal to the required output signal. In radar, an RF amplifier is used to convert the low-power RF signal generated by the transmitter into a high-power RF transmission signal. The RF amplifier is generally located before the radar transmit antenna and drives the transmit antenna. The design parameters of an RF amplifier include gain, power output, bandwidth, input and output impedance matching, and linearity.

[0079] The gain of an RF amplifier is defined as the ratio of the output power to the input signal power. Bandwidth defines the operating signal bandwidth of the amplifier, and ideally, different amplifiers would be required depending on the signal frequency. Impedance is defined as a measure of opposition to electrical flow. Impedance is a complex-valued quantity with resistance as its real part and reactance as its imaginary part. Impedance matching is the process of making the input and output impedances equal in order to maximize signal power transfer by minimizing signal reflection. Linearity is described as the behavior of a circuit, where fluctuations in output signal strength are directly proportional to fluctuations in input signal strength. The output-to-input signal power ratio remains the same for a linear amplifier, regardless of the input signal power. 2.3.1 Amplifiers in Simulation

[0080] In the overall radar model, a simplified signal amplifier was used to amplify the input signal to an arbitrary user-defined value. The amplifier in the simulation has the option to perform both linear and nonlinear amplification. The amplifier in the simulation was set to linear amplification. Amplification coefficients in dB can also be provided to the amplifier. From the radar's power budget, the expected value of the signal power at the amplifier output is 33 dB. The power spectrum outputs the RMS value of the signal. For an ideal rectangular pulse with a width of 1 sec and a PRI of 136 sec, the RMS peak is reduced by a coefficient of 21.3 dB, resulting in a power spectrum that peaks at 11.7 dB (instead of 33 dB). Because the amplitude envelope in the simulation is not a perfect rectangle, the peak in the spectrum will be reduced by an additional 3.8 dB, resulting in a total reduction of 25.1 dB. The power spectrum of the signal at the amplifier output is given in Figure 7. In Figure 7, it can be seen that the peak power of the signal at the amplifier output is 7.9 dB, which is equal to the transmit power of 33 dB. 2.4 Transmitting Antenna

[0081] Antennas are widely used in all wireless systems. Antenna behavior depends on the components within the antenna, and antenna design depends on the application. In radar, transmitting antennas are used to broadcast RF transmission signals into the environment. Key parameters of an antenna are radiation pattern, antenna gain, and directivity.

[0082] The radiation pattern of an antenna is a description of the angular dependence of its emission, and these are a result of its three-dimensional shape. In a three-dimensional coordinate system, the horizontal plane (XY) is defined as the azimuthal plane, and the vertical plane (YZ) is defined as the elevation plane. The azimuthal angle is defined as the angle along the horizontal axis from the direction of the antenna boresight. The elevation angle is defined as the angle above the horizontal line in the vertical direction. Based on the radiation pattern, antennas can be divided into isotropic antennas and directional antennas. An isotropic antenna is a theoretical antenna pattern in which the radiated power is equal in all directions of azimuthal and elevation. In a directional antenna, the energy is dominated in one or more directions. Antenna gain is defined as the ratio of the power transmitted in the principal direction of a directional antenna to the power in an isotropic antenna. The principal direction of a directional antenna is called the radar boresight. The directivity of an antenna is defined as the maximum signal power radiated in a given direction divided by the average power in all directions. 2.4.1 Transmitting Antenna in Simulation

[0083] The transmitting antenna was designed as a simplified antenna with a constant antenna gain throughout the entire radar model. Simulink has an antenna design that can include parameters such as directivity. While simulating the simplified antenna model, constraints on the total simulation time for the entire radar simulation were taken into consideration. Depending on the application, further modifications may be incorporated into the antenna design. The antenna gain in the simulation was achieved using an amplifier as described in Section 2.3.1. The simplified mathematical model of the transmitted signal is: S T (t)=A Tcos[2πf T t+θ] (3) Given by, where S T (t) represents the transmitted signal, A T This represents the amplitude of the transmitted signal. The power spectrum of the signal at the output of the transmitting antenna is given in Figure 7. The transmitting antenna is expected to amplify the signal by 12.5 dB during transmission. The antenna gain value was selected based on the system parameters of the L-band gaze radar. From Figure 7, it can be seen that the signal at the output of the transmitting antenna peaked at 20.4 dB, which is equal to the signal power of 45.5 dB at the output of the transmitting antenna. A transmitting antenna gain of 12.5 dB is seen in Figure 7. In the simulation, the output of the transmitting antenna was connected to a block in the environment. 3 Environment

[0084] The radar environment includes both targets and clutter. A target is an object of interest in the environment, which may be stationary or moving. All moving targets have a Doppler frequency associated with them, which is used to measure the target's velocity. Radar targets include drones, aircraft, ships, cars, and birds. Clutter is generally stationary objects and may include buildings, trees, land, sea, and other geographical features within the field of view. Weather is also classified as clutter. Electromagnetic signals interact with the atmosphere, clutter, and targets, and different types of interactions are discussed herein. The interaction of electromagnetic signals with the environment is adapted to the radar range equations, as discussed herein. 3.1 Targets and Clutter in Simulations

[0085] The overall radar model simulated a simple representation of the magnitude responses of both targets and clutter. As shown in Figure 1, each subblock in the environment represents the magnitude response of a different object in the environment, which can be either a target or clutter. Targets and clutter are realized as point scatters in the overall radar model. The output of the transmit antenna block was connected to the input of the magnitude response block. Each magnitude response block was designed based on the radar range equation. The range, velocity, and RCS of each object can be defined in the magnitude response block. Ideally, the environment can represent any number of objects as point scatters with varying ranges, velocities, and RCS values. Since antenna directivity is not included in the simulation, the position of the objects corresponds to the range at the radar boresight. The simulation may include additional algorithms for positioning objects at specific angles in azimuth and elevation. The output of the magnitude response block was connected to the receive chain through an interface. The interface combined the reflected signals from all different objects in the environment and directed them to the receive chain. Atmospheric loss is currently kept at 1 in the simulation. Both the target and clutter are modeled as simple point scatterers, with the target modeled in a specific range bin and the clutter distributed across all range bins. 4 Receiving Chain

[0086] The receiver chain of a radar model includes components for performing signal reception, signal amplification, signal down-conversion, and analog-to-digital conversion. The receiving antenna receives signals reflected from the environment. The receiving antenna also provides amplification to the signal. Generally, radar receivers contain thermal noise resulting from random thermal fluctuations of electrons, as described herein. In the entire radar model, a separate block was used to generate thermal noise in the receiver chain of the radar system. The thermal noise block was located after the receiving antenna, as shown in Figure 1.

