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This study investigated and compared the practical methods used for the efficient Field- Programmable Gate Array (FPGA) implementation of space-time adaptive processing (STAP). The most important part of calculating the STAP weights is the QR decomposition (QRD), which can be implemented using the modified Gram-Schmidt (MGS) algorithm. The results show that the method that uses QRD with less computational burden leads to a more effective implementation. Its structure was parameterised with the vector size to create a trade-off between the hardware and performance factors. For this purpose, QRD-MGS algorithm was first modified to increase the speed, and then the STAP weight vector was calculated. The implementation results show that decreasing the vector size decreases the resource utilisation, computational burden and the consumption power. While the computation time increases slightly, the updated rate of the STAP weights is maintained. For example, the STAP weights in a system with 6 antenna arr...

Space---time adaptive processing (STAP) has an enormous computational complexity which has confined its practical applications. In this paper, we present an implementation based on field programmable gate array (FPGA) for the most computationally intensive portion of STAP, which is the adaptive weights calculation. This involves solving a set of linear equations that uses the radar return data. In the proposed architecture, QR decomposition block is the most computationally part which is parameterized by vector size to create a trade-off between the hardware resources consumption and delay. To achieve an efficient and high-speed structure, the architecture is simulated and implemented in two cases: single-vector and multi-vector. Results show that the calculation time of weights in single-vector design is less than that of multi-vector case. The delay of weights for 6ý×ý8ý×ý120 data cube using vector size of 17 and the maximum clock frequency of 259 MHz is 139 μs, and GFLOPs/Watt is 3.89 for implementation on Arria 10 floating point FPGA. Therefore, the presented approach can realize the real-time requirement and floating point computation of the adaptive weights for STAP.

HF radar systems are widely used for the measurement of ocean surface currents and wave parameters. Theoretical analysis of high frequency radio wave backscattering from the sea has been derived in the past. Also, the relationship between Bragg frequencies and surface currents is determined and has been proved by HF radars. It is known that when surface currents are present, the Bragg frequencies move to new frequencies. Their movement is proportional to the surface current in the direction toward radar. In addition, it is observed that sea surface current changes the second order part of the spectrum of radar reflection from the sea surface. In this paper, a theoretical analysis of high-frequency radio wave backscattering from the sea in the presence of constant surface current provides a formula for the second order part of the spectrum of sea echo. This resulted in similar formulas of others but the process of formula extraction is different. This method may be used for the determination of the second order spectra of radar cross section in the presence of other types of surface current.

Battery State of Health (SOH) is a vital parameter to maintain the battery as well. As a matter of fact this is the ability of a battery to store energy. It is needless to say that during the lifetime of a battery, its performance gradually decreases, so by using a suitable Battery Management System (BMS), safety and improvement of the usage lifetime can be guaranteed. The traditional methods for indicating usable capacity are basically based on output voltage measuring in the discharge process with a constant current pulse. However, due to load changes the discharge current of batteries in operation almost always fluctuates, which makes it hard to measure the online capacity measurement for the traditional methods. To overcome the above problems, a filter design approach is proposed in this paper to estimate the SOH. This paper by using a generic second order equivalent circuit, that has been used for both VRLA and Li-ion batteries before, presents comparison of three well-known filters performance in the battery SOH estimation application. Parameter estimation have been applied in order to compare and contrast. To verify the performance of the methods, simulations were built in Matlab and final results show accuracy of filters and claim merits and demerits of them.

In fixed rate, state space models are conventionally used to track the maneuvering objects. In contrast to fixed rate models, recently introduced variable rate particle filter (VRPF) is capable of tracking the target with a small number of states by imposing a Gamma distribution on the state arrival times, while the object trajectory is approached by a single dynamic motion model. It cannot estimate the position of targets in very high maneuvering regions. Thus, multiple model VRPF (MM-VRPF) method is utilized to overcome this shortage using various dynamic models. A weak point of particle filter is a phenomenon called degeneracy which even exists in MM-VRPF structure. In this study, differential evolution method is exploited to improve the mentioned method, and a novel structure called MM-VRPF with self-adaptive differential evolution is introduced. The simulation results, particularly those related to bearing only tracking achieved from the maneuvering target, revealed that the proposed structure has high performance, while it still takes advantage of variable rate structure.

There are various interference sources in the environment of HFSWR which limit detection of small targets. Signal processing techniques have been proposed to reject clutter and interference signals and improve the performance of detection systems. Considering that sea clutter can be modeled by Auto-regressive processes, in the work presented here, single-sensor AR-GC-GLR detector examined for actual sea clutter gathered by a HFSWR. Using simulation and practical measurements, the performance of this detector will be proposed. Findings show that the performance of detection will improve by fewer numbers of the interference samples. This dependency is more than ground clutter with sparse plant coverage, which is considered at AR-GC-GLR detector.

This article explores the M-AR-GLR detector with regard to the Gaussian spectrum on the actual sea clutter that is collected by multi-channel HFSWR. In this study, we will show that a large number of elements have no effect on the correlation matrix of interference samples that cause to reduce the compute volume. Spatial dependence of received sample is less, detector performance will be better. Since the detector performance with fewer number of secondary data is suitable, it can be used in HFSWR. Also in the small signal to noise ratios, the proposed detector can be better than Kelly's GLR [1].

Space-Time Adaptive Processing (STAP) can harness the efficacy of interference and clutter significantly. Calculations of the STAP weights involve solving linear equations which require very intensive computations. In this paper, the QR decomposition (QRD) using the modified gram-schmidt (MGS) algorithm is parameterized with vector size to create a trade-off between the hardware resources utilization and computation time. To achieve an efficient floating point structure, the proposed architecture of QRD-MGS algorithm is simulated and implemented in two modes: single-vector and multi-vector. Results show that the multi-vector method can lead to a high-performance design with higher operating frequency, lower power consumption, and less resource utilization than the single-vector method. For example, Modelism simulations show that the decomposition of a [Formula: see text] matrix with vector size of 17 takes 7.86[Formula: see text][Formula: see text]s with the maximum clock frequency of 282[Formula: see text]MHz, for implementation on the Arria10 FPGA. In real STAP applications, the matrix sizes are too large to be fit on FPGAs and the update rate of the weights are high. Therefore, this method can fit any matrix in the contemporary FPGAs with an acceptable update rate.

In standard target tracking, algorithms assume synchronous and identical sampling rate for measurement and state processes. Contrary to these methods particle filter is proposed with variable rate. These filters use a restricted number of states, and a Gamma distribution is applied at state arrival time so that the maneuvering targets could be tracked. Although this structure is capable of tracking a wide range of targets motion features using linear, curvilinear motion dynamics, it suffers from a basic weak point. It cannot estimate the position of targets in high maneuvering regions. Thus, multiple model variable rate particle filter (MM-VRPF) is utilized to overcome this shortage using various dynamic models. A weak point of a particle filter is a phenomenon called degeneracy that even exists in MM-VRPF structure. In this study, differential evolution method, is exploited to improve the mentioned method and a novel structure called multiple model variable rate particle filter with differential evolution (MM-VRPF with DE) is introduced. The simulation results of a bearing only tracking achieved from a maneuvering target, revealed that the proposed structure has better performance while it maintains advantages of variable rate structure.