Noise radar can be used in a great number of applications including SAR. The non-periodic waveform suppresses the range ambiguity and reduces the probability of intercept and interference. Due to the randomness of the waveform, a noise floor limiting the possible side lobe suppression accompanies the correlation integral involved. In strong clutter scenes with dominant reflectors, the induced noise floor can be too high and further suppression is needed. In this paper, the ambiguity function of random noise waveforms is first analyzed, and an improved formulation is introduced to include the noise floor effect. The use of mismatched filtering for improved sidelobe suppression is then discussed. Finally, an iterative subtraction algorithm is analyzed for noise floor cancellation in the presence of dominating reflectors. This method is successfully tested on random step frequency radar data and noise sodar data.
Pulse compression radar is used in a great number of radar applications. Excellent range resolution and high ECCM performance can be achieved by wide-band modulated long pulses, which spread out the transmitted energy in frequency and time. By using random noise as waveform, the range ambiguity can be suppressed as well. The same
limit in doppler resolution is achieved as for a coherent doppler radar when the time compression of the reference is tuned to that of the target. Mostly, the random signal is transmitted directly from a noise generating HF-source. A sine wave, which is phase or frequency modulated by random noise, is an alternative giving similar performance but higher transmitted mean power when peak-limited transmitters are applied. A narrower modulation noise bandwidth can also be applied to generate the same output bandwidth. For phase modulation, the bandwidth amplifying factor is simply the rms value of the phase modulation, and for a frequency modulating waveform the output rms bandwidth equals the rms value of the frequency modulation. The results also show that the range sidelobes can be highly suppressed compared with the sidelobes of the modulating signal. The mean and variance of the correlation integral are derived in terms of the autocorrelation function of the modulation. Finally, random bi-phase modulation and the effects of low-bit ADC at the correlation processing are analyzed and described. The advantages of low range sidelobes and enhanced range resolution make frequency and phase modulation attractive for a great number of applications.
Standard SAR-processing methods are based upon the assumption of a scene at rest. If targets are moving, their positions in the SAR-image are translated in azimuth and defocusing may occur. In this paper, some methods for the detection and imaging of moving targets and the estimation of their real positions are discussed, such as monopulse and DPCA. Experimental results are shown on position correction using the monopulse ratio of two SAR-imagery derived from the Σ and Δ-channels.
Pulse compression radar is used in a great number of radar applications. Excellent range resolution and high ECCM performance can be achieved by wide-band modulated long pulses, which spread out the transmitted energy in frequency and time. If a random noise waveform is used, the range ambiguity is suppressed as well. The range processing then correlates the received signal and a delayed reference. When the delay of the target signal coincides with that of the delayed reference a strong correlation peak occurs. In this paper, the theory of noise radar for Doppler/range indication is first described. Then the possible use of binary or low-bits ADC is analysed, which highly improves the signal-processing rate and reduces the costs. The application of noise radar to digital beam forming is finally discussed. Similar results are obtained using a frequency chirped waveform and low-bits ADC.
In this paper, the basic performance of MTI/SAR radar in ship and ground vehicle applications is analysed. If a single antenna beam scans a sector, significant degradations in MTI sensitivity and SAR resolution occur due to the reduced dwell time on target. Improved performance can be achieved by digital array beamforming with multiple beams, or high speed scanning. SAR needs accurate phase error compensation by inertial measurements or autofocus due to the non-linear movement path of the antenna.
In the analysis of SAR, there is a need of simulating complex scenes without having measured radar raw data available. The common method is based on a set of point reflectors, which model the response from an extended target. The received I,Q-signals along the SAR aperture are computed, and the effects of internal noise and uncompensated phase errors are easily included. This method becomes extremely cumbersome for complex scenes with radar response from each resolution cell. In this paper, a much more rapid method of raw data generation is applied. The complex numbers representing the SAR image are then converted to radar raw data using the SAR-processing algorithms backwards. The key transform is the scene centre focusing, which ties together SAR data and sensor geometry. This method of generating raw data is several orders of magnitude faster than the direct method described above. The effects of internal noise and phase errors, due to imperfect motion compensation, can easily be included. The method can be applied as well to simulation studies of auto-focusing, DPCA and STAP.
SAR-mapping is usually performed along a straight path. A curved path might increase the mapping rate significantly. Drawbacks are more complex signal processing and that defocusing may occur. In this paper, curved SAR-mapping is analysed in more detail including more forward look geometry. Relationships are derived how SAR-resolution and mapping rate are influenced by the curved SAR-path. Examples show how the non-linear phase error depends on side acceleration and scene geometry.
In this paper, a much more rapid method of raw data generation is applied, starting by simulating the SAR image itself. Conventional methods of translating a thermal IR- or photographic image into a SAR image are first briefly reviewed. The complex numbers representing the SAR image are then converted to radar raw data using the SAR-processing algorithms backwards. The key transform is the scene center focusing, which ties together SAR data and sensor geometry. This method of generating raw data is shown to be several orders of magnitude faster than the direct method described above, and raw data from the I- and Q-channels can readily be simulated even on a PC. The effects of internal noise and phase errors, due to imperfect motion compensation, can also be included and simulated in detail. The method is exemplified, showing the phase error effect on the SAR-image of an urban scene, the use of autofocusing to restore the image resolution, and finally the degradation due to additive noise on the collected SAR raw data.