[0087] One of the key blocks in the receiving chain of the entire radar model was the receive PLL. The frequency of the signal at the output of the receive PLL was different from the frequency of the signal at the output of the transmit PLL. The frequency offset between the transmit and receive PLLs was equal to the IF. A combination of a mixer and a band-pass filter performed the down-conversion of the received signal to the IF. The output of the receive PLL was mixed with the received signal. In the entire radar model, since both signals were digital signals, a signal multiplier was used to mix the received signals. The ADC is another key block in the receiving chain of the radar system. The ADC performs the basic operation of converting an analog signal to a digital signal. Whenever the sampling rate of the ADC does not conform to the Nyquist standard, aliasing will occur, and the input signal will be represented as a down-converted signal at the output of the ADC. According to the Nyquist standard, in order to reliably reproduce the signal, the sampling frequency should be at least twice the highest frequency of the signal. The Nyquist frequency is defined as twice the highest frequency of interest. When frequencies higher than half the Nyquist frequency are sampled, those frequencies are mistakenly detected as lower frequencies; this process is known as aliasing. In the overall radar model, the signal is already in the digital domain, and the ADC performs a second down-conversion through aliasing. 4.1 Receiving antenna and thermal noise

[0088] Receiving antennas are present in most wireless communication devices. In radar, the receiving antenna is used to receive signals from the environment. There are different types of radar antenna configurations depending on the application, including a single receiving antenna or an array of receiving antenna elements. These designs apply to both transmitting and receiving antennas. An antenna array contains a collection of identical, uniformly spaced antenna elements that operate as a single unit. Signals received in individual array elements are combined in the phase relationship required to enhance signals from a desired direction. Phased array antenna systems include phase shifters to steer individual array elements in the desired direction.

[0089] Thermal noise is an unavoidable factor in all electronic devices. Thermal noise defines a lower limit for target detection, and radar range Doppler plots are always limited by the thermal noise floor. As discussed herein, thermal noise depends on the bandwidth and temperature of the radar receiving chain. Thermal noise can be reduced by reducing the temperature and bandwidth of the radar receiving chain. 4.1.1 Receiving antenna in simulation

[0090] In the overall radar model, the receiving antenna was designed as a simplified antenna with a constant antenna gain. The L-band gaze radar used for validation includes a two-dimensional array of 64 receiving antenna elements. Given the time constraints for simulating the overall radar model, the simulation currently has a single receiving antenna element. Adding more antenna elements would add more receiving chain components, and the model could accommodate more antenna elements at the expense of simulation time. The output of the magnitude response block in the environment was connected to the input of the receiving antenna through an interface. The receiving antenna gain in the simulation was achieved using an amplifier similar to that described in Section 2.3.1.

[0091] Consider a radar that transmits a series of M pulses, 0 ≤ m ≤ M-1, separated by PRI of T. o For a target with an initial range and velocity ν, the range of the target for the mth transmitted pulse is R o -νmT. For the mth transmitted pulse, the signal reaching the receiver antenna will have a time delay equal to the following:

[0092]

number

[0093] The mathematical model of a delayed received signal is:

[0094]

number

[0095] Given by, here,

[0096]

number

[0097] This is the Doppler frequency of a target moving towards the radar.

[0098]

number

[0099] This is the phase of the received signal. R This represents the amplitude of the received signal and follows the radar range equation.

[0100] Consider a radar model with a single stationary test target in a 600m range and a specific value for RCS. The target in a 600m range and the specific value for RCS are used. For the target in 600m, the received signal will be delayed by 4 seconds compared to the transmitted signal. The time-domain representations of the signal at the output of the transmitting antenna and the signal at the output of the receiving antenna are given in Figure 8. Figure 8 clearly shows that, as expected, there is a 4-second delay between the transmitted and received signals. The radar range equation provides the expected value of the received signal power.

[0101] For a test target at 600m and given the specific RCS value provided, the signal power at the receiving antenna output is expected to be -74.4dB. The power spectrum of the signal at the receiving antenna output is given in Figure 10. Figure 10 shows the receiving antenna output peaking at -99.6dB. Because the peak in the power spectrum is reduced by a factor of 25.1dB due to the RMS value, the received signal power is -74.5dB, which is very close to the expected value of -74.4dB. 4.1.2 Thermal noise in simulations

[0102] In the simulation, thermal noise was added using a separate block. Thermal noise depends on both bandwidth and temperature. The thermal noise block in the simulation can provide any value for noise temperature. The noise bandwidth in the simulation was equal to the simulation's sampling rate. The simulation's sampling rate was set to 5 GHz, which is greater than twice the value of any frequency used in the simulation, and met the Nyquist criterion. For a noise bandwidth of 5 GHz and a noise temperature equal to room temperature (290 K), the expected thermal noise power would be -107 dBW. Figure 9 shows a histogram of the thermal noise power values ​​generated by the thermal noise block. A Gaussian distribution of thermal noise power values, peaking at -107 dB, can be seen in the histogram in Figure 9.

[0103] SNR is an important parameter that defines radar performance. The SNR of a received signal is given by equation (5), and the SNR of a received signal with an atmospheric loss coefficient of 1 is given by:

[0104]

number

[0105] The power spectrum of the signal at the output of the thermal noise block is compared to the power spectrum of the signal at the output of the receiving antenna in Figure 10. In Figure 10, thermal noise can be seen at all frequencies. The resolution bandwidth (RBW) of the power spectrum is 5 MHz, and the noise bandwidth is 5 GHz, resulting in a thermal noise floor of -137 dB in Figure 10, which is 30 dB lower than the expected thermal noise power of -107 dB. 4.2 LNA

[0106] LNAs (Low-Range Amplifiers) are essential components in most communication systems, particularly in the receiving chain of radar. An LNA is an electronic amplifier capable of amplifying weak signals without significant degradation of signal-to-noise ratio (SNR). In radar, LNAs are generally located after the receiving antenna to amplify weakly received signals and provide a suitable power level for analog-to-digital conversion or further processing in the analog domain. The performance of an LNA can be measured using different parameters, including noise figure, gain, and dynamic range. The gain of an LNA is defined as the amplification coefficient of the LNA. LNA gain is specified in dB units. The noise figure (NF) of an LNA is defined as a measure of the degradation of the SNR of the received signal. For example, if an LNA has a 3 dB SNR degradation at its output compared to its input, the NF of the LNA would be 3 dB. Radar engineers aim to manufacture LNAs with the lowest possible NF to reduce SNR degradation caused by the LNA. 4.2.1 LNA in Simulation

[0107] Throughout the radar model, the LNA was implemented using an amplifier with an option to specify the NF. The LNA design is based on the amplifier discussed in Section 2.3.1. The LNA can provide any user-defined amplification value. Along with the LNA gain, the LNA block can also specify any value for the NF. The received signal (S) at the output of the LNA LNA The mathematical model for ) is, S LNA (t)=A LNA cos[2πf T t+2πf d mT+θ'] (6) Given by, where A LNA This represents the amplitude of the received signal after the LNA. The SNR (SNR) at the output of the LNA. LNA )teeth, SNR LNA =SNR R -NF (7) The signal power spectrum at the output of the LNA is compared to the signal at the output of the thermal noise block in Figure 11. We can see the signal power and noise floor amplified by a factor equal to the LNA amplification factor. In Figure 11, we can also see that the SNRs at the outputs of the thermal noise block and the LNA are 37.4 dB and 32.9 dB, respectively. The SNR at the output of the thermal noise block is reduced by a factor equal to the LNA NF, which is 4.5 dB, compared to the SNR at the output of the thermal noise block.

