Sandeep P. Sira

Dynamic Configuration of Time-Varying Waveforms for Agile Sensing and Tracking in Clutter

The advent of waveform-agile sensors has enabled the design of tracking systems where the transmitted waveform is changed on-the-fly in response to the trackers requirements. This approach can provide performance improvements over individual optimization of the sensor waveform or the tracking algorithm. In this paper, we consider joint sensor configuration and tracking for the problem of tracking a single target in the presence of clutter using range and range-rate measurements obtained by waveform-agile, active sensors in a narrowband environment. We propose an algorithm to select and configure linear and nonlinear frequency-modulated waveforms to minimize the predicted mean square error (MSE) in the target state estimate; the MSE is predicted using the Cramer-Rao lower bound on the measurement error in conjunction with the unscented transform. We further extend our algorithm to match wideband environments, and we demonstrate the algorithm performance through a Monte Carlo simulation of a radar tracking example.

Adaptive Waveform Design for Improved Detection of Low-RCS Targets in Heavy Sea Clutter

The dynamic adaptation of waveforms for transmission by active radar has been facilitated by the availability of waveform-agile sensors. In this paper, we propose a method to employ waveform agility to improve the detection of low radar-cross section (RCS) targets on the ocean surface that present low signal-to-clutter ratios due to high sea states and low grazing angles. Employing the expectation-maximization algorithm to estimate the time-varying parameters for compound-Gaussian sea clutter, we develop a generalized likelihood ratio test (GLRT) detector and identify a range bin of interest. The clutter estimates are then used to dynamically design a phase-modulated waveform that minimizes the out-of-bin clutter contributions to this range bin. A simulation based on parameters derived from real sea clutter data demonstrates that our approach provides around 10 dB improvement in detection performance over a nonadaptive system

Waveform-agile sensing for tracking

Waveform-agile sensing is fast becoming an important technique for improving sensor performance in applications such as radar, sonar, biomedicine, and communications. The paper provided an overview of research work on waveform-agile target tracking. From both control theoretic and information theoretic perspectives, waveforms can be selected to optimize a tracking performance criterion such as minimizing the tracking MSE or maximizing target information retrieval. The waveforms can be designed directly based on their estimation resolution properties, selected from a class of waveforms with varying parameter values over a feasible sampling grid in the time-frequency plane, or obtained from different waveform libraries.

Time-varying waveform selection and configuration for agile sensors in tracking applications

We present an algorithm for dynamic waveform selection and configuration for agile sensors in a target tracking application. The method selects and configures generalized frequency-modulated (FM) waveforms with time-varying signatures to minimize the predicted mean squared tracking error. We derive the Cramer-Rao lower bound (CRLB) for these signals and use the CRLB in conjunction with the unscented transform to compute the predicted mean square error. The method is computationally feasible and applicable to nonlinear scenarios as demonstrated in our simulations.

Waveform design and scheduling for agile sensors for target tracking

Waveform agile sensors are capable of modifying transmitted waveforms to adapt to a particular scenario of interest. In this paper, we investigate the problem of optimizing the pulse length and frequency sweep rate of transmitted sonar waveforms to track a target moving in two dimensions using two sensors. We use simultaneous perturbation stochastic approximation (SPSA) in conjunction with a particle filter tracker to optimize the waveform parameters to minimize the expected squared tracking error at each time (greedy minimization). Simulation results show that waveforms with optimized parameters provide better performance than those that use fixed parameters.

Waveform-Agile Sensing for Tracking Multiple Targets in Clutter

In this paper, we consider the problem of scheduling the waveform transmitted by waveform-agile radar sensors to track multiple targets in clutter. A number of generalized frequency modulated chirps with trapezoidal envelope form the library of waveforms available to the sensors, which obtain measurements using a nonlinear observation model. A joint probabilistic data association filter is used to track the target, and the waveform selection is made so as to minimize the predicted mean square tracking error. We provide simulation results to show that the scheduling improves the tracking performance of multiple targets even in the presence of clutter.

Waveform Scheduling in Wideband Environments

The time-variation due to Doppler scaling effects, coupled with scattering due to multipath propagation, can severely limit the performance of wideband systems. In this paper, we examine the dynamic configuration of transmitted waveforms for agile sensing to increase tracking performance in wideband environments. Using wideband frequency modulated waveforms, we present an algorithm for predicting the mean square tracking error and selecting the waveform that minimizes it in a target tracking application with a nonlinear observations model. The algorithm is based on the Cramer-Rao lower bound on the measurement errors that is computed using the wideband ambiguity function. Using simulations, we demonstrate the improved performance provided by scheduling over fixed configurations

Advances in Waveform-Agile Sensing for Tracking

Recent advances in sensor technology and information processing afford a new flexibility in the design of waveforms for agile sensing. Sensors are now developed with the ability to dynamically choose their transmit or receive waveforms in order to optimize an objective cost function. This has exposed a new paradigm of significant performance improvements in active sensing: dynamic waveform adaptation to environment conditions, target structures, or information features. The manuscript provides a review of recent advances in waveform-agile sensing for target tracking applications. A dynamic waveform selection and configuration scheme is developed for two active sensors that track one or multiple mobile targets. A detailed description of two sequential Monte Carlo algorithms for agile tracking are presented, together with relevant Matlab code and simulation studies, to demonstrate the benefits of dynamic waveform adaptation. The work will be of interest not only to practitioners of rada and sonar, but also other applications where waveforms can be dynamically designed, such as communications and biosensing. Table of Contents: Waveform-Agile Target Tracking Application Formulation / Dynamic Waveform Selection with Application to Narrowband and Wideband Environments / Dynamic Waveform Selection for Tracking in Clutter / Conclusions / CRLB Evaluation for Gaussian Envelope GFM Chirp from the Ambiguity Function / CRLB Evaluation from the Complex Envelope

Maximizing Detection Performancewithwaveform Design for Sensing in Heavy Sea Clutter

In this paper, we consider a radar system capable of adaptively adjusting its transmitted waveform to mitigate the effect of the environment and improve detection performance. We thus bring the potential of waveform agility to bear on the challenging problem of target detection in heavy sea clutter. Using a two-stage procedure, we first estimate the statistics of the sea clutter in the vicinity of a predicted target. A phase-modulated waveform is then designed and transmitted so as to maximize the generalized likelihood ratio at the predicted target location, thus improving the signal-to-clutter ratio (SCR). Employing the compound-Gaussian (CG) model, we exploit a subspace-based approach to further mitigate sea clutter and deliver improved detection performance. Simulations illustrate the effectiveness of our method.

A Subspace-Based Approach to Sea Clutter Suppression for Improved Target Detection

A key issue in detection of small targets on the ocean surface using active radar is low signal-to-clutter ratio (SCR), particularly in situations involving low grazing angle and high sea state. When sufficiently high pulse repetition rates are available, it is possible to obtain several samples of the clutter in a time interval short enough that key contributors to the clutter remain approximately stationary. This paper develops an approach to estimate the clutter subspace in such scenarios and exploit this estimate for clutter suppression and improved detection performance.