Target Amplitude Fluctuations Research Articles

Performance analysis of complex pseudo-spectrum based velocity filtering for fluctuating target track-before-detect

Complex pseudo-spectrum (CPS) based track-before-detect (TBD) method is capable of improving the detection performance through the use of phase information in integration process. However, existing CPS based approach is derived for non-fluctuating target detection. Its analysis and design are not suitable for fluctuating target detection. This paper considers multiframe detection of fluctuating targets via CPS based velocity filtering in complex Gaussian noise. The common Swerling 1 and 3 models are used to describe target amplitude fluctuation from scan to scan. The theoretical output envelope of the fluctuating target is derived in detail. The velocity filter bank of CPS based approach for fluctuating targets is investigated and designed according to the derived target envelope. A numerical strategy is provided for filter bank design in the case of target fluctuation. The theoretical receiver operating characteristic (ROC) curves in the case of Swerling 1 and 3 fluctuations are derived in detail, respectively. Simulation experiments are provided to evaluate the validity of CPS based TBD in dealing with Swerling 1 and 3 types of fluctuating targets.

Bernoulli track‐before‐detect filter for maritime radar

In this work, the authors study the problem of detecting and tracking small targets using high-resolution maritime radar, where sea clutter is correlated in range, and its amplitude fluctuations are characterised by occasional spikes. To tackle this problem, they develop a Bernoulli track-before-detect filter, as the optimal recursive Bayesian detector/estimator of target state and its presence in noise. A realistic clutter model, in the form of the K-distribution with unknown distribution parameters, is adopted. Target amplitude fluctuations are also included in the model. The detection and tracking improvement are demonstrated by simulations and compared against a conventional point target tracking algorithm.

Sequential Monte Carlo Cardinalized probability hypothesized density filter based on Track-Before-Detect for fluctuating targets in heavy-tailed clutter

In this paper, a Sequential Monte Carlo Cardinalized Probability Hypothesized Density (SMC-CPHD) filter based on Track-Before-Detect (TBD) algorithm in heavy-tailed clutter is proposed. The CPHD filter, which jointly propagates the first-order statistical moment and cardinality distribution, is an effective and efficient solution to multitarget filter problem in the presence of data association uncertainty, detect uncertainty, and time-varying. The TBD algorithm is extremely sensitive to the presence of clutter spikes in the data as the formulation of the TBD filter is dependent on knowledge of underlying clutter behavior. Therefore, the clutter is modeled in terms of K distribution with sea surface background and G0 distribution with primary forest background, while the popular Swerling targets of type 1 and 3 are considered to capture the target amplitude fluctuation frame-to-frame in this paper. The utilization of K distribution and G0 distribution clutter models instead of conventional Rayleigh distribution are verified through analysis of simulated and realistic data. The relevant results prove that proposed SMC-CPHD-TBD algorithm based on K distribution model and G0 distribution model can improve the detect and tracking performance over the conventional method in heavy-tailed clutter scenario. Additionally, the numerical simulations show that proposed CPHD-TBD algorithm can output the multitarget trajectories in the better precision with considerably less computational complexity compared with DP-TBD algorithm.

Knowledge-Based Track-Before-Detect Strategies for Fluctuating Targets in <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math> </inline-formula>-Distributed Clutter

In this paper, we address the problem of detecting fluctuating targets in heavy-tailed clutter through the use of dynamic programming based track-before-detect (DP-TBD) in radar systems. The clutter is modeled in terms of $K$ distribution, while the well-known Swerling targets of types 1 and 3 are considered to capture the target amplitude fluctuation scan-to-scan. Conventional DP-TBD suffers significant performance loss in this case due to the high frequency of target-like outliers. In this paper, we resort to the knowledge-based techniques, i.e., use a priori information to enhance radar detection performance. More precisely, knowledge-based DP-TBD (KB-DP-TBD) strategies are developed by incorporating the amplitude information into the integration process of DP-TBD. Simulations are used to assess performances and computational complexities of different DP-TBD strategies. The relevant result is that KB-DP-TBD can improve the detection performance over the conventional DP-TBD, especially for very heavy-tailed $K$ -distributed clutter.