[0108] The signal and noise power at the outputs of the thermal noise block and LNA are also compared by plotting histograms in Figure 12. In Figure 12, broad peaks correspond to noise power, and smaller peaks towards the right end of the histogram correspond to peak signal power. Figure 12(a) shows the signal and noise power after the thermal noise block peaks at -74.5 dB and -107 dB, respectively. Figure 12(b) shows the signal and noise power after the LNA peaks at -10 dB and -38 dB, respectively. Figure 12 also shows the SNR at the output of the LNA, which is reduced by only 4.5 dB compared to the output of the thermal noise block. 4.3 Receiver PLL and Mixer

[0109] One of the important steps in any of the receiving chains of a radar system is down-converting the received RF signal. Down-conversion to baseband can be done in a single step or through multiple steps, as discussed herein. In most cases, the received RF signal is down-converted through multiple steps, each step having a corresponding IF. Down-conversion to IF is generally performed by mixing the received signal with either the LO signal in the transmitting chain or a signal derived from the LO signal, thus maintaining coherence.

[0110] Mixers are commonly used in a vast number of RF applications. An RF mixer is a three-port device used to upconvert or downconvert signal frequencies. Mixers can be either passive or active. An ideal mixer changes the frequency of an input signal while maintaining the phase of the input signal. Mixers are used in radar receiving chains to downconvert RF signals to frequencies convenient for performing further processing steps. A mixer has two inputs, namely an RF input and an LO input. The output of a mixer will have two frequencies, namely the sum of the two input frequencies and the difference between the two input frequencies. A typical mixer is illustrated in Figure 13.

[0111] Two important parameters that affect the SNR of a mixer output are the mixer conversion loss and the mixer's NF. Mixer conversion loss is defined as the ratio between the input RF power and the output IF power. Mixer NF is defined as the ratio between the input SNR and the output SNR. In an ideal mixer where the LO input amplitude is 1 and the RF input amplitude is A, each output amplitude will be equal to A / 2. Since power is proportional to the square of the amplitude, each power of the mixer's output signal will be 1 / 4 times the input power. Therefore, the conversion loss of an ideal mixer will be 6 dB. Mixer NF (NF MXR ) is related to the image frequency. Along with the desired frequency band, there is also an undesirable frequency band known as the image frequency for mixers that use LO to downconvert RF to IF. If noise is present in both the desired frequency band and the image frequency band, the downconversion folds the noise in the image band over the noise in the desired band, resulting in a 3dB NF. 4.3.1 Receiver PLL in Simulation

[0112] In the overall radar model, the receive PLL was designed similarly to the transmit PLL. The receive PLL was designed as a direct digital synthesizer to output an arbitrary user-defined frequency. The frequency at the mixer output was equal to the sum of the transmit frequency and the IF. Coherence between the receive and transmit PLLs was achieved within the Simulink simulation. Both the transmit and receive PLLs were run simultaneously in the simulation, always on, and the phase relationship between the transmit and receive PLLs was made coherent. 4.3.2 Mixers in Simulation

[0113] In the overall radar model, the RF input of the mixer was connected to the output of the LNA, and the LO input of the mixer was connected to the output of the receiver PLL. The output of the mixer contained two frequencies. The difference frequency at the output of the mixer was equal to IF1. In the simulation, the mixer was implemented using a multiplier block to mix both input signals.

[0114] The amplitude at the output of the receiving PLL was kept at 1, and therefore the mixer conversion loss was 6 dB, as in the case of an ideal mixer. The conversion loss affected both signal and noise equally, and the signal power and noise power at the mixer output were 6 dB lower than at the LNA output. The effective SNR gain due to the conversion loss in the mixer was zero. In the simulation, the real-valued received signal was accompanied by complex-valued thermal noise. Therefore, down-conversion folded the thermal noise present in the image band with the thermal noise present in the desired band, making the noise figure of the mixer equal to 3 dB. SNR at the mixer output (SNR MXR )teeth, SNR MXR =SNR LNA -NF MXR (8) Given by, where NF MXR is the NF of the mixer. The power spectrum of the signal at the mixer output is compared to the signal at the LNA output in Figure 14. In Figure 14, two peaks can be seen in the power spectrum at the mixer output. The two peaks correspond to the sum frequency and the difference frequency (IF1 signal). As expected, the mixer output spectrum peaks in Figure 14 are reduced by 6 dB compared to the LNA output spectrum. Since the conversion loss of the mixer is 6 dB and the NF of the mixer is 3 dB, the effective noise power at the mixer output will be reduced by 3 dB compared to the LNA output, as shown in Figure 14. 4.4 BPF

[0115] Filters are present in most communication systems. Filters are electronic components used to allow or block specific signal frequencies. Filters are important for improving the performance of communication systems by filtering out noise and reducing interference from external signals. Generally, filters can be divided into four categories: band-pass filters, band-reject filters, low-pass filters, and high-pass filters. A BPF allows a specific frequency band to pass through and blocks all other frequencies. A band-reject filter blocks a specific frequency band and allows all other frequencies to pass through. A low-pass filter allows all frequencies below a certain value to pass through, and a high-pass filter allows all frequencies above a certain value to pass through. In radar, a BPF filters out all out-of-band noise, thereby reducing noise present in the receiving chain of the radar system. The BPF is placed outside the mixer to transmit the difference frequency at the mixer output and block the sum frequency. 4.4.1 BPF in Simulation

[0116] In the overall radar model, the BPF is placed outside the mixer to transmit the difference frequency centered on IF1. The signal (S) at the output of the BPF BPF The mathematical model for ) is,

[0117]

number

[0118] Given by, here,

[0119]

number

[0120] This is the amplitude of the signal after the BPF. The signal power at the output of the BPF will be equal to the signal power at the difference frequency. The noise power at the output of the BPF is B / B BPF The noise is reduced by a coefficient of , where B is the noise bandwidth before the BPF, and B BPFThis is the bandwidth of the BPF. The SNR at the output of the BPF is (SNR BPF ) is given by the following:

[0121]

number

[0122] In the overall radar model, a narrowband BPF was retained at the mixer output. Histograms of the mixer and BPF outputs are given in Figure 15. In Figure 15(a), it can be seen that the signal power and noise power at the mixer output peaked at -41 dB and -10 dB, respectively. Even if the signal power at the mixer output were reduced by only 6 dB, the signal peak at the mixer output in Figure 15(a) would be the same as the signal peak at the LNA output as shown in Figure 12(b), since both frequency peaks exist before filtering. The noise power at the mixer output is reduced by only 3 dB compared to the LNA output, and therefore is expected to be -41 dB. In Figure 15(b), it can be seen that the signal power and noise power at the BPF output peaked at -61 dB and -16 dB, respectively.

