Track-before-detect Method Research Articles

Continuous track-before-detect method for rotating radars

Mechanically steered scanning radars receive measurements in different azimuths sequentially rather than simultaneously for target detection and tracking. However, in conventional track-before-detect (TBD) methods, the requirement of waiting for all measurements of a whole scan, referring to a certain azimuth, leads to significant processing delays and boundary effect, which appears as that targets around the boundary may be scanned 0 or 2 times in a scan. This may also cause degraded detection and estimation performances. To solve these problems, a continuous TBD method is proposed for target detection and tracking in rotating radars. A space–time joint retrodiction method is presented to produce precise revisit intervals and retrodicted positions, which contribute to immediate and accurate energy integration. Once measurements in an azimuth cell are received, each range cell in this azimuth is retrodicted according to the space–time joint retrodiction method with an assumed velocity. Then, a number S of pseudo-spectra are constructed around the cell and its retrodicted positions in the past S−1 scans. Samples of each pseudo-spectrum are added onto corresponding cells for energy accumulation. After that, some cells in the past (S−1)th scan and their guard and reference cells complete energy integration, and thus are detected immediately. Continuous integration and detection are realized without waiting for measurements at the boundary of a scan. This eliminates the boundary effect and minimizes the processing latency. Experimental results corroborate the validity of the continuous TBD method.

Multi-frame non-coherent track-before-detect method for weak tones in passive sonar

In order to solve the problem of performance degradation of track-before-detect (TBD) methods using single frame information when received signals exhibit a large fluctuation. A TBD method utilizing multi-frame information is proposed. Specifically, the novel measurement model and likelihood ratio function based on the non-coherent accumulation of multi-frame data are established. Multi-frame integration greatly improves the signal-to-noise ratio (SNR) of measurements. Furthermore, the derived likelihood ratio function can more effectively describe the statistical characteristics of the spectrum measurements. Combining these two advantages can improve the detection performance of weak tones for passive sonar. The simulation and experimental data results both demonstrate the advantages of the proposed method.

Multi-frame coherent track-before-detect method for weak tones in passive sonar

To address the performance degradation caused by power fluctuations of tones, the multi-frame coherent integration-based track-before-detect (TBD) method is proposed. In the proposed method, the high gain advantage of coherent integration and the information accumulation ability of multi-frame TBD method based on batch processing technique are combined to improve the detection and tracking performance of fluctuating tones. Firstly, a new measurement model is established with batch processing technology and coherent integration across multiple data frames is achieved utilizing the tonal coherence, thus the SNR in measurement is greatly improved. Then, a new likelihood ratio function is derived to match the established measurement model, ensuring that TBD processing can be performed correctly. Finally, the effects of the newly established likelihood ratio and the batch length on the detection performance are analyzed in detail through theoretical derivations. The superiorities and robustness of the proposed method are demonstrated through simulation analysis and processing results for sea experimental data.

DP–MHT–TBD: A Dynamic Programming and Multiple Hypothesis Testing-Based Infrared Dim Point Target Detection Algorithm

The detection and tracking of small targets under low signal-to-clutter ratio (SCR) has been a challenging task for infrared search and track (IRST) systems. Track-before-detect (TBD) is a widely-known algorithm which can solve this problem. However, huge computation costs and storage requirements limit its application. To address these issues, a dynamic programming (DP) and multiple hypothesis testing (MHT)-based infrared dim point target detection algorithm (DP–MHT–TBD) is proposed. It consists of three parts. (1) For each pixel in current frame, the second power optimal merit function-based DP is designed and performed in eight search areas to find the target search area that contains the real target trajectory. (2) In the target search area, the parallel MHT model is designed to save the tree-structured trajectory space, and a two-stage strategy is designed to mitigate the contradiction between the redundant trajectories and the requirements of more trajectories under low SCR. After constant false alarm segmentation of the energy accumulation map, the preliminary candidate points can be obtained. (3) The target tracking method is designed to eliminate false alarms. In this work, an efficient second power optimal merit function-based DP is designed to find the target search area for each pixel, which greatly reduces the trajectory search space. A two-stage MHT model, in which pruning for the tree-structured trajectory space is avoided and all trajectories can be processed in parallel, is designed to further reduce the hypothesis space exponentially. This model greatly reduces computational complexity and saves storage space, improving the engineering application of the TBD method. The DP–MHT–TBD not only takes advantage of the small computation amount of DP and high accuracy of an exhaustive search but also utilizes a novel structure. It can detect a single infrared point target when the SCR is 1.5 with detection probability above 90% and a false alarm rate below 0.01%.