Direction finding accuracy of sequential lobing under target amplitude fluctuations

Using recently developed statistical target fluctuation models, the accuracy of sequential lobing is analytically studied. The study shows that the sequential lobing method suffers from a significant performance loss, in comparison with the monopulse method, for the Rayleigh fluctuation model. For other fluctuation models, the performance loss gradually decreases as the amplitude spread associated with the fluctuation gets smaller. The present study aims to analytically quantify the mentioned accuracy loss because of target amplitude fluctuation and aims to assess the engineering utility of the sequential lobing method for practical applications. To that aim, two operational regimes, namely the noise-limited and target-fluctuation-limited operational regimes, are introduced and the boundary between regimes, separating acceptable and unacceptable performance loss with respect to monopulse method, is analytically determined.

Frequency-based target localization methods for widely separated MIMO radar

Frequency-based localization methods are widely used to find emitter locations. Several techniques are described in the literature for emitter localization based on Doppler frequency shifts. These techniques can be used efficiently for emitter localization by using narrowband signals. Although these methods are simple and efficient, the application to the radar systems for target localization is very limited. In this paper, a new low-complexity target localization method, Target Localization via Doppler Frequencies (TLDF), for Doppler-only Multi-Input, Multi-Output (MIMO) radar with widely separated stations is described. By using widely separated MIMO radars with unmodulated continuous wave signals, the received frequencies and the Doppler shifts can be estimated efficiently. The position and the velocity of the target can be found from these estimated frequencies by a search in the position space. As the Doppler frequency is estimated efficiently, not only the target velocity but also the direction of the target is estimated accurately with the TLDF method. The Cramer-Rao Bounds (CRB) are calculated for the target velocity and the target position estimations in two-dimensional space. In simulations, the proposed method is compared with the iso-Doppler curves-based traditional method and with the CRB for different geometries. The performance of the proposed method is not affected from the target amplitude fluctuations because of its frequency-based nature. Finally, the comparison between the frequency-only MIMO radar and the pulsed monostatic radar is investigated, and the simplicity and the efficiency of the proposed method are demonstrated.

Bayesian track-before-detect algorithm with target amplitude fluctuation based on expectation–maximisation estimation

Bayesian track-before-detect is an efficient approach to detect low observable targets. Before implementing Bayesian track-before-detect, one needs to exactly ascertain the target motion model and the measurement model. When the target return amplitude fluctuates, the target return amplitude of the measurement model is not known a priori. In the scenario, standard Bayesian track-before-detect algorithms such as particle filters, which assume perfect knowledge of the model parameters, cannot work well. In this study, the authors propose an expectation–maximisation (EM) algorithm for Bayesian track-before-detect with target amplitude fluctuation, in which the fluctuation models are incorporated into the likelihood function, and the average target return amplitude is estimated by the EM algorithm. The simulation results show that the average target return amplitude can be estimated by the EM algorithm, which is helpful in improving the performance of detection and tracking. Therefore it is feasible to apply the EM algorithm to Bayesian track-before-detect with target amplitude fluctuation.

Recursive track-before-detect with target amplitude fluctuations

A particle-based track-before-detect filtering algorithm is presented. This algorithm incorporates the Swerling family of target amplitude fluctuation models in order to capture the effect of radar cross-section changes that a target would present to a sensor over time. The filter is designed with an existence variable, to determine the presence of a target in the data, and an efficient method of incorporating this variable in a particle filter scheme is developed. Results of the algorithm on simulated data show a significant gain in detection performance through accurately modelling the target amplitude fluctuations.

Benchmark for radar allocation and tracking in ECM

A benchmark problem for tracking maneuvering targets is presented. The benchmark problem involves beam pointing control of a phased array (i.e., agile beam) radar against highly maneuvering targets in the presence of false alarms (FAs) and electronic counter measurements (ECM). The testbed simulation described includes the effects of target amplitude fluctuations, beamshape, missed detections, FAs, finite resolution, target maneuvers, and track loss. Multiple waveforms are included in the benchmark so that the radar energy can be coordinated with the tracking algorithm. The ECM includes a standoff jammer (SOJ) broadcasting wideband noise and targets attempting range gate pull off (RGPO). The limits on the position and maneuverability of the targets are given along with descriptions of six target trajectories. The best tracking algorithm is the one that minimizes a weighted average of the radar energy and radar time, while satisfying a constraint of 4% on the maximum number of lost tracks, The radar model, the ECM techniques, the target scenarios, and performance criteria for the benchmark are presented.