[0123] Since the BPF allows only the difference frequency in the IF, the signal power at the output of the BPF is expected to be -16 dB. The noise power at the output of the BPF will be reduced by a coefficient equal to the bandwidth ratio, as given in equation 10. For the BPF bandwidth provided in the simulation, the expected reduction in noise power after the BPF is 23 dB, which was also confirmed by the power budget calculation. Thermal noise includes both the desired frequency band and the image frequency band. The BPF operates in both the desired frequency band and the image frequency band, resulting in an additional 3 dB noise contribution from the image frequency band. Therefore, the effective reduction in noise power after the BPF will be 20 dB. In Figure 15, the noise power at the outputs of the mixer and the BPF are -41 dB and -61 dB, respectively, showing the expected 20 dB reduction in noise power after the BPF. 4.5 ADC

[0124] Analog-to-digital converters (ADCs) are crucial in digital data processing systems. An ADC is an electronic integrated circuit that converts signals from the analog domain to the digital domain. Signals in the analog domain are continuous signals with continuous values, while signals in the digital domain are represented by a sequence of discrete values. In radar, the ADC is located before the data processing block. The ADC in radar facilitates the conversion from analog to digital signals, enabling digital data processing to generate target information. Key parameters in an ADC include sampling rate, resolution, and quantization error. 4.5.1 ADC Basics

[0125] A typical ADC block diagram is shown in Figure 16. The sample block is used to sample the ADC input signal at a specific sampling frequency. The ADC input signal is continuous in both time and amplitude. The output of the sample block is a signal with discrete time and continuous amplitude. The hold block is used to hold the output of the sample block until the next batch of samples. The signal at the output of the hold block is discrete in time, continuous in amplitude, and remains unchanged until the next sample set. The quantization block is used for quantizing the signal. The continuous analog amplitude at the input is converted to a discrete amplitude at the output of the quantization block. The signal at the output of the quantization block is digital because it is discrete in both time and amplitude. The last block, the encoder block, converts the digital signal to binary format.

[0126] In an ADC, the sampling rate is defined as the number of samples acquired per second. A higher sampling rate allows the ADC to handle higher frequencies. The reconstruction of an analog signal in the digital domain depends on the sampling rate; the higher the sampling rate, the better the signal is reconstructed. If the ADC's sampling rate is lower than twice the frequency of the analog signal, aliasing occurs, and the reconstructed digital signal characteristics differ from those of the analog signal.

[0127] Digital signals have discrete, finite values ​​and are represented using bits. Each bit corresponds to a specific number of steps in the digital signal. The resolution of an ADC is equal to the number of bits that represent the amplitude of the digital signal. The number of steps increases exponentially with the number of bits. As the bit resolution increases, the digital signal becomes closer to the original analog signal.

[0128] The digitized signal at the output of an ADC will always differ from its analog counterpart. Quantization error is the difference between the analog signal and the nearest accessible digital signal at each sampling point. Quantization error introduces noise, known as quantization noise, into the system. Quantization noise is one of the significant noise components in the receiving chain of a radar system. As bit resolution increases, the difference between the digital and analog signals decreases, reducing both quantization error and quantization noise. For an ideal ADC, quantization noise and bit resolution are defined by the signal-to-quantization noise ratio. The signal-to-quantization noise ratio for a number of bits equal to n is given by: SN q R = 20 log(2 n ) (11) 4.5.2 ADC in Simulation

[0129] The ideal scenario was to add a comprehensive ADC simulation to the entire radar model. Since the signals in Simulink are already in the digital domain, the ADC in the entire radar model was used to perform frequency downconversion through aliasing. The ADC was sampled at a specific ADC clock frequency, and the ADC clock frequency was below IF1 at the ADC input. Through frequency aliasing, the ADC output was downconverted to IF2. The ADC was implemented in the entire radar model using a signal-to-workspace block. The ADC clock frequency was achieved by sampling the signal-to-workspace block at the required sampling frequency. The ADC in the entire radar model has the potential to add further subblocks to realize quantization noise. The mathematical model of the signal at the ADC output is: S ADC (t)=A ADC cos[2πf IF2 t+2πf d mT+θ'] (12) Given by, where A ADC This is the amplitude of the signal after ADC, and is equal to the amplitude of the signal before ADC. In the simulation, the ADC does not change the SNR, and therefore the SNR at the output of the ADC is as follows: SNR ADC =SNR BPF (13)

[0130] The histogram of the ADC output is given in Figure 17. Comparing Figure 17 with Figure 15(b), it can be seen that there is no relative difference in signal power or noise power due to the ADC. The signal-to-workspace block carried the Simulink data to the MATLAB workspace. All radar data processing stages were implemented in MATLAB. 5. Data Processing Chain

[0131] The data processing chain in a radar system is as important as other subsections within the radar. The radar data processing chain performs both functions: improving the signal's SNR and calculating target characteristics. Target characteristics primarily include target range and target Doppler velocity. The processing chain can distinguish between target and clutter with respect to range and velocity. One of the key aspects of the data processing chain is the data cube. The data cube is a three-dimensional matrix with three axes: received channel number, slow time, and fast time. The fast time axis contains a single PRI. In the final stage of data processing, the fast time axis will be further subdivided into different range bins with specific range resolutions. The slow time axis is updated for each PRI. Slow time corresponds to a single PRI, and adjacent PRI data are stacked along the slow time axis. Both fast and slow time represent data matrices as shown in Figure 18(a).

[0132] A data cube is an extension of a data matrix with multiple receiving channels. The fast and slow times from each receiving channel constitute the data matrix and are stacked to form a data cube, as shown in Figure 18(b).

[0133] In the data processing chain, one method for improving the signal-to-noise ratio (SNR) is to perform beamforming. Beamforming is performed when multiple receiving antenna elements are present in the radar system. In radar, beamforming is a technique in which the receiving antenna array can be focused over arbitrary angles of elevation and azimuth to improve the SNR. The overall radar model currently includes only one receiving antenna element, and therefore beamforming is not performed in the data processing chain of the radar simulation. The components in the data processing chain of the radar system depend on the radar's functionality. In a pulsed Doppler radar system, the data processing chain typically includes Hilbert transform, matched filtering, windowing, FFT, and range Doppler plot generation. The Hilbert transform is the process of generating a complex-valued signal from a real-valued signal. Matched filtering involves convolution of an unknown signal with a conjugate time-inverted reference signal to improve the signal's SNR in the presence of random noise. In the overall radar model, the Hilbert transform and matched filtering blocks are used to perform both operations on the signal at the output of the ADC.

