Radar Signal Processing using MATLABIr. Dr. KOO Voon-Chet Business Development Manager, iRadar Sdn Bhd Overview Solutions Solutions Services Services Support Support Our Core Competencies • High Resolution Imaging and Non-Imaging Radars • RF Subsystems Design (Antennas, RF Transceivers, Radar Processors) • High-Speed PCB Design and Analysis • High-Speed PCB Design and Analysis • Embedded Solutions (MCU, FPGA, HPC) Overview of Radar Solutions by Scatterometer Scatterometer System (SCAT) System (SCAT) • Non-imaging remote sensing radar • Backscattering coefficient measurements Synthetic Aperture Radar (SAR) Synthetic Aperture Radar (SAR) • High-resolution imaging radar • Terrain mapping and disaster monitoring Landslide Monitoring Radar (LSR) Landslide Monitoring Radar (LSR) Landslide Monitoring Radar (LSR) Landslide Monitoring Radar (LSR) • High-resolution interferometry SAR • Instant risk assessment and landslide monitoring Ultra Wideband Tracking Radar (UWB) Ultra Wideband Tracking Radar (UWB) • High-resolution tracking radar • Precision target detection and localization Traffic Detection Radar (TDR) Traffic Detection Radar (TDR) • Short-range object detection radar • Traffic detection and monitoring AGENDA • What is Radar • Principles of Radar Imaging • 2D Radar Signal Processing • iSAR Pro v2013 – An integrated Matlab-based SAR Utility Program • iSAR Pro v2013 – An integrated Matlab-based SAR Utility Program • Conclusion What is Radar ? What is Radar ? What is Radar ? • RADAR = Radio Detection And Ranging • Contactless sensor operating at radio waves What kind of information can a radar obtain ? Range Speed Location or coordinate Parameters Camera Radar Altitude Target’s signatures Relative phase Images Advantages of using radar Operable in all weather and lighting conditions Ranging and positioning capabilities Long distance and wide area coverage Light-of-sight is not mandatory 2D and 3D imaging capabilities Penetration capability (see-through wall, under-ground, etc.) Target identification and classification Complement optical/IR sensors These are radars NASA/JPL AIRSAR/TO Ship-based Radar: The rotating antenna radiates a vertical fan-shaped beam A model of the Type 345 Fire Control Radar taken at CIDEX 2005 Defence Show Sandia Lab MiniSAR Ku Band, 0.1 m resolution FMCW Ground-based Radar: EISCAT Kiruna Radar AIRSAR/TO PSAR Airborne Radar Space-borne Imaging Radar: TerraSAR- X (2007), TanDEM-X (2010) Envisat (2002) ESA, Europe FMCW Scatterometer, Multimedia University, Malaysia Examples of Radar Applications Defense: to locate targets, guide missile, monitor air traffic, Remote Sensing: to monitor natural resources, land-use mapping, Security and Safety: to detect intruders, safeguard critical facilities, Search and Rescue: to search for life, locate people in a crisis, Process Control: to track assets, monitor critical operations, Transportation: to detect vehicle’s speed, to avoid collision Robotics: to avoid obstacles, guide an unmanned vehicle, And more Is it difficult to integrate a radar to my existing system ? • NO. Today, radar could be very small in size, simple to use, and cost effective, too! Principles of Radar Imaging Principles of Radar Imaging Radar Working Principles Antennas RF Transmitter Processor Data Acquisition & Storage Emitted Pulse Backscattered Returns RF Receiver Information Radar Electronics Radar Processor IF Section ADC Example of Non-Imaging Radar Measurements Scatterometer Measurement Campaign to monitor various stages of paddy (rice) growth Imaging Radar for Wider Area Coverage Aircraft with y Synthetic Aperture Radar (SAR) High azimuth resolution is achieved by synthesizing a very long antenna High range resolution is achieved by pulse compression technique or short pulse x, across-track direction y, along-track direction Aircraft with velocity, v z-direction Footprint ∆x, across-track resolution ∆y, along-track resolution x Functional Block Diagram of a SAR System Motion Sensing Unit GPS Antenna GPS Data 10MHz Ref Rx Window Control Signal PRF Motion sensing data FPGA-based Embedded SAR Controller RF Subsystem Power Supply Module (+15V / +28V) Embedded SAR Processor Data Downlink Data Recorder Antenna System Tx/Rx signal 80MHz LFM I and Q signals Down-converted received I and Q signals From UAV main power supply Down-link Antenna DLR-HR’s E-SAR Sensor (2011) X-band antenna operation console Digital rack S/C-band rack X-band rack screen keyboard control computer ARSM iSAR02 SAR Sensor (2012) SAR Processor RF Subsystem Digital Electronics 27 cm (W) × 35 cm (L) × 25 cm (H) 2D Radar Signal Processing 2D Radar Signal Processing Overview of Synthetic Aperture Radar Signal Processing Geometry of a Generic SAR α Azimuth direction, y Altitude direction, z Ideal trajectory, (0, y, h) R i r 2 2 ) ( i i i y y r R − + = Nominal Altitude, h Ground range direction, x Target-i at arbitrary coordinates (r i , y i ) r i Transmit Chirp Signal amplitude, a() duration, τp bandwidth, B p B τ α = Chirp rate Chirp signal: ) ` ¹ ¹ ´ ¦ | ¹ | \ | = 2 2 1 2 exp ) ( ) ( t j t a t p α π pulse repetition interval, PRI ( ( ¸ ( ¸ = p t j t t rect t p e y t s c τ ω ) ( ) , ( Transmitted signal: Received Signals After demodulation, the backscattered signal received from a point scatterer located at (r i , y i ) is given by | | ( ( ( − − − = − − i i c R j c R t rect R t p r y y w e r y y t g i c ω 2 ) 2 ( , ) , , ( 2 2 | | ( ( ( ¸ ¸ − − = − p i i i c i i c rect c R t p r y y w e r y y t g τ ) 2 ( , ) , , ( 2 ⇒ g(⋅) is the system impulse response 2D Image Formation ∫∫ − − = i i i i i i i dy dr r y y r r g y r f y r s ) , , ( ) , ( ) , ( The raw signal s(r, y): Target signature System impulse response The SAR imaging problem is to design an appropriate filter such that we can recover the best estimate of f(r, y) based on the received signal s(r, y). SAR Processor s(r,y) Raw data f(r,y) image Basic Ideas Range compression Range Migration Azimuth compression 2D Image Formation (Range Doppler Algorithm) Raw Signal s(r,y) Range FT P * (k r ) Range FT -1 Range Compression SAR Image f(r,y) exp[jα(k y )r] Range FT Azimuth FT Azimuth FT -1 RM Correction Range Migration Correction (Interpolation) Azimuth Compression Azimuth Inverse Fourier Transform Azimuth Fourier Transform 2D Image Formation (ω-k Algorithm) Raw Signal s(r,y) 2D FT P * (k r ) Range Compression 2D Fourier Transform SAR Image f(r,y) Nonlinear Mapping 2D FT -1 Stolt Mapping (Interpolation) 2D Inverse Fourier Transform Simulated Point Target S y n t h e t i c A p e r t u r e ( S l o w - t i m e ) U , m e t e r s Simulated Stripmap SAR Signal 2000 4000 6000 Fast-time t, sec S y n t h e t i c A p e r t u r e ( S l o w - t i m e ) U , m e t e r s 8 8.5 9 9.5 10 10.5 x 10 -5 -6000 -4000 -2000 0 Range Compression S y n t h e t i c A p e r t u r e ( S l o w - t i m e ) U , m e t e r s Stripmap SAR Signal after Fast-time Matched Filtering 2000 4000 6000 Fast-time t, sec S y n t h e t i c A p e r t u r e ( S l o w - t i m e ) U , m e t e r s 8 8.5 9 9.5 10 10.5 x 10 -5 -6000 -4000 -2000 0 Azimuth Compression C r o s s - r a n g e Y , m e t e r s Wavefront Stripmap SAR Reconstruction 50 100 Range X, meters C r o s s - r a n g e Y , m e t e r s 1.19 1.195 1.2 1.205 x 10 4 -100 -50 0 Challenges in SAR Signal Processing Huge Data Volume Real-Time Processing Motion Errors and Compensation iSAR Pro v2013 iSAR Pro v2013 An Integrated Matlab-based SAR Utility Program Introduction Design and development of SAR system has been an expensive and time- consuming task Re-designing in hardware and software are costly but sometime it is unavoidable are costly but sometime it is unavoidable With proper planning and simulation, it could help to shorten the design cycle and hence, reducing development cost and time A Matlab-based SAR utility program, iSAR iSAR Pro v2013 Pro v2013 is developed for this purpose Earlier Version: iSim iSim iSim iSim is a Matlab-based SAR simulation program first introduced in 2008 [1]. It is a modular-based simulator with various independent modules that share a pool of data files. a pool of data files. [1] Koo, V.C., C.S.Lim and Y.K.Chan (2007), “iSIM - An Integrated Sar Product Simulator For System Designers And Researchers” Journal of Electromagnetic Waves and Applications (JEWMA), Vol.21, No.3, pp.313-328, ISSN 0920-5071. iSAR iSAR Pro v2013 Pro v2013 iSAR iSAR Pro v2013 Pro v2013 is an enhanced version of iSIM iSIM program with added SAR utility tools: SAR Designer SAR Simulator SAR Simulator SAR Reader SAR Processor SAR Viewer iSAR iSAR Pro v2013 Pro v2013 >> SAR Designer Module SAR Designer Module – SAR system-level design tool – Categories of SAR design parameters: • Geometry • Sensor Configurations • Sensor