Track-Before-Detect Procedures in AM Radio-Based Passive Radar

In amplitude modulation radio-based passive radar, the track-before-detect (TBD) procedures are performed to process long time observation data. This work mainly focuses on the tracking of the time-Doppler and time-azimuth traces of multiple target under real scenarios such as low signal-to-noise ratio, hybrid clutter, severe breaking points, and intersecting traces. A new original approach to deal with the TBD problem of this work is developed. Two types of linear equations according to the simplified radio wave propagation model are formulated, which is the key point of the proposed method. We make the theoretical analysis about how the linear equations can be used to track multiple target, and how multiple target’s constant velocities and initial positions can be estimated, which is an additional parameter estimation capability of the proposed method. Both the simulated data and real experimental data are performed with the proposed method and some conventional TBD methods. Several comparisons of the results are given to verify the effectiveness of the proposed method.

Increasing the Radar Equivalent Energy Potential by “Track Before Detect” Method

The problem of radar detection of small-sized targets using the traditional methods of selection of signals embedded in background noise is considered. It is shown that for a false alarm rate of 10–5, which provides for 1–2 false alarms within the entire coverage of the modern 3D radar, the probability of detection of a small-sized target is getting unacceptably low. Repeatedly decreasing the threshold can provide an acceptable level of the detection probability at ultra-low signal-tonoise ratio (SNR) values. At the same time, decreasing the threshold will result in an unacceptable increase of the false alarm rate. A new target detection procedure using the “track before detect” method (TBD) is proposed. In the TBD procedure, the target is considered detected when two conditions are met: the signal exceeds once a definite threshold; the target is detected within a strictly defined observation area (acquisition or tracking gate). For low SNR values in the range of 3–8 dB and equal false alarm rate, the detection probability increases by 20–50 % compared to the traditional detection method. The simulation results showed a strong dependence of efficacy of the TBD algorithm on the threshold value and the decision rule. The possibility is noted of adaptive control over the threshold due to the use the detection results in the preceding scanning cycles, as well as the introduction of matrix radar surveillance not only by the target coordinates and parameters, but also by the detection threshold, decision rules, etc. Examination of these issues is the subject of further research.

Pseudo-Spectrum Based Track-Before-Detect for Weak Maneuvering Targets in Range-Doppler Plane

Track-before-detect (TBD) of weak maneuvering targets in range-Doppler plane is investigated. The existing TBD methods suffer from performance degradation due to the use of approximate motion models in range-Doppler plane. In this paper, exact time evolving rules of target range and Doppler are presented for constant acceleration (CA) and coordinated turn (CT) motions in Cartesian coordinates, and two novel pseudo-spectrum based TBD approaches are proposed for effective detection of weak CA and CT targets in range-Doppler plane, respectively. In accordance with the common CA and CT motions in Cartesian coordinates, the accurate time evolution equations of range and Doppler are derived for CA and CT targets, respectively, which are nonlinear functions of range-Doppler and several invariable parameters. Then, the pseudo-spectrum based TBD of CA and CT targets in range-Doppler plane is performed by matching these invariants. Each range-Doppler cell is predicted according to the exact evolution equation. A pseudo-spectrum is constructed around the predicted position, and sample values of the pseudo-spectrum are added onto cells for energy integration. This eliminates performance loss caused by model mismatch and target output envelope can be well maintained. To deal with the unknown target parameters, two pseudo-spectrum based matched filter banks are proposed for CA and CT targets, respectively. Analysis and design of the filter banks are provided in detail. Simulations are conducted to evaluate the validity of the proposed methods in handling weak maneuvering targets.