[0134] In radar, the Fast Fourier Transform (FFT) is performed to extract frequency-domain information from a time-domain signal. The Fourier Transform is also used to improve the signal-to-noise ratio (SNR) through pulse integration. Pulse integration can follow coherent and non-coherent methods depending on the signal characteristics. The FFT is applied over a slow time axis. In the overall radar model, windowing is performed before the FFT to reduce the effect of frequency component leakage on the Fourier Transform. Range Doppler plots are generated by performing the FFT over a slow time axis of the data matrix. In the overall radar model, range Doppler plot generation follows windowing and the FFT to provide range and Doppler information for both target and clutter. 5.1 Hilbert Transform and Matched Filtering

[0135] In data processing, the Hilbert transform is defined as a linear operator that, when applied to a real-valued function x(t), generates a function H[x(t)]. Mathematically, the Hilbert transform of x(t) is:

[0136]

number

[0137] Given as shown, where * illustrates the convolution operation. The Hilbert transform is useful for generating an analytic signal. The analytic signal is a complex-valued function that does not have negative frequency components. The real and imaginary parts of the analytic signal are real-valued functions that are related to each other by the Hilbert transform. The analytic signal at the output of the Hilbert transform can be defined as follows:

[0138]

number

[0139] The real and imaginary parts of equation 15 are known as the common-mode and quadrature values. In the frequency domain, the Hilbert transform imparts a 90° phase shift to a signal. In radar, the Hilbert transform is one of the methods used to downconvert a signal to baseband. The Hilbert transform is generally performed through quadrature detection, where the IF signal is downconverted to baseband along with the generation of a common-mode baseband signal and a quadrature baseband signal. The Hilbert transform and the generation of the analyzed common-mode and quadrature signals are important in data processing and Doppler velocity calculation.

[0140] In communication systems, matched filters are used as linear filters to improve the signal-to-noise ratio (SNR) of a signal in the presence of additive white Gaussian noise. A matched filter is a time-domain linear filter based on the principle of correlation between a received signal and a reference signal, for improving the SNR of a received signal. In an ideal matched filter, the maximum SNR is achieved when the reference signal is a time-delayed mirror image of the received signal. 5.1.1 Hilbert Transform and Matched Filtering in Simulations

[0141] In the overall radar model, a Hilbert transform and matched filtering block was used to reduce IF2 to baseband and perform matched filtering. The simplified mathematical model of the baseband complex signal at the output of the Hilbert transform is:

[0142]

number

[0143] Given by, where A HT This is the amplitude of the signal after the Hilbert transform. The complex baseband radar signal is used to measure the phase and amplitude independently. The matched filter for the entire radar model was derived from the transmitted pulse. MATLAB code for matched filtering was used to generate the matched filter output. The mathematical model of the signal at the output of the matched filter is:

[0144]

number

[0145] Given by, where A MF G is the amplitude of the signal at the output of the matched filter. An important parameter in matched filtering is the gain of the matched filter. The gain of the matched filter depends on the matched filter coefficients. MF For a matched filter that provides an SNR gain of , the SNR at the output of the matched filter is (SNR MF ) is given by the following: SNR MF =SNR ADC +G MF (18)

[0146] Figure 19 shows the time-domain representation of the signals at the input and output of the matched filter. A rectangular pulse, after undergoing matched filtering, outputs a triangular waveform. Because matched filtering performs correlation between the input signal and the reference signal, the matched-filtered output of the rectangular pulse will peak at the endpoint of the input signal. For any finite input signal, the duration of the matched filter output is twice the duration of the input signal. Since the input to the matched filter is approximately a rectangular pulse, and the reference derived from the transmitted signal is also approximately a rectangular pulse, the output of the matched filter in Figure 19 is a triangular pulse, as expected. The duration of the matched filter output in Figure 19 is twice the duration of the input signal, and it also peaks at the endpoint of the input signal. A histogram of the output of the matched filter is shown in Figure 20. The signal and noise power at the output of the matched filter are 79 dB and 23.5 dB, respectively. Comparing Figure 20 with Figure 17, it can be seen that the SNR at the output of the matched filter has improved by a factor of 10.5 dB. The improvement achieved through harmonized filtering is very close to the expected value confirmed using the gaze radar power budget. 5.2 Windowing, FFT, and Ranged Doppler Plots

[0147] Fourier analysis transforms a signal in the time domain or spatial domain into a representation in the frequency domain, or vice versa. The Fast Fourier Transform (FFT) is an algorithm for performing Fourier analysis, including both the Discrete Fourier Transform (DFT) and the Inverse Discrete Fourier Transform (IDFT). The DFT of a signal is obtained by decomposing the signal into its frequency components. FFT is used in a wide range of applications in communication systems. In radar, FFT transforms a time-domain signal into a frequency-domain signal. FFT is one of the important data processing techniques used in radar to acquire target Doppler information.

[0148] The FFT is performed along the direction of the slow time axis in the data matrix or data cube. Performing an FFT on the data matrix or data cube results in a range Doppler plot where the slow time is converted to the Doppler axis and the fast time is converted to the range axis. The Doppler axis contains the Doppler bins, and the range axis contains the range bins. Generally, in pulsed Doppler radar, the range Doppler plot is used to represent the processed data from the radar. The range Doppler plot contains range and Doppler information for the target and clutter. The range Doppler plot can also be used to analyze the relative signal intensity from different scatterers and to evaluate the effect of phase noise in radar target detection.

[0149] In radar, the FFT also performs pulse integration to improve the signal-to-noise ratio (SNR). Because the FFT is performed along a slow time axis, the pulse integration is also performed along a slow time axis that includes a stack of received pulses. The FFT performs coherent integration of both signal and noise. For the coherent integration of N pulses containing both signal and noise, the power in the integrated signal component is N 2 The noise power increases by N, and the integrated noise power increases by N. Therefore, the effective increase in SNR is N in dB.

[0150] For any time-domain signal undergoing a Fourier transform into the frequency domain, discontinuities resulting from the non-integer period of the waveform lead to frequency components leaking into adjacent frequency bins. This leakage diffuses fine spectral lines in the FFT and is known as spectral leakage. Spectral leakage is minimized by a technique known as windowing. Windowing multiplies the measured time-domain discontinuous signal by an amplitude envelope that approaches zero at both ends. The SNR at the output of windowing is degraded by a coefficient known as the loss coefficient. The loss coefficient is defined as the ratio of the maximum achievable SNR (SNR without windowing) to the actual SNR (SNR with windowing). Depending on the application, there are different types of windowing functions, including the Hamming window, Hanning window, and Blackman-Harris window. 5.2.1 Windowing, FFT, and Ranged Doppler Plots in Simulations

[0151] The entire radar model applies windowing to the time-domain signal before performing the FFT to generate a range Doppler plot. Following the gaze radar, Blackman-Harris windowing was applied in the data processing chain of the radar model. SNR(SNR) at the output of the windowing W )teeth, SNR W =SNR MF -LF (19) The result is given by, where LF is the loss coefficient of the Blackman-Harris window. The FFT was performed after windowing. SNR at the output of the FFT (SNR FFT )teeth, SNR FFT =SNR W +N (20) The FFT is given by , where N is the number of pulses. Depending on the requirements, the FFT can be performed with any number of pulses. For the entire radar model, the FFT was performed with 2048 pulses (N=2048). Since there was only one stationary test target in the simulation, the FFT with 2048 pulses significantly reduces the number of target peak occurrences in the histogram. Therefore, it is not possible to represent the signal power at the output of the FFT with a histogram plot. A range Doppler plot with a stationary target at 600m is given in Figure 21. Figure 21 includes range Doppler plots with and without Blackman-Harris windowing. The range Doppler plot contains several range bins on the vertical axis depending on the radar configuration. The number of Doppler bins on the horizontal axis is the same as the number of pulses used to generate the range Doppler plot. The range Doppler plot in Figure 21 contains 15 range bins and a central Doppler bin, equal to a span of 1400Hz.

