We consider the problem of multiple target estimation using a colocated Multiple Input Multiple Output (MIMO) radar system. We employ sparse modeling to estimate the unknown target parameters (delay, Doppler) using a MIMO radar system that transmits frequency-hopping waveforms. We derive analytical expressions for the correlations between the different blocks of columns of the sensing matrix. Using these expressions, we compute the block coherence measure of the dictionary. Next, we use this measure to optimally design the sensing matrix by selecting the hopping-frequencies for all the transmitters. We demonstrate the performance improvement using numerical simulations.
With more users populating the frequency spectrum and less available contiguous bandwidth, radar and communication waveforms are slowly forced to become more efficient at using their available frequencies. As waveform designs become more exotic, it is critical that future sparse-frequency methods also consider constraints imposed by amplifiers, such as modulus and phase restrictions. In this paper, three sparse-frequency waveform designs are compared. Experimental results are shown and validated against simulated data.
Affine approximation is a technique used to model time-invariant periodicity preservation (TIPP) systems, which represent a broad class of wireless system nonlinear components. This approach approximates the harmonic transfer characteristics of a nonlinear system and, as a consequence, is expected to be very useful in both waveform design and circuit optimization. While this approach is useful, there are limitations of this approximation based on the strength of the nonlinearity, the size of the perturbation imposed on the large-signal operating condition, and the number of harmonics used to approximate the signal. This paper examines some sample TIPP nonlinearities and show that the affine approximation accuracy often degrades for increasing perturbation size and when a reduced number of harmonics is used to approximate system results for waveforms containing significant harmonic content.
A Dual-Channel Radar (DCR) has been developed providing lite-weight SAR GMTI capability for Small UAVs. The prototype radar weighs 5-lbs and has demonstrated the extraction of ground moving targets (GMTs) embedded in high-resolution SAR imagery data. Sum and difference channel data is used in a DPCA algorithm to extract the GMTs and display them on the Sum channel high resolution SAR image. Heretofore this type of capability has been reserved for much larger systems such as the JSTARS. Previously small liteweight SARs featured only a single channel and only displayed SAR imagery. Now, with the advent of this new capability, SAR GMTI performance is now possible for small UAV class radars.
We introduce Complete Complementary Codes (CCC) into Multiple-input multiple-output (MIMO) radar in this paper. By using CCC, MIMO radar can achieve perfect elimination of waveform cross-correlation and autocorrelation sidelobes when Doppler shift is zero. When Doppler shift is not zero, we propose a new CCC construction method by using generalized Prouhet-Thue-Morse code (GPTM) which can make CCC Doppler resilient at modest Doppler shift.
Considering frequency diversity, we study different multistatic radar system geometries with multiple transmitters and multiple receivers in order to shape the multistatic ambiguity function in this paper. The multistatic radar system geometries and frequency diversity are shown to play an important role in evaluating radar performance. The simulation results illustrate that using orthogonal frequency waveforms, the multistatic radar system not only could minimize the interference from one waveform to the other, but also achieve better resolution.
In a high mobility wireless channel, the Doppler effect is compounded and must be corrected by using pilot-aided symbols. The pilot symbol rate depends on the severity of the Doppler effect. There are existing algorithms such as differential decision-feedback (D-DF) and double-differential decision-feedback (DD-DF) for single-input single-output (SISO) systems to improve channel tap estimation. Such algorithms improve the bit error rate (BER) performance of pilot symbol aided decision feedback demodulation. In this paper, minimum mean square error (MMSE) estimation was implemented to further improve the channel tap estimation for the D-DF and DD-DF algorithms. BER performance showed significant improvement for higher order modulation schemes. On the other hand, implementation of the new algorithm on quadrature phase-shift keying (QPSK) showed negligible improvement.
The Air Force Institute of Technology (AFIT) has developed an experimental multistatic ultrawideband noise radar network (NoNET) to produce highly accurate, highly resolved imagery of a target scene while maintaining a featureless waveform for efficient spectrum utilization. In this work we present the results of an experimental study to assess the effectiveness of a template replay strategy to mitigate the effects of network latency in the radar network while preserving the Low Probability of Intercept (LPI) nature of the random noise (RN) waveform. Based on laboratory prototype measurements and a MATLAB model of two representative play-back schemes we demonstrate up to a 75% increase in processing efficiency while maintaining the ability to operate in a frequency contested environment.
The problem of radar waveform design for moving target detection in the presence of radio frequency interference (RFI) is considered. The transmit waveform is assumed to be a coherent train of uniform pulses, and the RFI is modeled as a wide-sense stationary random process with a known power spectral density. Both pulse-Doppler processing and matched filter bank processing are considered. For each case, it is shown that a constant modulus pulse can be found that locally maximizes the signal-to-noise ratio while respecting user-specified ambiguity function constraints. It is also shown that under certain conditions approximations can be made that result in identical waveform design problems for both processing schemes. The modulus and ambiguity function constraints render the waveform design problem analytically intractable, but numeric techniques can be employed. Simulation results are provided to demonstrate the efficacy of the approach.
In this paper we describe a novel clutter cancellation platform based on a two stage approach that combines a feedback guided predictive front-end hybrid clutter canceller with high performance back-end filtering and target detection. The front-end architecture is based on an FPGA implementation of a Kalman filter that predicts target locations in real time and removes the target signals from the incoming data prior to hybrid cancellation. The back-end is user configurable and exploits high performance GPU and multi-core parallel hardware to simultaneously compute multiple clutter suppression and target detection algorithms coupled to an intelligent selection strategy for selecting the most accurate result. These target locations are fed back to the FPGA Kalman filter periodically to update the target predictions.
