An adaptive tracking algorithm for bistatic radar system with an independent receiver

In this paper, we have identified the system instability factors in a bistatic radar system using pulse chasing and considered their effects on the bistatic receiver's MTI(Moving Target Indication) improvement performance. The pulse chasing is a method to efficiently scan a restricted search area within the limited transmitter power and time in a bistatic radar and to track a series of transmitted pulses using the receiver beam which has ideally matched to the pulse propagation rate. In this paper, we have discussed the interrelationship between the pulse chasing and time and frequency/phase synchronization and described the effects of the identified system instability factors on two kinds of MTI filter configuration, single delay-line and double delay-line, in the bistatic radar. And also, we have confirmed that the overall system improvement is restricted by a lower improvement factor among identified them, and discussed the allowable tolerance of the time and frequency/phase synchronization in the bistatic system.

An adaptive template matching-based single object tracking algorithm with parallel acceleration

The receiver of a global navigation satellite system (GNSS) is likely to lose tracking for a GNSS signal in some degraded environments. To solve this problem, this paper designs a new adaptive tracking algorithm for GNSS signals. First, we design an error extraction module to extract phase errors so that a CTL can estimate phase errors without using a loop discriminator, which can reduce the requirements of the carrier-to-noise ratio (CNR) of GNSS signals. Second, we design a motion detection module to detect the real-time movement status of GNSS receivers. Then, using its detecting results as inputs, we design an algorithm to automatically make the CTL switch between a second-order loop and a third-order loop. Third, we design a bandwidth-adjusting module to adjust the bandwidth of CTL, according to the CNR and the movement status of a GNSS receiver. Finally, a simulation is performed to verify that our adaptive carrier tracking algorithm can effectively improve the precision of CTL, as well as enhance its dynamic range.

When tracking maneuvering targets, the sudden changes of target states and nonlinear problems in passive tracking cause serious decline and even divergence in the performance of some conventional algorithms. Taking this into account, a novel adaptive tracking algorithm for maneuvering targets is proposed in this paper. First, a new weighed factor could adaptively adjust the filter gain is introduced in Modified Input Estimation (MIE) algorithm to improve its tracking performance. Then, the least squares technique is combined for tracking passive multi-sensor maneuvering targets in high accuracy without any priori information of maneuver. Simulation results show the effectiveness of the proposed algorithm. Compared with the traditional MIE and fuzzy MIE algorithm, this algorithm has a better tracking performance.

Visual tracking has been widely used in robot systems, and numerous approaches for visual tracking have been proposed. However, developing a robust and real-time visual tracking algorithm which can adaptively track the varying appearance of target under challenging conditions for mobile robot is still an open problem. This paper presents an adaptive probabilistic tracking algorithm with multiple cues integration. An effective evaluation function is proposed to evaluate each cue used for tracking based on their discriminating abilities between foreground and background. Then the likelihood functions of the cues are integrated in particle filter framework with different weights determined based on the evaluation scores. A novel target model updating strategy is proposed to adapt to the varying appearance of target resisting gradual drift which is still an unsolved problem in many adaptive tracking algorithms. Experimental results on a mobile robot demonstrate the robust performance of the proposed algorithm under challenging conditions.

The article discusses the issues of building bistatic radar systems, which are supposed to be used in the National Guard of Ukraine to perform tasks for the protection of important state facilities. The peculiarities of the construction of this system are associated with the absence of its own transmitter in its composition and the use of the «translucent» location effect. A general configuration of bistatic coherent radar systems is proposed and the main problems of signal processing in such a radar system are considered. The central problem of constructing this bistatic radar system is the effect of an interfering direct signal from a radiation source (usually continuous), penetrating into the receiving path along the side lobes of its antenna. Using analytical expressions, the dependences of the ratio of the power of the useful and penetrating signals for delivery vehicles and intruders on the azimuth of the target relative to the receiver at various fixed ranges are obtained. The analysis of the dependences showed that at ranges to the target less than 5 km, the ratio of the signal capability does not depend on the target azimuth. With an increase in the detection range and an  increase  in the azimuth of the target, the signal power  ratio  tends,  respectively,  to -65 dB for delivery vehicles and -90 dB for intruders. Ways of suppressing the direct signal are considered. To improve the suppression of such a signal, an adaptive compensator as part of the receiver is proposed. It consists of a feedback control system and takes into account the ratio of the useful and penetrating signals, information about their delay and structure. The general structure of a receiver with a compensation system for a penetrating signal and a block diagram of an adaptive compensation device are presented. Direction for further research. Particular attention should be paid to defining the characteristics of the bistatic covert surveillance system in terms of compensating for the influence of the underlying surface and local objects.

The strong tracking filter (STF) can reduce adaptively estimate bias and thus has ability to track maneuvering target in nonlinear systems. However, STF achieves the perfect performance in maneuvering segment at a cost of the precision in non-maneuvering segment. So based on the strong tracking filter, a new adaptive tracking algorithm (modified strong tracking filter, MSTF) is derived in this paper, which is also suitable for tracking maneuvering target and has improved the precision of STF in non-maneuvering segment as well as maneuvering segment. The Monte-Carlo simulation results show that the MSTF algorithm has a more excellent performance than STF and can estimate efficiently

Three different algorithms based on the multimodel partitioning approach are designed for the problem of tracking a manoeuvering target utilizing aircraft derived data (ADD) measurements of heading. These are subsequently evaluated with respect to their performance in the sense of the mean square estimation error by means of simulations in three realistic tracking scenarios. The extend Kalman filter (EKF) for this problem has been used as a measure of comparison for the performance of all algorithms. The results indicate the improvement in performance obtained when using adaptive techniques to track manoeuvering targets instead of the non-adaptive EKF.