[0152] The FFT of 2048 pulses provides an SNR gain of 33 dB. The range Doppler plot without a Blackman-Harris window in Figure 21(a) has a target peak power of 145 dB and an average noise power of 56.5 dB, which is equal to an SNR of 88.5 dB. As illustrated in Figure 20, the SNR at the MF output was 55.5 dB, so the SNR of 88.5 dB at the FFT output is equal to the expected value of the 33 dB increase in SNR. The range Doppler plot with a Blackman-Harris window given in Figure 21(b) has a target peak power of 136 dB and an average noise power of 51 dB, which is equal to an SNR of 85 dB. The SNR of 85 dB at the FFT output with windowing is 3.5 dB lower than the value of 88.5 dB at the FFT output without windowing. Windowing follows the loss coefficient of the Blackman-Harris window. The Blackman-Harris window loss factor is between 3 and 3.5 dB, and the SNR value calculated from Figure 21 also shows a loss factor of 3.5 dB. In the simulation, the waveform in the Fourier transform using a stationary target contains only an integer number of periods. Therefore, frequency spread to adjacent frequency bins will not be visible in the stationary target. 6. Validation of the radar model

[0153] Radar models can be validated by comparing the results of the radar model with both theoretical and actual radar data. By comparing key metrics in range Doppler plots, simulated radar models can be evaluated and validated in the best possible way against actual radar. SNR and clutter-to-noise ratio (CNR) are two key metrics in range Doppler plots.

[0154] The entire radar model described herein benefits from its ability to simulate targets and clutter in the environment at any given range, velocity, and RCS. The entire radar model is validated by comparing the simulated results with experimentally measured results from an L-band gaze radar. The validation of the radar model was facilitated by generating simulated data equal to a full coherent processing interval (CPI). The CPI contained time durations equal to 2048 PRIs. The model was validated by comparing actual range Doppler plots from radar trials with simulated range Doppler plots. Several range bins and 2048 Doppler bins constitute the actual range Doppler plots.

[0155] Three levels of validation were performed to verify the accuracy of the entire simulated radar model. System-level signal power and SNR values ​​at the outputs of various blocks of the simulation were compared to expected values ​​in the first level of validation. Basic parameters from the generated range Doppler plots were compared with basic parameters from the actual radar in the second level of validation. CNR, SNR, and thermal noise floor from the simulated range Doppler plots and the actual range Doppler plots were compared in the third level of validation. 6.1 Comparison of System-Level Signal Power and SNR

[0156] The first level of validation was performed by comparing the SNR and system-level signal power at the outputs of different building blocks in the transmit chain, receive chain, and data processing chain. In the first stage of validation, a single stationary clutter was simulated at a range of 600m and an arbitrary RCS value. A simulation time equal to the full CPI was performed. Signal power, noise power, and SNR values ​​were measured using test probes placed at the outputs of building blocks in the radar model. It is convenient to divide the first stage of validation into two sections. Signal power is compared in the first section, and SNR values ​​are compared in the second section.

[0157] [Table 1]

[0158] In the first section, the simulated signal power at the outputs of the transmitting amplifier, transmitting antenna, and receiving antenna are compared to the expected values, as shown in Figures 7 and 8. The expected values ​​of the signal power at the outputs of the transmitting amplifier and transmitting antenna were obtained from the gaze radar power budget. The expected value of the signal power at the output of the receiving antenna was calculated from the radar range equation. A comparison of the simulated and expected values ​​of the signal power is summarized in Table 1. Table 1 shows that the simulated values ​​of the signal power at the outputs of the transmitting amplifier and transmitting antenna are exactly the same as the expected values. The simulated value of the signal power at the output of the receiving antenna is offset by a negligible value of 0.1 dB.

[0159] [Table 2]

[0160] The simulated SNR at the output of each building block in the receiving chain and data processing chain was compared to the expected SNR in Section 2. The simulated SNR values ​​were determined using the signal power and thermal noise power at the output of each building block. Radar theory and power budget reference values ​​were used to calculate the expected SNR values. The simulated and expected SNR values ​​at the output of different radar building blocks are summarized and compared in Table 2. Table 2 shows a good agreement between the simulated and expected SNR values ​​at the output of all building blocks. A constant offset of 0.1 dB between the expected and simulated SNR exists for all SNR values ​​starting from the receiving antenna. The 0.1 dB difference in Table 2 is due to small differences in parameters for the target modeling. The SNR at the output of the matched filter and FFT in Table 2 is offset by -0.4 dB. The -0.4dB difference, along with a constant 0.1dB offset present across all building blocks, is the net effect of the 0.5dB difference between the expected and simulated values ​​of the SNR gain in the matched filtering. The high agreement between the simulated and expected values ​​of system-level signal power and SNR, as shown in Tables 1 and 2, demonstrates the fidelity of the building blocks in the simulated radar model with theoretical and gaze radar power budgets. 6.2 Comparison of Range and Doppler Basic Parameters

[0161] After comparing system-level signal power and SNR at the first level of validation in Section 6.1, fundamental parameters from both simulated and gazed radar range Doppler plots are compared at the second level of validation. Moving targets and arbitrary RCS at a range of 1500m, along with stationary clutter and arbitrary RCS at 600m, were also simulated at the second level of validation. The RCS and range values ​​were arbitrary and did not follow any particular criteria. The target was assumed to be moving at 251Hz, equivalent to a speed of 30m / s for a given L-band transmit frequency of the radar. Range Doppler plots with thermal noise were simulated using the entire radar model, including both clutter and targets.

[0162] The simulated range Doppler plot contained several range bins. The PRF of the simulated range Doppler plot was the same as that of the gaze radar range Doppler plot. Figure 22 shows the simulated range Doppler plot with range bins on the vertical axis and Doppler frequency on the horizontal axis. Figure 22 clearly shows a target with a Doppler frequency of approximately 251 Hz (highlighted in red) and stationary clutter with a Doppler frequency of 0 Hz (highlighted in green), confirming the overall ability of the radar model to simulate targets and clutter at user-defined speeds.

[0163] The second level of validation involves comparing the range and Doppler fundamental parameters, including range bin, Doppler bin, range resolution, and Doppler resolution, for both the simulated radar model and the L-band gaze radar. Figure 22 was used to measure the range and Doppler bin for both target and clutter. The range resolution and Doppler resolution for the entire simulated radar model were also calculated from Figure 22. There was excellent agreement in range resolution and Doppler resolution between the simulated radar model and the actual radar. A comparison of the fundamental parameters, including range bin and Doppler bin, for the simulated radar model and gaze radar is summarized in Table 3.