The track-before-detect (TBD) approach can be used to track a single target in a highly noisy radar scene. This is because it makes use of unthresholded observations and incorporates a binary target existence variable into its target state estimation process when implemented as a particle filter (PF). The PF-TBD has been extended to track two targets but only for the special case of the second target spawning from the first target. This paper proposes the extension of the recursive PF-TBD approach to detect multiple targets in low signal-to-noise ratios (SNRs). The new algorithm estimates the joint posterior probability density of all the target trajectories while keeping track of targets entering and leaving the noisy radar scene under observation using multiple modes. The algorithm's successful performance is demonstrated using a simulated three-target example.
This paper describes the application of low-cost radar technology for the detection and analysis of the Doppler signatures of wild life, in particular birds and bats. We demonstrate the ability to extract the wing beat frequency in real-time. Such processing affords a method of readily discriminating between animals and other flying objects such as micro-unmanned aerial vehicles. The timing waveforms used for sampling and integration can have a significant impact on the fidelity of the data that can be captured and on the response time of the system to detect and analyse animals in motion. This work paves the way for future research in support of target classification and conservation ecology.
Our goal in this paper is to discuss several issues and challenges involved with dynamic spectrum access in cognitive radio networks (CRNs). In this context, we introduce three optimization problems for spectrum shaping in cognitive radio networks (CRNs) using orthogonal frequency division multiplexing (OFDM). These optimization problems utilize the concept of transmission hyperspace (TH). The first problem involves maximization of sum-rate of the network under primary and secondary quality-of-service (QoS) constraints utilizing frequency and space dimensions of the TH. The second problem relaxes the omnidirectional assumption and incorporates antenna directionality in the first problem. As for the third problem, time dimension of the TH is added to the first two problems and the resulting problem is formulated as a time scheduling problem. We show that all these problems are NP-hard which requires the development of efficient heuristic algorithms.
In this paper, we deals with the radar optimal waveform design problem using the atomic decomposition radar cross section (RCS) characteristics of target for radar imaging. The proposed method utilizes the radar target characteristics to design matched waveforms, For RCS characteristics of the given target, some signals can be obtained to have a better presented RCS characteristics of target by atomic decomposition method. The optimal signals derived from a target and noise environment. We defined a radar imaging performance by minimizing a distance measure between the reconstructed target RCS characteristics and the actual RCS characteristics. In simulation, we find that a approximation RCS characteristics of target can be obtained by a few atoms.
We built and tested a forward-looking polarimetric GPR which measured the scattering matrix of targets. The scattering matrix can be transformed to determine the polarizability angle, relative phase angle, and target magnitude. These quantities are invariant to rotations about the sensor-to-target axis. Measurements were made on dry sand without a target, sand with a buried polystyrene cylinder, and sand with a buried styrofoam cylinder. As frequency was swept the relative phase changed more rapidly when either cylinder was present compared to the sand alone. The relative phase may be a useful feature for detecting plastic objects and voids in sand.
An adaptive SAR imaging algorithm is proposed in this paper. Assume that the received radar data is echoes from isolated point targets, a signal model is constructed. The parameters of the signal model are estimated, and can be used to characterize a single scatterer. Then the estimated parameter space can be mapped to form SAR image. Simulation experiment is used to validate the algorithm.
The topic of waveform design is investigated for a multistatic radar scenario where it is assumed that one radar sensor is transmitting while the remaining sensors receive. This is accomplished by using an information-theoretic measure (i.e., mutual information between the image and the radar return) as an objective function for waveform optimization. Simulations are given to illustrate performance gains in image quality metrics.
In this paper, an application of the tunable Q-Factor wavelet transform (TQWT) to a maritime object classification problem is demonstrated. The TQWT, which depends on the two main parameters of the Q-factor and asymptotic redundancy, matches the oscillatory behaviour of the signal of interest when tuned. The approach, which differs from the Fourier and Wavelet transforms, decomposes a signal into a “high-Q-factor” and “low-Q-factor” component, and can be used to distinguish two radar range profiles of different oscillatory nature. The results of the paper show that the TQWT can provide sparse representation for some signals and that morphological component analysis (MCA) can be used to differentiate two radar signals based on their TQWT parameters.
Suppression of wideband signals contaminated by pulsed interference is undertaken using synchronous temporal blanking. This repetitive time gating of the raw signaling waveforms employed at physical level of communications, radar or sensor systems can sometimes largely remove interference, but leaves a waveform remnant that will often be insufficient to permit satisfactory restoration of the entire signal waveform through simple interpolatory filtering. If the interference is periodic the requisite gating of the digitized signal can be treated as periodic nonuniform decimation. We undertake restoration using the “alias unraveling” approach, introducing a digital filter design of the sampling pulse which leads to significantly better conditioning of the unraveling matrix, and affords a very considerable extension of resilience to interferer duty cycle.
Different from traditional SAR system, the cognitive SAR proposed in this paper transmits various waveforms to the illuminated scene. The waveforms are designed offline based on the property of clutter and targets of interested. These waveforms are selected and optimized online by priori knowledge and the backscattered signal. The cognitive ability of SAR system can be gained by the matching illumination to the target and scene adaptively.