Underwater moving target detection using track-before-detect method with low power and high refresh rate signal

Conventional active sonar systems transmit a high power and low refresh rate signal to keep high power echoes from detected targets and maintain an effective detection range. However, high power signal brings a sonar reception blind area and fewer update times result in a poor detection and tracking performance. This paper proposes using the track-before-detect (TBD) method with a low power and high refresh rate acoustic signal to detect underwater targets. Low power signals could reduce the strong direct blast during transmission period and avoid blind areas, and the high refresh rate ensures more time domain measurements. Moreover, the TBD method improves the detection and tracking performance of lower power echoes. The simulation and experimental results demonstrate that compared with the conventional detect-before-track (DBT) method, which uses a high power and low refresh rate signal, the proposed TBD method with a low power and high refresh rate signal had superior detection and tracking performance.

Pseudo-Spectrum Based Speed Square Filter for Track-Before-Detect in Range-Doppler Domain

Track-before-detect (TBD) in range-Doppler plane is of significance for Doppler radars. Existing methods suffer from approximate motion models in range-Doppler plane and inefficiency of energy integration. In this paper, an accurate evolution equation of target range and Doppler with respect to time is presented and a new pseudo-spectrum based TBD method is proposed to effectively integrate target energy and detect weak target in range-Doppler domain. According to common constant velocity motion in Cartesian coordinates, the evolution of range and Doppler is correctly described by a nonlinear function of target range-Doppler and time invariant speed square. Consequently, a novel TBD method, which matches target speed instead of velocity in each direction, is proposed. Each cell in range-Doppler plane is predicted to the last frame according to the time evolution equation. Then, a pseudo-spectrum is constructed around the predicted position according to a point spread function. Multiframe accumulation is achieved by adding the sampling values of the pseudo-spectrum over several cells to the last frame. To match the unknown target speed square, a bank of pseudo-spectrum based speed square filter (PS-SSF) is presented. The target output envelopes in both range-Doppler and speed square domains are derived theoretically and a feasible parameter estimation method is proposed. Numerical results demonstrate that the proposed PS-SSF method can not only achieve effective weak target detection in range-Doppler domain, but also produce precise parameter estimates of range and Doppler, as well as target speed.

Track‐before‐detect algorithm for airborne radar system

This study considers the detection of dim target via track-before-detect (TBD) in airborne radar system. Dim target detection by TBD is well-studied while aircraft ego-motion is not taken into account. Ego-motion of the aircraft (platform) causes traditional TBD method useless in airborne radar system by two reasons. On one hand, the movement of the aircraft causes measurement misaligned among different frames. On the other hand, state space and measurement space is non-one-to-one correspondence. Considering these two issues, the authors proposed a TBD algorithm for airborne radar based on plots (AR-P-TBD). First, they derived coordinate rotation and motion compensation formula, and convert each frame of measurement into a unified coordinate system to make these measurements aligned. Then, TBD processing in measurement space is implemented. It avoids the non-one-to-one correspondence between state space and measurement space. Simulation results demonstrate the effectiveness of AR-P-TBD.