[0164] [Table 3]

[0165] The simulated values ​​correspond to the values ​​measured from the simulated range-Doppler plot in Figure 22. The gaze radar's range resolution and Doppler resolution were used to calculate the expected values ​​of the range bin and Doppler frequency bin for both clutter and targets. Table 3 shows that the fundamental parameters from the simulation are the same as the expected parameters from the actual radar. This section concludes that the radar model has the ability to replicate the gaze radar range and Doppler fundamental parameters in the simulation. The radar model discussed herein has the potential to optimize all fundamental parameters, including range resolution, Doppler resolution, CPI, and PRF, to any configuration required by any user. 6.3 Comparison of the entire range Doppler plot

[0166] The third stage of validation involves validating the CNR, SNR, and thermal noise floor from a simulated range Doppler plot by comparing them to an actual radar range Doppler plot. The range Doppler plot from an actual radar experiment was used as the baseline for the third level of validation. The L-band gaze radar was used for the actual radar trial, which was conducted at an airfield using a controlled drone (target of interest) flying along a predetermined course. The actual range Doppler plot consisted of a single frame from a single beam at a fixed azimuth and elevation center on the direction of the target in the drone radar trial, equal to the full CPI. For the selected frame, the drone approached the radar at a specific distance and a line-of-sight velocity of 8.3 m / s. The radar trial was performed in a rural environment, but the actual range Doppler plot included other unwanted targets within the radar's field of view.

[0167] The third stage of validation was performed by simulating actual range Doppler plots using a comprehensive radar model. The range Doppler plots were initially simulated so that the CNR from the simulation and the CNR from the actual range Doppler plots were close to each other. The current model is not capable of simulating an exact replica of the CNR for all range bins because clutter power from each range bin leaks to neighboring range bins. For both the actual and simulated range Doppler plots, the CNR was calculated taking thermal noise into account.

[0168] [Table 4]

[0169] Table 4 compares the CNR from simulated range Doppler plots with the CNR from actual range Doppler plots for different range bins. The actual radar environment contains a wide variety of clutter, each with a different RCS. In the simulation, clutter power is replicated by modeling a single clutter in each range bin to represent a combination of real-world clutter. Furthermore, in the simulation, clutter power leaks from one range bin to an adjacent range bin. Therefore, a variation of a few dB in CNR levels between the simulation and actual data is acceptable. The values ​​in Table 4 demonstrate a strong agreement between the simulated and actual CNRs.

[0170] The second step in the final level of validation involved adding target information to the range Doppler plot in the simulation. The target drone was represented in the simulation as an object at a distance equal to the target's actual range. The target's Doppler frequency was simulated at 69.8 Hz, equal to the drone's speed of 8.3 m / s. The drone's RCS was modeled so that the SNR of both the simulation and the actual data were equivalent.

[0171] For any real-world radar, phase noise plays a crucial role in target detection. Phase noise in a radar system causes the phase noise floor of a range Doppler plot to emerge from the thermal noise floor in range bins with stronger clutter power. This increase in the overall noise floor makes target detection more difficult. The introduction of phase noise in radar simulations and its effects on range Doppler plots are discussed herein.

[0172] A comparison of simulated and actual range Doppler plots is given in Figure 23. Both range Doppler plots in Figure 23 are normalized for the strongest clutter. Figure 23(a) shows the simulated range Doppler plot, and Figure 23(b) shows the actual range Doppler plot. In Figure 23(b), an increase in the overall noise floor and the appearance of a phase noise floor exceeding the thermal noise floor are observed in ranges with very high clutter. Since phase noise is not added to the overall radar model simulation, the simulated range Doppler plot in Figure 23(a) includes only a uniform thermal noise floor. The actual range Doppler plot in Figure 23(b) also includes extra peaks corresponding to unwanted targets in the radar field of view.

[0173] Except for the phase noise floor and unwanted targets, the simulated and actual range Doppler plots are qualitatively similar in Figure 23. Clutter power for each range bin does not independently match because the CNR for each range bin cannot be precisely reproduced in the simulation. The target of interest is highlighted inside the red box for both range Doppler plots in Figure 23. In both the simulated and actual range Doppler plots, the target appears in the 19th range bin and the 19th Doppler bin. The signal-to-thermal noise ratio of the target was calculated from both the simulated and actual range Doppler plots and is given in Table 5. Table 5 shows excellent agreement between the simulated and actual signal-to-thermal noise ratios. The high level of agreement in the signal-to-thermal noise ratio confirms the ability of the comprehensive radar model to simulate range Doppler plots with targets and thermal noise floors that replicate values ​​from actual range Doppler plots.

[0174] [Table 5]

[0175] Peak clutter power and thermal noise floor for each range bin were obtained from Figures 23(a) and 23(b) to generate comparison graphs. The thermal noise floor refers to the noise floor for range bins far from the radar where the effect of clutter-induced phase noise is negligible. The graphs in Figure 24 compare the simulated range Doppler plots from Figures 23(a) and 23(b) with the actual range Doppler plots, respectively.

[0176] Figure 24 clearly shows the overlapping thermal noise floors for both the actual and simulated range Doppler plots. The comparison graph also shows a good agreement between the simulated and actual clutter power. The clutter power for range bin 21 from the actual range Doppler plot was an exception that could not be replicated in the simulation. Clutter in actual radar includes complex objects and other weather phenomena within the radar field of view. In contrast, clutter in the simulation is represented by a single stationary clutter in each range bin. Because the radar pulse width is longer than the range bins, there is an extra effect of leaking clutter power to adjacent range bins. Due to these two effects, the clutter power in all range bins cannot be replicated with perfect agreement in the simulation. Figures 23(a), 23(b), and 24, along with Tables 4 and 5, show the confidence and fidelity of the comprehensive radar model for replicating actual gaze radar trials in the simulation.

[0177] Range Doppler plots of the radar trials realized in the simulation and the actual gaze radar trials are shown in Figure 25(c).

[0178] Figure 25(c) clearly shows the good agreement between the actual range Doppler plot with classical oscillator phase noise and the simulated range Doppler plot. Figure 25(c) represents with a high level of detail the ability of the comprehensive radar model to replicate actual gaze radar trials. The actual range Doppler plot in Figure 25(b) includes unwanted targets in the radar field of view along with the target of interest. At the same time, in the simulated range Doppler plot in Figure 25(a), only the target of interest is modeled. In both range Doppler plots in Figure 25(c), the presence of a phase noise floor can be clearly seen. It can be seen that the phase noise floor comes out of the thermal noise floor for range bins with higher clutter power. Since the reflected power from the clutter is inversely proportional to the fourth power of the clutter range, in almost all cases, the range bins closer to the radar will have the highest clutter power. In Figure 7, it can be clearly seen that the phase noise floor comes out of the thermal noise floor for range bins closer to the radar (range bins 1-4) with high clutter power. The effects of phase noise are also observed for range bins 6, 7, 9, and 10, which have relatively higher clutter power.