The problem of passively tracking underwater maneuvering targets using time differentials of received sound waves is explored. The paper next develops a two-dimenslonal, adaptive tracking algorithm that gives an unbiased range estimate as a target makes random step changes in radial velocity.

To investigate the problem of improving tracking performance in wide monitoring area for netted multi-sensor system, an adaptive tracking algorithm with variable observation (ATVO) based on the scheme of monitoring area segmentation is derived. During the computational process of target tracking, the sub monitoring field of the target is calculated by its position estimation and observation distribution matrix (ODM) is introduced to adaptively adjust measurement system of equations. Observing equations with low accuracy are eliminated and observing equations with high accuracy are reserved. Simulation results show that ATVO not only ensures the precision of state estimation but also reduces the dimension number of observation equations. The accuracy and real-time capability of tracking moving target are improved.

The purpose of this letter is to solve the space-time off-grid problem caused by the extremely inhomogeneous clutter spectrum under the space/airborne bistatic system. For the estimation of inhomogeneous clutter spectrum, the method of sparse recovery (SR) is usually adopted. However, performance of sparse recovery may degrade since the real clutter point often does not fall in the center of the space-time grid. To deal with this issue, a local mesh splitting algorithm is proposed in this letter. First, select the space-time vector atoms which are most relevant to the residuals by subspace projection. Then, iterative local mesh splitting is performed around the selected atoms to match the real clutter points. Finally, the clutter subspace is reconstructed from all matched atoms selected by global and local iterations. The simulation results show that the method can obtain better accuracy with lower computational effort.

Passive Bistatic radar is an effective and low-cost approach to surveillance in an area of interest. In many bistatic radar configurations, the impact of the Direct Signal Interference (DSI) is minimized by the physical locations of the transmitter and the receiver on the bistatic plane. In the satellite-based bistatic radar, the bistatic baseline is comparable to the distance from the transmitter to the target of interest; as such, the signal from the transmitter no longer dominates at the receiver. A feature that minimizes the computational complexity of the necessary algorithms to suppress DSI. Although the bistatic radar system is passive, it is susceptible to jamming when spotted. Interferers can affect the operation of a bistatic system if not adequately addressed. The existence of jammers in a bistatic environment can have a destructive effect on the surveillance channel. In specific radar modes, like track initiation and track maintenance, the Minimum Variance Distrotionless Response (MVDR) offers many advantages as it seeks to maximize power in the look direction. In this paper, we evaluate the performance of the combination of the MVDR and adaptive nulling in a satellite-based bistatic radar and Normalized Least Mean Square (NLMS) as a DSI canceller in a jamming environment.

At endmost stage of flight for tracking, target often tends to fulfill whole scene. If the target maneuvers rapidly, it is easy to lose the target. In this paper, an adaptive template update tracking algorithm model based on graph-regularized salience is proposed to solve this problem. The proposed algorithm introduces the graph-regularized salience model into the target tracking framework. When the target’s size exceeds the setting threshold or the target will overflow the screen in the setting number of frames, that is, the target’s lifetime, salience model is used to extract the most prominent position of the target to update the template, in order to ensure that the template has a good response to the target and ensure the stability of target tracking.

A bistatic/monostatic HF surface wave radar (HFSWR) experiment was conducted using both the transmit and receive systems of the HFSWR at Cape Race, Newfoundland and the receive system of the HFSWR at Cape Bonavista, Newfoundland. While the HFSWR at Cape Race operated in the monostatic mode, the transmitter at Cape Race and the receiver at Cape Bonavista were synchronized via the Global Positioning System to operate in the bistatic mode. The results of the experiment show that the range of the clutter return from a specified layer in the ionosphere in the bistatic radar, and the range of the clutter return from the same layer in the monostatic radar follow, approximately, the relationship specified by the Pythagorean theorem, i.e., R=(h/sup 2/+(L/2)/sup 2/)/sup 1/2/, where h is the virtual height of the specified layer in the ionosphere, which is also the range of the clutter return in the monostatic radar case, and L is the separation between the transmitter and receiver in the bistatic configuration. Based on these observations, we calculated the coverage areas of bistatic HFSWR systems for water-entry areas along coastlines. The calculation shows that a bistatic HFSWR system has the potential to significantly improve the nighttime coverage area over a monostatic system, while, approximately, maintaining the same daytime coverage.

Modulation recognition of single‐component radar signals has been widely studied in the fewer years. Meanwhile, as the electromagnetic environment in the actual battlefields is ever‐increasingly complicated, the demand for the modulation recognition of multi‐component radar signals is also increasing. However, the modulation recognition of single‐component radar signals performs poorly on multi‐component radar signals. To address this problem, a novel modulation recognition method of multi‐component radar signals based on the deep convolutional neural network with a convolutional block attention module (DCNN‐CBAM) is proposed, and the network architecture mainly includes mobile‐inverted bottleneck convolution (MBConv), Fused‐MBConv, and convolutional block attention module (CBAM). An adaptive optimization algorithm Adam with weighted gradient and dynamic bound is proposed for accelerating convergence speed and generalisation performance. The proposed method can not only recognise dual‐component and three‐component radar signals but also four‐component radar signals. The experimental results demonstrate that the recognition accuracies of all radar signals modulation types are more than 83.57% at −8 dB for dual‐component radar signals, 67.41% for three‐component radar signals, and 57.95% for four‐component radar signals, which verify the better recognition performance of DCNN‐CBAM for multi‐component radar signals. The recognition accuracy of the proposed DCNN‐CBAM for dual‐component radar signals can be up to 95.95% at −6 dB, which shows superior recognition performance over the residual method. This work provides an essential guidance for enhancing the recognition performance of multi‐component radar signals.