Target Detection and Tracking With Errors in Intensity and Mapping Dimensions

This paper addresses an engineering problem that measurement errors in intensity and mapping dimensions exist in the track-before-detect (TBD) architecture simultaneously. Drawing lessons from the probability data association (PDA) and the basic generalized likelihood ratio test TBD (GLRT-TBD), the joint log-likelihood ratio test TBD (JLLRT-TBD) method is proposed. Unlike conventional TBD algorithms, which consider intensities captured in the cells occupied by physically admissible transitions only, the novel method regards the cells centered at the transitions as effective ones. After that, detection probabilities are deduced from their positions, and then weighting likelihood ratios of their intensities results in metrics of different transitions. Finally, the maximum metric is compared with the designed threshold, and the estimation of the target state is obtained by retracing the maximization process. With this, the incoherent integration gain along consecutive scans is acquired, and the increment in computational complexity is avoided. Monte Carlo numerical results demonstrate that the detection and estimation performances of the JLLRT-TBD are superior significantly to those of the basic GLRT-TBD while the lower averaged execution time is spent.

A SURVEY OF MOTION-BASED MULTITARGET TRACKING METHODS

Multitarget tracking (MTT) in surveillance system is extremely challenging, due to uncertain data association, maneuverable target motion, dense clutter disturbance, and real-time processing requirements. A good many methods have been proposed to cope with these challenges. However, no up-to-date survey is available in the literature that can help to select suitable tracking algorithm for practical problem. This paper provides a comprehensive review of the state-of-the-art motion-based MTT techniques, classifies existing methods into two groups, i.e., the detect-before- track (DBT) scheme and the track-before-detect (TBD) scheme. The DBT scheme is employed to achieve robust and tractable tracking performance when the signal-noise-ratio (SNR) is strong. The TBD scheme is used in the scenarios of low SNR, and it aims to cumulate target energy by multiple sensor frames. Furthermore, depending on the data association mechanism, the DBT methods can be classified into two categories, data association based approaches and finite set statistics (FISST) based approaches. And the TBD methods can be classified into non-Bayesian approaches and Bayesian approaches depending on the basis theory used for tracking. For each category, this paper provides the detailed descriptions of the representative algorithms, and examines their pros and cons. Finally, new trends for future research directions are offered.

SMC-PHD based multi-target track-before-detect with nonstandard point observations model

Detection and tracking of multi-target with unknown and varying number is a challenging issue, especially under the condition of low signal-to-noise ratio (SNR). A modified multi-target track-before-detect (TBD) method was proposed to tackle this issue using a nonstandard point observation model. The method was developed from sequential Monte Carlo (SMC)-based probability hypothesis density (PHD) filter, and it was implemented by modifying the original calculation in update weights of the particles and by adopting an adaptive particle sampling strategy. To efficiently execute the SMC-PHD based TBD method, a fast implementation approach was also presented by partitioning the particles into multiple subsets according to their position coordinates in 2D resolution cells of the sensor. Simulation results show the effectiveness of the proposed method for time-varying multi-target tracking using raw observation data.

Multiple model particle filter track-before-detect for range ambiguous radar

The middle pulse repetition frequency (MPRF) and high pulse repetition frequency (HPRF) modes are widely adopted in airborne pulse Doppler (PD) radar systems, which results in the problem that the range measurement of targets is ambiguous. The existing data processing based range ambiguity resolving methods work well on the condition that the signal-to-noise ratio (SNR) is high enough. In this paper, a multiple model particle filter (MMPF) based track-before-detect (TBD) method is proposed to address the problem of target detection and tracking with range ambiguous radar in low-SNR environment. By introducing a discrete variable that denotes whether a target is present or not and the discrete pulse interval number (PIN) as components of the target state vector, and modeling the incremental variable of the PIN as a three-state Markov chain, the proposed algorithm converts the problem of range ambiguity resolving into a hybrid state filtering problem. At last, the hybrid filtering problem is implemented by a MMPF-based TBD method in the Bayesian framework. Simulation results demonstrate that the proposed Bayesian approach can estimate target state as well as the PIN simultaneously, and succeeds in detecting and tracking weak targets with the range ambiguous radar. Simulation results also show that the performance of the proposed method is superior to that of the multiple hypothesis (MH) method in low-SNR environment.