Configurations • Signal Processing Parameters • Doppler Parameters • Image Quality – Each category has two type of parameters • User-defined parameters • Computed parameters – Save and load functions are included to ease the system design process Examples of System Level Requirements Wavelength, λ (1 cm – 1m) Polarization (usually linear, VV, HH, VH, HV) Pulse length (~10 µs – 50 µs) Pulse bandwidth (~10 MHz – 200 MHz) Pulse Repetition Frequency, PRF (~1000 Hz – 2000 Hz) System Parameters Specifications Mode of Operation Stripmap Operating Frequency 5.3 GHz (C-band) Bandwidth 80 MHz Polarization Single, VV Antenna Gain > 25 dBi Spatial Resolution 5 m x 5 m RCS Dynamic Range 30 dB (0 dB to -30 dB) SNR > 10 dB Pulse Repetition Frequency, PRF (~1000 Hz – 2000 Hz) Transmit Power (~100 W to several kW) Antenna Size (flat array, ~1 m – 10 m+) Operating range (~ several km – hundreds km) SNR > 10 dB Incident Angle 30 deg Platform Height 1000 m Swath Width 1000 m Nominal Platform Speed 30 m/s Data Take Duration 1 hour (10 min per scene) SAR Processing Off-line Overall Sensor Weight < 25 kg Overall Sensor Dimension < 26" (L) x 20" (H) x 17 " (W) Operating Platform UAV, Aludra MK2 (UST) Built-in Power Supply 22 V - 32 V iSAR iSAR Pro v2013 Pro v2013 >> SAR Designer Module iSAR iSAR Pro v2013 Pro v2013 >> SAR Simulator Module SAR Simulator Module – Point target simulation tool – User-friendly input method • Mouse input • Mouse click to add a point target on the map • Mouse click to add a point target on the map – It shares the same parameters from SAR Designer module SAR Simulator >> Reset SAR Simulator >> Adding Point Target SAR Simulator >> Adding Point Targets SAR Simulator >> Generating Raw Data SAR Simulator >> Slant Range Matched Filtering SAR Simulator >> 2D Frequency Spectrum SAR Simulator >> SAR Image Reconstruction SAR Simulator >> SAR Image Reconstruction with RMC iSAR iSAR Pro v2013 Pro v2013 >> SAR Reader Module SAR Reader Module – Data extraction from SAR raw data file – Extracted SAR data is saved in MAT-format – Extra features • Real and imaginary signal plots • Real and imaginary signal plots • Range compressed signal analysis iSAR iSAR Pro v2013 Pro v2013 >> SAR Reader Module iSAR iSAR Pro v2013 Pro v2013 >> SAR Processor Module SAR Processor Module – Level-0 SAR raw data processing module – SAR data imported in MAT-format – User-defined processing algorithm • Custom processing algorithm can be imported (as M-files) • Custom processing algorithm can be imported (as M-files) – Intermediate output is shown – Final output image is save in MAT and TIFF-format iSAR iSAR Pro v2013 Pro v2013 >> SAR Processor 51 SAR Processor-0 >> Raw data processing 52 SAR Processor-0 > Image enhancement 53 iSAR iSAR Pro v2013 Pro v2013 >> SAR Viewer Module SAR Viewer Module – View processed SAR image – Plotting options • Default plot • Gray plot • Gray plot – Customized color intensity • Data rescaling by adjusting minimum and maximum values iSAR iSAR Pro v2013 Pro v2013 >> SAR Viewer 55 iSAR iSAR Pro v2013 Pro v2013 >> SAR Viewer 56 Conclusion Conclusion Result Verifications and Summary SAR Experiment (Stop-n-Go) Truck-mounted SAR Imaging UAV-based ARSM-MMU SAR Experiments UAV-based ARSM-MMU SAR Experiments UAV-based ARSM-MMU VV-POL SAR Images Taken Dec 2010, at Mersing, Johor, Malaysia (R119, C-band 3 x 3 m) Concluding Remarks • Terrain Mapping and Classification • Vegetation and Environmental Monitoring • Landslide Monitoring and Early Warning System • Deforestation Monitoring SAR imaging has many potential applications, such as: • Deforestation Monitoring • Coastal Line Monitoring • and more • Some of these, particularly civilian, have not yet been adequately explored because lower cost electronics are just beginning to make SAR economical for smaller scale uses • With the advancement in RF and semiconductor technologies, radar will become a standard app for electronic gadgets in very near future. Concluding Remarks • Radar Signal Processing is a challenging subject • The use of powerful Matlab tools greatly reduces the complexity of SAR processing • An integrated Matlab-based SAR utility program has been developed to assist the SAR designer Matlab Experiences designer • Future direction: Real-time implementation of Matlab codes in Embedded Processor and FPGA THANK YOU Dynamic Solutions. Precise Results.