[0179] A better comparison of the relationship between simulated range Doppler plots and actual range Doppler plots with classical oscillator phase noise is given in Figure 26. The comparison graph in Figure 26 shows the peak clutter power, thermal noise floor, and overall noise floor for both the simulated range Doppler plot and the actual range Doppler plot in Figure 25(c). The thermal noise floor refers to the noise floor for range bins far from the radar where the effect of clutter-induced phase noise is negligible. The comparison graph in Figure 26 is an extended version of the previously described comparison graph, including the overall noise floor for each range bin. The overall noise floor consists of both the thermal noise floor and the phase noise floor.

[0180] Figure 27 schematically illustrates the method according to an illustrative embodiment.

[0181] This method simulates a RADAR system defined by a corresponding RADAR model comprising a transmit chain, a receive chain, and a data processing chain, and is implemented by a computer equipped with a processor and memory, and this method Transmitting radio frequency (RF) signals into an environment containing clutter, both targeted and optional, via a transmission chain (2702), To calculate the reflected RF signals of the RF signals transmitted from the target and an optional clutter (2704), The receiving chain receives each reflected RF signal (2706), The data processing chain uses each received reflected RF signal to detect the target (2708), Using the results of target detection to determine the simulated output of the RADAR system (2710) It is equipped with.

[0182] Figure 28 schematically illustrates the method according to an illustrative embodiment.

[0183] This method validates a RADAR model that includes a transmit chain, a receive chain, and a data processing chain, which are compatible with a RADAR system. This method is implemented by a computer equipped with a processor and memory. The objective is to acquire output from RADAR systems in environments including clutter, and optionally (2802), Creating a RADAR model that corresponds to the RADAR system (2804), By simulating the RADAR model, the simulated output of the RADAR model for the target and optionally including clutter in the environment is determined (2806), Validating the RADAR model by comparing the simulated output with the acquired output (2808) It is equipped with. 7 Conclusion

[0184] This specification presents the development of a comprehensive radar model in simulation and the validation of the radar model using data from actual radar. A radar model that includes all the basic building blocks in the transmit, receive, and data processing chains, along with targets and clutter in the environment, is a powerful tool for testing virtual and real-world radar scenarios. Such a comprehensive radar model was developed from the ground up, focusing on the behavior of the building blocks of radar hardware. The radar model was validated by comparing the results generated in simulation with actual experimental results from L-band gaze radar. The radar model was capable of replicating actual radar parameters at various levels.

[0185] Detailed studies characterizing the performance of different classical quantum oscillators will enable comprehensive radar models to analyze the phase noise limits of conventional radar oscillators and explore the potential advantages of quantum oscillators in radar systems.

[0186] While preferred embodiments have been shown and described, it will be recognized by those skilled in the art that various changes and modifications can be made without departing from the scope of the invention as defined in the appended claims and described above.

[0187] Attention is drawn to all documents and papers filed concurrently with or prior to this specification in connection with this application and made available to the public together with this specification, and the contents of all such documents and papers are incorporated herein by reference.

[0188] All features disclosed herein (including any appended claims and drawings) and / or all steps of any method or process disclosed herein may be combined in any combination, except for any combination in which many or some of such features and / or steps are mutually exclusive.

[0189] Each feature disclosed herein (including any attached claims and drawings) may be replaced by an alternative feature serving the same, equivalent, or similar purpose unless otherwise specified. Therefore, unless otherwise specified, each disclosed feature is merely an example of a general set of equivalent or similar features.

[0190] The present invention is not limited to the details of the embodiments described above. The present invention extends to any novel one or any novel combination of features disclosed herein (including any appended claims and drawings), or any novel one or any novel combination of steps of any method or process so so disclosed.

Claims

1. A method for validating a RADAR model comprising a transmit chain, a receive chain, and a data processing chain, which corresponds to a RADAR system, wherein the method is implemented by a computer comprising a processor and memory, and the method is To acquire the output from the RADAR system in an environment including clutter, which is the target and optional. Creating the RADAR model corresponding to the RADAR system, The simulated output of the RADAR model is determined by simulating the RADAR model for the target and the environment, which optionally includes the clutter. The RADAR model is validated by comparing the simulated output with the acquired output. A method that includes [a certain feature].

2. The method according to claim 1, wherein comparing the simulated output with the acquired output comprises comparing the signal power and / or signal-to-noise ratio (SNR) values ​​of the respective transmission chain, reception chain, and / or data processing chain with their expected values.

3. The method according to claim 2, wherein comparing the signal power and / or signal-to-noise ratio (SNR) values ​​of each of the transmission chain, the reception chain, and / or the data processing chain with their expected values ​​comprises comparing the signal power and / or signal-to-noise ratio (SNR) values ​​of each of the outputs of the transmission chain, the reception chain, and / or the data processing chain with their expected values.

4. The method according to any one of claims 1 to 3, wherein comparing the simulated output with the acquired output comprises comparing the basic parameters of the simulated output with the basic parameters of the acquired output.

5. The method according to claim 4, wherein comparing the basic parameters of the simulated output with the basic parameters of the acquired output comprises comparing the basic parameters of the generated range Doppler plot of the simulated output with the basic parameters of the range Doppler plot of the acquired output.

6. The method according to any one of claims 1 to 5, wherein comparing the simulated output with the acquired output comprises comparing the clutter-to-noise ratio (CNR), signal-to-noise ratio (SNR), and / or thermal noise floor of the simulated output and the acquired output.

7. The method according to claim 6, comprising comparing the CNR, SNR, and / or thermal noise floor of the simulated output and the acquired output with the CNR, SNR, and / or thermal noise floor of the generated range Doppler plot of the simulated output and the acquired range Doppler plot of the output.

8. The method according to claim 7, wherein the CNR takes thermal noise into consideration.

9. Adapting the RADAR model, for example, the transmission chain, the reception chain, and the data processing chain, Optionally, modify the aforementioned target and / or the optional clutter. The method according to any one of claims 1 to 8, comprising: calculating the reflected RF signal of each of the RF signals transmitted from the target and the optionally selected clutter, and further comprising: calculating the reflected RF signal of each of the modified RF signals transmitted from the target and the optionally selected modified clutter.

10. The method according to any one of claims 1 to 9, wherein the transmission block comprises a quantum oscillator.

11. A method for providing a RADAR system, the method being at least partially implemented by a computer having a processor and memory, the method is To validate a RADAR model that includes a transmit chain, a receive chain, and a data processing chain, corresponding to the aforementioned RADAR system, To provide the RADAR system using the results of validating the aforementioned RADAR model. A method that includes [a certain feature].

12. The method according to claim 11, further comprising fitting the RADAR model based on the results of validating the RADAR model.

13. The method according to claim 12, comprising validating the adapted RADAR model.

14. A RADAR system provided by the method according to any one of claims 11 to 13.

15. A computer having a processor and memory configured to implement the method according to any one of claims 1 to 13; a computer program having instructions that, when executed by the computer having a processor and memory, cause the computer to perform the method according to any one of claims 1 to 13; or a non-transient computer-readable storage medium having instructions that, when executed by the computer having a processor and memory, cause the computer to perform the method according to any one of claims 1 to 13.

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